阅读说明：本技术 基于频谱认知的无人机信道优化分配方法和系统 (Unmanned aerial vehicle channel optimal allocation method and system based on frequency spectrum cognition ) 是由 翟学锋 潘志新 王永强 王红星 黄郑 高超 韩卫 于 2020-06-08 设计创作，主要内容包括：本发明公开了一种基于频谱认知的无人机信道优化分配方法和系统,将正在飞行的无人机自身作为频谱感知监测节点,对无人机使用的频段进行频谱扫描和监测,得到频域功率谱数据,将其量化得到信道状态信息,采用数传电台将频谱感知数据和遥测信息回传至地面控制与数据处理中心,地面控制与数据处理中心将全部节点的回传数据进行汇总,计算时间和信道间的相关度,结合信道优化分配模型,根据频谱感知数据与时间和信道间的相关度对空闲频谱和信道进行预测,为无人机分配空闲概率最大的信道,并将信道分配信息回传至无人机,无人机自动切换至优化分配的信道上,保持连续通信。本发明能够极大减少图传信道受到干扰的概率,提高图传信道的可靠性。(The invention discloses an unmanned aerial vehicle channel optimal allocation method and system based on spectrum cognition, which comprises the steps of taking a flying unmanned aerial vehicle as a spectrum sensing monitoring node, carrying out spectrum scanning and monitoring on a frequency band used by the unmanned aerial vehicle to obtain frequency domain power spectrum data, quantizing the frequency domain power spectrum data to obtain channel state information, transmitting the spectrum sensing data and telemetering information back to a ground control and data processing center by adopting a data transmission radio station, summarizing the returned data of all the nodes by the ground control and data processing center, calculating the correlation degree between time and channels, predicting idle spectrum and channels according to the correlation degree between the spectrum sensing data and the time and the channels by combining a channel optimal allocation model, allocating channels with the maximum idle probability for the unmanned aerial vehicle, transmitting the channel allocation information back to the unmanned aerial vehicle, and automatically switching the unmanned aerial vehicle to the optimally allocated channels, continuous communication is maintained. The invention can greatly reduce the probability of the interference on the image transmission channel and improve the reliability of the image transmission channel.)

1. An unmanned aerial vehicle channel optimal allocation method based on spectrum cognition is characterized by comprising the following steps:

s1: each unmanned aerial vehicle in the flight state is used as a spectrum sensing monitoring node, and spectrum scanning and monitoring sensing are carried out on each working frequency band used by each unmanned aerial vehicle in the flight state to obtain corresponding frequency domain power spectrum data;

s2: each frequency spectrum sensing monitoring node quantizes respective frequency domain power spectrum data to obtain channel state information of each unmanned aerial vehicle in a flight state;

s3: each frequency spectrum sensing monitoring node transmits respective frequency domain power spectrum data back to the ground control and data processing center by adopting a data transmission radio station;

s4: the ground control and data processing center collects frequency domain power spectrum data returned by all the spectrum sensing monitoring nodes, and calculates the correlation between time and channels;

s5: importing the frequency domain power spectrum data acquired in real time and the correlation information between the time and the channels obtained through calculation into a pre-trained channel optimization allocation model, predicting idle frequency spectrums or channels, selecting a certain number of channels with the maximum idle probability according to the prediction result, allocating corresponding optimal channels for all unmanned aerial vehicles in the flight state according to a preset allocation principle, and transmitting channel allocation information back to all unmanned aerial vehicles in the flight state through a data transmission radio station;

s6: and each unmanned aerial vehicle in the flight state receives the channel allocation information and automatically switches the respective transmission channel to the optimally allocated channel.

2. The unmanned aerial vehicle channel optimal allocation method based on spectrum cognition according to claim 1, wherein each unmanned aerial vehicle in a flight state is simultaneously provided with a data transmission radio station and a Wifi module, the unmanned aerial vehicle adopts the data transmission radio station to transmit remote control and remote measurement information, and the Wifi module is adopted to transmit image/video information.

3. The unmanned aerial vehicle channel optimal allocation method based on spectrum cognition according to claim 2, wherein the frequency band occupied by the transmission of the remote control telemetry information comprises a special frequency band for the unmanned aerial vehicle of 840.5-845MHz, and the frequency band occupied by the transmission of the image/video information comprises two frequency bands of 2.4GHz and 5 GHz.

4. The method for optimal allocation of unmanned aerial vehicle channels based on spectrum cognition according to claim 1, wherein in step S2, the process of obtaining the channel state information of each unmanned aerial vehicle in flight state includes the following steps:

setting the channel state information of each unmanned aerial vehicle in the flight state as CSI (t, c):

wherein, 0 represents that the channel is in an idle state, 1 represents that the channel is in an occupied state, R (T, C) is the power value of the channel C at the sampling time T, the unit dBm, T is the total duration, and C is the total number of the channels.

5. The method for unmanned aerial vehicle channel optimal allocation based on spectrum cognition according to claim 1, wherein in step S4, the process of calculating the correlation between time and channel comprises the following steps:

s41, setting X_k、Y_kThe method comprises the following steps that two channel state information sequences are provided, and each channel state information sequence is a 0-1 sequence;

s42, calculating to obtain X according to the following formula_k、Y_kCorrelation degree ρ:

wherein, i (a) is a judgment function, if a is true, i (a) is 1, if a is not true, i (a) is 0, Σ_kCalculated for the accumulated sum.

6. The method for the optimal allocation of the unmanned aerial vehicle channels based on spectrum cognition according to claim 1, wherein in step S5, the preset allocation principle includes matching the degree of task urgency of the unmanned aerial vehicle according to the magnitude of the idle probability of the channel, and the distance between the unmanned aerial vehicle and the control center, that is, the channel with the largest idle probability is allocated to the unmanned aerial vehicle with the most urgent or farthest task, and the channel with the second idle probability is allocated to the unmanned aerial vehicle with the second importance or second distance to the task.

7. The spectrum cognition based unmanned aerial vehicle channel optimization distribution method according to claim 1, wherein the channel optimization distribution model is constructed based on a back propagation neural network algorithm and comprises a first layer neural network and a second layer neural network which are connected with each other;

the first layer of neural network is used for performing parallel training on input vectors of a time domain and a frequency domain to obtain a time domain training result and a frequency domain training result, the second layer of neural network is used for integrating the time domain training result and the frequency domain training result of the first layer of neural network by combining the correlation of the time domain and the frequency domain of a channel, an idle frequency spectrum or the channel is predicted, and the prediction result is the idle probability of each channel.

8. The spectrum cognition-based unmanned aerial vehicle channel optimal allocation method according to claim 1, wherein the optimal allocation method further comprises:

the unmanned aerial vehicle collects telemetering information in real time/periodically, the telemetering information is added into a frequency spectrum sensing data frame and is transmitted back to a ground control and data processing center, and the telemetering information comprises: the geographical position, speed, acceleration, self-state information and acquisition timestamp of the unmanned aerial vehicle;

and the ground control and data processing center processes the remote information, calculates to obtain corresponding remote control information, and transmits the calculated remote control information and channel allocation information back to each unmanned aerial vehicle in a flight state through the data transmission radio station.

9. An unmanned aerial vehicle channel optimization distribution system based on frequency spectrum cognition is characterized by comprising a ground control and data processing center and at least one unmanned aerial vehicle in a flight state;

the unmanned aerial vehicle comprises an unmanned aerial vehicle body, and a first spectrum sensing module, a first data processing module, a first GPS module, a remote control and telemetry module, a first Wifi module and a first data transmission radio station which are carried on the unmanned aerial vehicle body;

the first data processing module is respectively connected with the first spectrum sensing module, the first GPS module, the first remote control and remote measurement module, the first Wifi module and the first data transmission radio station module;

the first frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data; the first data processing module is used for processing the received frequency domain power spectrum data, obtaining channel state information of the unmanned aerial vehicle after quantization, and sending the quantization result back to the ground control and data processing center through the first data transmission radio station module;

the first GPS module is used for collecting the position and speed information of the unmanned aerial vehicle in real time/periodically, the remote control and telemetry module is used for collecting the state information of the unmanned aerial vehicle in real time/periodically, and the first data processing module integrates the collection results of the first GPS module and the first remote control and telemetry module into telemetry information which is added into a frequency spectrum sensing data frame and is transmitted back to the ground control and data processing center;

the ground control and data processing center comprises a second data processing module, and a second spectrum sensing module, a second GPS module, a second Wifi module, a second data transmission radio station and a channel optimization distribution module which are connected with the second data processing module;

a data transmission channel is established between the second data transmission radio station and the first data transmission radio station of each unmanned aerial vehicle, the frequency spectrum sensing data frames sent by all the first data transmission radio stations are received, and the received frequency spectrum sensing data frames are sent to the second data processing module;

the second frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the ground unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data;

the second data processing module receives frequency domain power spectrum data of the ground unmanned aerial vehicles fed back by the frequency spectrum sensing data frames and the second frequency spectrum sensing module sent by all the unmanned aerial vehicles, analyzes the frequency domain power spectrum data to obtain channel state information and telemetering information of each unmanned aerial vehicle, continuously processes the telemetering information to obtain corresponding remote control information, simultaneously sends the channel state information of each unmanned aerial vehicle to the channel optimization distribution module, the channel optimization distribution module adopts the unmanned aerial vehicle channel optimization distribution calculation based on frequency spectrum cognition in any one of claims 1-8 to obtain an optimal channel corresponding to each unmanned aerial vehicle, and feeds back the calculation result to the second data processing module;

the second data processing module transmits the channel distribution information and the remote control information back to each unmanned aerial vehicle through the second data transmission radio station, so that the unmanned aerial vehicle automatically switches channels of the first Wifi module and the first data transmission radio station according to the received channel distribution information, and drives the remote control and telemetry module to execute flight control on the unmanned aerial vehicle according to the received remote control information;

a data transmission channel is established between the second Wifi module and the first Wifi module of each unmanned aerial vehicle, and image/video information sent by all the unmanned aerial vehicles through the first Wifi modules is received;

and the second GPS module is used for acquiring the position and speed information of the ground control and data processing center in real time/periodically and transmitting the acquisition result and the channel allocation information to the unmanned aerial vehicle.

10. The spectrum cognition based unmanned aerial vehicle channel optimization distribution system according to claim 9, wherein the channel optimization distribution module comprises a spectrum analysis unit, a spectrum prediction and channel distribution unit;

the frequency spectrum analysis unit is used for processing frequency domain power spectrum data acquired in real time and calculating correlation degree information between time and channels;

the frequency spectrum prediction and channel allocation unit is used for predicting the idle frequency spectrum or the channel by combining frequency domain power spectrum data and correlation degree information between time and the channel, selecting a certain amount of channels with the maximum idle probability according to a prediction result, and allocating corresponding optimal channels to each unmanned aerial vehicle in a flight state according to a preset allocation principle.

Technical Field

The invention relates to the technical field of wireless communication, in particular to an unmanned aerial vehicle channel optimal allocation method based on frequency spectrum cognition.

Background

In recent years, civil unmanned aerial vehicles have been developed rapidly, and remote control, telemetry and image transmission devices mainly use two wifi (wireless fidelity) frequency bands of 2.4GHz and 5GHz, which are also Industrial, Scientific and medical (ISM) frequency bands. The connection modes such as Bluetooth and ZigBee also use the 2.4GHz frequency band. Because the two frequency bands are free open frequency bands, the number of used devices is increased day by day, and the interference to users is more and more serious. The communication channels of the civil unmanned aerial vehicles are all fixedly distributed in advance, so that once the communication channels are interfered, the error rate is rapidly increased, even the communication is interrupted, the remote control and remote measurement function is not good, video mosaics are increased, or no images exist.

In order to solve the problem of channel interference, the invention of patent No. CN108513734A discloses a channel switching method, a device, and a communication device, which employs a channel switching method, wherein an interference power obtaining module obtains interference powers of a plurality of channels in a communication frequency band, and if the interference power of a current channel exceeds a preset threshold, a target channel selecting module selects a suitable target channel according to the interference powers of the plurality of channels, and switches the target channel to the target channel through the channel switching module to continue communication. The method simply judges whether the channel is available according to the interference power currently suffered by the channel, whether the interference at the future moment exists is not predicted, and when the current channel is seriously interfered and the communication between the ground station and the unmanned aerial vehicle is interrupted, the channel switching information can not be interacted, and the channel switching is difficult to succeed.

The invention patent No. CN1084966384A discloses a method and device for controlling communication mode, which determines whether two communication parties use TDD or FDD duplex mode for communication by observing the measurement parameters of the communication channel. The method is essentially to dynamically adjust communication parameters according to channel conditions, but the number of parameters to be measured is small, and the parameters to be adjusted are limited.

The invention patent CN106877947A discloses a radio frequency channel parallel detection device and method for an unmanned aerial vehicle, which divides a 2.4GHz communication frequency band commonly used by the unmanned aerial vehicle into 42 parallel channels, measures the signal intensity and background noise of each channel, and detects the image transmission signal and the remote control and remote measurement signal of the unmanned aerial vehicle by using the signal bandwidth characteristic. Although the method improves the accuracy of channel measurement, the complexity is greatly increased, and the method is mainly used for unmanned aerial vehicle signal detection.

To sum up, at present, a mechanism and a method for sensing, analyzing and optimizing the full-band frequency spectrum used by the unmanned aerial vehicle and rapidly allocating and switching the channel when the communication channel of the unmanned aerial vehicle is interfered are lacked, so that the reliability and effectiveness of the communication of the unmanned aerial vehicle are improved.

Disclosure of Invention

The invention aims to provide an unmanned aerial vehicle channel optimal allocation method based on frequency spectrum cognition, which is characterized in that idle frequency spectrums or channels are predicted by utilizing frequency spectrum sensing data and time channel correlation degree information and adopting a machine learning-based algorithm, and an appropriate channel with the maximum idle probability is selected according to a prediction result, so that the probability of interference on a picture transmission channel is greatly reduced, and the reliability of the picture transmission channel is improved; in addition, the high-reliability and anti-interference digital radio station is used for transmitting remote control information, frequency spectrum sensing information and channel control information to ensure the reliable transmission of control signals, and the high-capacity and high-speed WiFi module is used for transmitting image and video information and can be quickly switched to an idle channel when being interfered to ensure the continuous and uninterrupted image transmission.

In order to achieve the above object, with reference to fig. 1, the present invention provides an unmanned aerial vehicle channel optimal allocation method based on spectrum cognition, where the optimal allocation method includes:

s1: and taking each unmanned aerial vehicle in the flight state as a spectrum sensing monitoring node, and carrying out spectrum scanning and monitoring sensing on each working frequency band used by each unmanned aerial vehicle in the flight state to obtain corresponding frequency domain power spectrum data.

S2: and each frequency spectrum sensing monitoring node quantizes the respective frequency domain power spectrum data to obtain the channel state information of each unmanned aerial vehicle in the flight state.

S3: and each frequency spectrum sensing monitoring node transmits respective frequency domain power spectrum data back to the ground control and data processing center by adopting a data transmission radio station.

S4: and the ground control and data processing center collects frequency domain power spectrum data returned by all the spectrum sensing monitoring nodes and calculates the correlation between time and channels.

S5: the method comprises the steps of importing frequency domain power spectrum data acquired in real time and correlation information between time and channels obtained through calculation into a pre-trained channel optimization allocation model, predicting idle frequency spectrums or channels, selecting a certain number of channels with the maximum idle probability according to a prediction result, allocating corresponding optimal channels for all unmanned aerial vehicles in a flight state according to a preset allocation principle, and transmitting channel allocation information back to all unmanned aerial vehicles in the flight state through a data transmission radio station.

S6: and each unmanned aerial vehicle in the flight state receives the channel allocation information and automatically switches the respective transmission channel to the optimally allocated channel.

As a preferred example, each unmanned aerial vehicle in a flight state is simultaneously provided with a data transmission radio station and a Wifi module, and the unmanned aerial vehicle transmits remote control and remote measurement information by using the data transmission radio station and transmits image/video information by using the Wifi module.

As a preferable example, the frequency band occupied by the transmission of the remote control and telemetry information includes a frequency band dedicated to the unmanned aerial vehicle of 840.5-845MHz, and the frequency band occupied by the transmission of the image/video information includes two frequency bands of 2.4GHz and 5 GHz.

As a preferable example, in step S2, the process of obtaining the channel state information of each drone in flight state includes the following steps:

setting the channel state information of each unmanned aerial vehicle in the flight state as CSI (t, c):

wherein, 0 represents that the channel is in an idle state, 1 represents that the channel is in an occupied state, R (T, C) is the power value of the channel C at the sampling time T, the unit dBm, T is the total duration, and C is the total number of the channels.

As a preferred example, in step S4, the process of calculating the correlation between time and channel includes the following steps:

s41, setting X_k、Y_kTwo channel state information sequences are provided, and each channel state information sequence is a 0-1 sequence.

S42, calculating to obtain X according to the following formula_k、Y_kCorrelation degree ρ:

wherein, i (a) is a judgment function, if a is true, i (a) is 1, if a is not true, i (a) is 0, Σ_kCalculated for the accumulated sum.

As a preferred example, the preset allocation principle includes matching the channel idle probability with the mission emergency degree of the drone and the distance between the drone and the control center, that is, the channel with the highest idle probability is allocated to the drone with the most emergency or farthest mission, and the channel with the second idle probability is allocated to the drone with the second most important mission or farthest mission.

As a preferred example, the channel optimization allocation model is constructed based on a back propagation neural network algorithm, and includes a first layer neural network and a second layer neural network connected to each other.

The first layer of neural network is used for performing parallel training on input vectors of a time domain and a frequency domain to obtain a time domain training result and a frequency domain training result, the second layer of neural network is used for integrating the time domain training result and the frequency domain training result of the first layer of neural network by combining the correlation of the time domain and the frequency domain of a channel, an idle frequency spectrum or the channel is predicted, and the prediction result is the idle probability of each channel.

As a preferred example, the optimal allocation method further includes:

the unmanned aerial vehicle collects telemetering information in real time/periodically, the telemetering information is added into a frequency spectrum sensing data frame and is transmitted back to a ground control and data processing center, and the telemetering information comprises: the geographical position, speed, acceleration, self state information and the collection timestamp of unmanned aerial vehicle.

And the ground control and data processing center processes the remote information, calculates to obtain corresponding remote control information, and transmits the calculated remote control information and channel allocation information back to each unmanned aerial vehicle in a flight state through the data transmission radio station.

With reference to fig. 2, the present invention further provides an unmanned aerial vehicle channel optimal allocation system based on spectrum cognition, where the unmanned aerial vehicle channel optimal allocation system includes a ground control and data processing center and at least one unmanned aerial vehicle in a flight state.

Unmanned aerial vehicle includes the unmanned aerial vehicle body to and carry on first spectrum sensing module, first data processing module, first GPS module, remote control telemetering measurement module, first Wifi module and the first digital radio station on the unmanned aerial vehicle body.

The first data processing module is connected with the first spectrum sensing module, the first GPS module, the first remote control and telemetry module, the first Wifi module and the first data transmission radio module respectively.

The first frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data; the first data processing module is used for processing the received frequency domain power spectrum data, obtaining channel state information of the unmanned aerial vehicle after quantization, and sending the quantization result back to the ground control and data processing center through the first data transmission radio module.

The first GPS module is used for collecting position and speed information of the unmanned aerial vehicle in real time/periodically, the remote control and telemetry module is used for collecting state information of the unmanned aerial vehicle in real time/periodically, and the first data processing module integrates the collection results of the first GPS module and the first remote control and telemetry module into telemetry information, adds the telemetry information into a frequency spectrum sensing data frame and transmits the telemetry information back to the ground control and data processing center.

The ground control and data processing center comprises a second data processing module, and a second spectrum sensing module, a second GPS module, a second Wifi module, a second data transmission station and a channel optimization distribution module which are connected with the second data processing module.

A data transmission channel is established between the second data transmission radio station and the first data transmission radio station of each unmanned aerial vehicle, spectrum sensing data frames sent by all the first data transmission radio stations are received, and the received spectrum sensing data frames are sent to the second data processing module.

The second frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the ground unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data.

The second data processing module receives frequency domain power spectrum data of the ground unmanned aerial vehicle fed back by frequency spectrum sensing data frames sent by all unmanned aerial vehicles and the second frequency spectrum sensing module, analyzes the frequency domain power spectrum data to obtain channel state information and telemetering information of each unmanned aerial vehicle, continuously processes the telemetering information to obtain corresponding remote control information, and simultaneously sends the channel state information of each unmanned aerial vehicle to the channel optimization distribution module, the channel optimization distribution module adopts the unmanned aerial vehicle channel optimization distribution calculation based on frequency spectrum cognition to obtain an optimal channel corresponding to each unmanned aerial vehicle, and the calculation result is fed back to the second data processing module.

The second data processing module transmits the channel distribution information and the remote control information back to each unmanned aerial vehicle through the second data transmission radio station, so that the unmanned aerial vehicle automatically switches channels of the first Wifi module and the first data transmission radio station according to the received channel distribution information, and drives the remote control and remote measurement module to execute flight control on the unmanned aerial vehicle according to the received remote control information.

A data transmission channel is established between the second Wifi module and the first Wifi module of each unmanned aerial vehicle, and image/video information sent by all the unmanned aerial vehicles through the first Wifi modules is received.

And the second GPS module is used for acquiring the position and speed information of the ground control and data processing center in real time/periodically and transmitting the acquisition result and the channel allocation information to the unmanned aerial vehicle.

As a preferred example, the channel optimization allocation module includes a spectrum analysis unit, a spectrum prediction and channel allocation unit.

The frequency spectrum analysis unit is used for processing frequency domain power spectrum data acquired in real time and calculating correlation degree information between time and channels.

The frequency spectrum prediction and channel allocation unit is used for predicting the idle frequency spectrum or the channel by combining frequency domain power spectrum data and correlation degree information between time and the channel, selecting a certain amount of channels with the maximum idle probability according to a prediction result, and allocating corresponding optimal channels to each unmanned aerial vehicle in a flight state according to a preset allocation principle.

In summary, the invention uses the unmanned aerial vehicle as a spectrum sensing monitoring node, performs spectrum scanning and monitoring on a frequency band used by the unmanned aerial vehicle to obtain frequency domain power spectrum data, quantizes the frequency domain power spectrum data to obtain channel state information CSI, and then uses a data transmission radio station to transmit the spectrum sensing data and telemetry information back to the ground, the ground collects the returned data of all nodes, calculates the correlation between time and channels, predicts the idle spectrum and channels according to the correlation between the spectrum sensing data and the time and the channels by combining with a channel optimization distribution model, distributes channels with the maximum idle probability to the unmanned aerial vehicle, and transmits channel distribution information back to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically switches to the optimally distributed channels to maintain continuous communication. In addition, the data transmission radio station is used for transmitting remote control and remote measurement information, the Wifi module transmits image/video information, for example, the remote control and remote measurement information uses 840.5-845MHz frequency band, the image/video information transmission dynamically selects 2.4GHz and 5GHz frequency bands, and reliable transmission of control signals and continuous and uninterrupted image transmission are ensured.

Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:

(1) the invention uses the high-reliability data transmission station with anti-interference capability for remote control and remote measurement information, frequency spectrum sensing information and channel control information transmission, thereby ensuring the reliable transmission of control signals.

(2) The invention uses the WiFi module with large capacity and high speed rate for image and video information transmission, and can quickly switch to an idle channel when being interfered, thereby ensuring continuous and uninterrupted image transmission.

(3) The method and the device utilize the frequency spectrum sensing data and the time channel correlation degree information, adopt an algorithm based on machine learning to predict the idle frequency spectrum or the channel, and select the proper channel with the maximum idle probability according to the prediction result, thereby greatly reducing the probability of the interference on the image transmission channel and improving the reliability of the image transmission channel.

It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.

The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.

Drawings

The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:

fig. 1 is a flowchart of an unmanned aerial vehicle channel optimal allocation method based on spectrum cognition in the invention.

Fig. 2 is a schematic diagram of an unmanned aerial vehicle channel optimization allocation system based on spectrum cognition.

Fig. 3 is a schematic diagram of the unmanned aerial vehicle channel optimization distribution method based on spectrum cognition.

Detailed Description

In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.

In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.

In practical application, the information quantity required to be transmitted by remote control and telemetry is small, and the reliability requirement is high, so that a frequency hopping data transmission radio station with high reliability and strong anti-interference capability can be selected to transmit the remote control and telemetry information. The image/video transmission information quantity is large, the information association degree is high, and a certain error rate can be tolerated, so that a Wifi module can be selected for image/video information transmission.

In order to further improve the reliability of information transmission of the unmanned aerial vehicle, a special frequency band of the unmanned aerial vehicle of 840.5-845MHz is selected as a remote control and telemetry information transmission channel, the frequency band has less interference and high reliability, two frequency bands of 2.4GHz and 5GHz are dynamically selected as image/video information transmission channels, and automatic switching can be performed according to the interference degree.

Therefore, in the invention, the unmanned aerial vehicle is simultaneously provided with the data transmission radio station and the Wifi module, the data transmission radio station is adopted to transmit remote control and remote measurement information, the Wifi module transmits image/video information, and the frequency band occupied by the remote control and remote measurement information and the image/video information is different. In fact, the channel optimal allocation method mentioned in the present invention needs to perform optimal allocation on the channels occupied by two transmission modules. Considering that the frequency bands occupied by the Wifi module are only two, the computing resources are more prone to frequency band optimization of the data transmission radio station.

With reference to fig. 1 and fig. 3, the present invention provides a spectrum cognition-based channel optimization allocation method for an unmanned aerial vehicle, including:

s1: and taking each unmanned aerial vehicle in the flight state as a spectrum sensing monitoring node, and carrying out spectrum scanning and monitoring sensing on each working frequency band used by each unmanned aerial vehicle in the flight state to obtain corresponding frequency domain power spectrum data.

All unmanned aerial vehicles in the flight working state are used as spectrum sensing monitoring nodes, frequency bands used by all unmanned aerial vehicles, such as frequency bands of 840M, 2.4G and 5G, are scanned comprehensively and rapidly, the service conditions of the frequency bands are monitored in a sensing mode, and frequency domain power spectrum data are obtained.

S2: and each frequency spectrum sensing monitoring node quantizes the respective frequency domain power spectrum data to obtain the channel state information of each unmanned aerial vehicle in the flight state.

Setting the channel state information of each unmanned aerial vehicle in the flight state as CSI (t, c):

wherein, 0 represents that the channel is in an idle state, 1 represents that the channel is in an occupied state, R (T, C) is the power value of the channel C at the sampling time T, the unit dBm, T is the total duration, and C is the total number of the channels.

CSI (t, c) is information about whether channel c is occupied at the time of t sampling, specifically: (1) when the value of CSI (t, c) is 0, the channel is not occupied and is in an idle state; (2) when the value of CSI (t, c) is 1, it indicates that the channel is occupied and in a busy state.

S3: and each frequency spectrum sensing monitoring node transmits respective frequency domain power spectrum data back to the ground control and data processing center by adopting a data transmission radio station.

And each unmanned aerial vehicle frequency spectrum sensing monitoring node transmits the acquired telemetering information and the processed frequency spectrum sensing data back to the ground control and data processing center through the data transmission radio station for data processing.

Preferably, each unmanned aerial vehicle frequency spectrum perception monitoring node still will acquire through first GPS module and remote control telemeasuring module unmanned aerial vehicle's positional information and state information, for example geographical position, speed, acceleration and time stamp etc. add to frequency spectrum perception data frame as telemeasuring information, pass back ground control and data processing center in the lump through the data transfer radio station and handle the analysis, improve the whole transmission efficiency of data.

S4: and the ground control and data processing center collects frequency domain power spectrum data returned by all the spectrum sensing monitoring nodes and calculates the correlation between time and channels.

Let X_k、Y_kFor two channel state information sequences, each of which is a 0-1 sequence, then X_k、Y_kThe correlation ρ of (d) is:

wherein, i (a) is a judgment function, if a is true, i (a) is 1, if a is not true, i (a) is 0, Σ_kCalculated for the accumulated sum.

And a spectrum analysis module located in the ground control and data processing center collects and analyzes spectrum sensing data returned by all the spectrum sensing monitoring nodes of the unmanned aerial vehicle, calculates correlation numerical values rho of channel state information between each time interval and each channel, and provides a data analysis basis for subsequent channel prediction and optimized distribution.

When there is the ground unmanned aerial vehicle who does not take off who occupies the channel on ground, can gather ground unmanned aerial vehicle's spectrum perception data by ground control and data processing center, the spectrum perception data that the unmanned aerial vehicle that reintegrates the flight that receives sent, as complete unmanned aerial vehicle's channel relevant information.

S5: the method comprises the steps of importing frequency domain power spectrum data acquired in real time and correlation information between time and channels obtained through calculation into a pre-trained channel optimization allocation model, predicting idle frequency spectrums or channels, selecting a certain number of channels with the maximum idle probability according to a prediction result, allocating corresponding optimal channels for all unmanned aerial vehicles in a flight state according to a preset allocation principle, and transmitting channel allocation information back to all unmanned aerial vehicles in the flight state through a data transmission radio station.

The preset allocation principle comprises the steps of matching the idle probability of the channel with the task urgency of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the control center, namely allocating the channel with the maximum idle probability to the unmanned aerial vehicle with the most urgent or farthest task, allocating the channel with the second idle probability to the unmanned aerial vehicle with the second important or second distant task, and so on until the channel allocation is finished.

The channel optimization distribution model is constructed based on a machine learning algorithm, and can be trained by using received historical spectrum sensing data, and the adopted machine learning algorithm comprises a reverse transmission neural network algorithm.

And substituting the frequency spectrum sensing data acquired in real time and the correlation information between the time and the channel obtained by analysis into the trained channel optimization distribution model, performing prediction analysis on the idle frequency spectrum and the channel, distributing the information with the maximum idle probability to all the unmanned aerial vehicles according to the prediction result, and sending the obtained channel distribution information to each unmanned aerial vehicle through a data transmission radio station.

For example, the channel optimization allocation model is constructed based on a back propagation neural network algorithm, and comprises a first layer neural network and a second layer neural network which are connected with each other.

The first layer of neural network is used for performing parallel training on input vectors of a time domain and a frequency domain to obtain a time domain training result and a frequency domain training result, the second layer of neural network is used for integrating the time domain training result and the frequency domain training result of the first layer of neural network by combining the correlation of the time domain and the frequency domain of a channel, an idle frequency spectrum or the channel is predicted, and the prediction result is the idle probability of each channel.

S6: and each unmanned aerial vehicle in the flight state receives the channel allocation information and automatically switches the respective transmission channel to the optimally allocated channel.

The unmanned aerial vehicle receives the channel allocation information and the remote control information (as feedback to the telemetering information) returned by the ground control and data processing center, and automatically switches to the idle channel for optimized allocation, so that the interference channel is avoided, and the continuous and uninterrupted transmission of the image transmission information is ensured.

With reference to fig. 2, the invention provides an unmanned aerial vehicle channel optimization allocation system based on spectrum cognition, which includes an unmanned aerial vehicle and a ground control and data processing center.

Unmanned aerial vehicle includes the unmanned aerial vehicle body to and carry on first spectrum sensing module, first data processing module, first GPS module, remote control telemetering measurement module, first Wifi module and the first digital radio station on the unmanned aerial vehicle body.

The first data processing module is connected with the first spectrum sensing module, the first GPS module, the first remote control and telemetry module, the first Wifi module and the first data transmission radio module respectively.

The first frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data; the first data processing module is used for processing the received frequency domain power spectrum data, obtaining channel state information of the unmanned aerial vehicle after quantization, and sending the quantization result back to the ground control and data processing center through the first data transmission radio module.

The first GPS module is used for collecting position and speed information of the unmanned aerial vehicle in real time/periodically, the remote control and telemetry module is used for collecting state information of the unmanned aerial vehicle in real time/periodically, and the first data processing module integrates the collection results of the first GPS module and the first remote control and telemetry module into telemetry information, adds the telemetry information into a frequency spectrum sensing data frame and transmits the telemetry information back to the ground control and data processing center.

The ground control and data processing center comprises a second data processing module, and a second spectrum sensing module, a second GPS module, a second Wifi module, a second data transmission station and a channel optimization distribution module which are connected with the second data processing module.

A data transmission channel is established between the second data transmission radio station and the first data transmission radio station of each unmanned aerial vehicle, spectrum sensing data frames sent by all the first data transmission radio stations are received, and the received spectrum sensing data frames are sent to the second data processing module.

The second frequency spectrum sensing module is used for monitoring and sensing the frequency spectrum of the current used frequency band of the ground unmanned aerial vehicle to obtain corresponding frequency domain power spectrum data.

The second data processing module receives frequency domain power spectrum data of the ground unmanned aerial vehicle fed back by frequency spectrum sensing data frames sent by all unmanned aerial vehicles and the second frequency spectrum sensing module, analyzes the frequency domain power spectrum data to obtain channel state information and telemetering information of each unmanned aerial vehicle, continuously processes the telemetering information to obtain corresponding remote control information, and simultaneously sends the channel state information of each unmanned aerial vehicle to the channel optimization distribution module, the channel optimization distribution module adopts the unmanned aerial vehicle channel optimization distribution calculation based on frequency spectrum cognition to obtain an optimal channel corresponding to each unmanned aerial vehicle, and the calculation result is fed back to the second data processing module.

The second data processing module transmits the channel distribution information and the remote control information back to each unmanned aerial vehicle through the second data transmission radio station, so that the unmanned aerial vehicle automatically switches channels of the first Wifi module and the first data transmission radio station according to the received channel distribution information, and drives the remote control and remote measurement module to execute flight control on the unmanned aerial vehicle according to the received remote control information.

A data transmission channel is established between the second Wifi module and the first Wifi module of each unmanned aerial vehicle, and image/video information sent by all the unmanned aerial vehicles through the first Wifi modules is received.

And the second GPS module is used for acquiring the position and speed information of the ground control and data processing center in real time/periodically and transmitting the acquisition result and the channel allocation information to the unmanned aerial vehicle.

Preferably, the channel optimization allocation module includes a spectrum analysis unit and a spectrum prediction and channel allocation unit.

The frequency spectrum analysis unit is used for processing frequency domain power spectrum data acquired in real time and calculating correlation degree information between time and channels. The frequency spectrum prediction and channel allocation unit is used for predicting the idle frequency spectrum or the channel by combining frequency domain power spectrum data and correlation degree information between time and the channel, selecting a certain amount of channels with the maximum idle probability according to a prediction result, and allocating corresponding optimal channels to each unmanned aerial vehicle in a flight state according to a preset allocation principle.

Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.