阅读说明：本技术 一种基于毫米波/太赫兹波辐射的目标自动检测算法 (Target automatic detection algorithm based on millimeter wave/terahertz wave radiation ) 是由 梁晓林 邓建钦 王明超 辛海鸣 于 2019-11-22 设计创作，主要内容包括：本发明公开了一种基于毫米波/太赫兹波辐射的目标自动检测算法,包括如下过程：利用毫米波/太赫兹波辐射计阵列进行目标辐射信号的数据采集；对目标所处环境进行背景估计,并进行背景抑制；对经过背景抑制的数据进行信号增强,得到信号矩阵；对增强后的信号矩阵的每一列信号进行翘度与标准差特性分析,以此为基础构建目标辐射信号的混合特征谱,即基于标准差的翘度谱,并对其进行时频分析,得到目标辐射信号的混合时频特征谱；利用五层卷积神经网络对混合时频特征谱进行优化和提取,并利用Softmax算法实现目标自动检测,本发明所公开的算法可显著改善信号信噪比,实现目标的精确识别。(The invention discloses an automatic target detection algorithm based on millimeter wave/terahertz wave radiation, which comprises the following processes: acquiring data of a target radiation signal by using a millimeter wave/terahertz wave radiometer array; carrying out background estimation on the environment where the target is located, and carrying out background suppression; carrying out signal enhancement on the data subjected to background suppression to obtain a signal matrix; performing characteristic analysis of the warping degree and the standard deviation on each column of signals of the enhanced signal matrix, constructing a mixed characteristic spectrum of the target radiation signal on the basis of the characteristic analysis, namely the warping degree spectrum based on the standard deviation, and performing time-frequency analysis on the warped degree spectrum to obtain a mixed time-frequency characteristic spectrum of the target radiation signal; the five-layer convolutional neural network is utilized to optimize and extract the mixed time-frequency characteristic spectrum, and the Softmax algorithm is utilized to realize the automatic detection of the target.)

1. An automatic target detection algorithm based on millimeter wave/terahertz wave radiation is characterized by comprising the following processes:

(1) acquiring data of a target radiation signal by using a millimeter wave/terahertz wave radiometer array;

(2) carrying out background estimation on the environment where the target is located by utilizing a background filtering algorithm, and carrying out background suppression on the acquired data;

(3) performing signal enhancement on the data subjected to background suppression by using a local optimization algorithm to obtain a signal matrix;

(4) performing characteristic analysis of the warping degree and the standard deviation on each column of signals of the signal matrix after signal enhancement, constructing a mixed characteristic spectrum of the target radiation signal on the basis of the characteristic analysis, namely the warping degree spectrum based on the standard deviation, and performing time-frequency analysis on the warping degree spectrum to obtain a mixed time-frequency characteristic spectrum of the target radiation signal;

(5) and optimizing and extracting the mixed time-frequency characteristic spectrum by utilizing a five-layer convolutional neural network, and detecting the target by utilizing a Softmax algorithm.

2. The algorithm for automatically detecting the target based on the millimeter wave/terahertz wave radiation as claimed in claim 1, wherein in the step (1), the radiometer array consisting of the radiometers is used for data acquisition of the target radiation signal, the radiometer array has 9 radiation modules, and a single radiation module has 4 radiation channels and 36 radiation channels.

3. The automatic target detection algorithm based on millimeter wave/terahertz wave radiation as claimed in claim 1, wherein in step (1), an oversampling method is adopted to collect data, and down-sampling processing is performed on the collected digital signals, that is, starting from the first digitized signal obtained in the sampling process, each P values are a group, and the median of the P values is taken as an effective signal to be stored and recorded as a matrix a_M×NWherein M represents the number of rows and N represents the number of columns;

and in the sampling process, the sampling rate and the pulse period of the radiation array controller meet the following requirements:

f_s＞＞γ_num×f_m(1)

wherein f is_sRepresenting the sampling rate, gamma_numNumber of median points, f_mRepresenting the pulse period of the radiating array controller.

4. The algorithm for automatically detecting the target based on the millimeter wave/terahertz wave radiation as claimed in claim 3, wherein in the step (2), the background estimation value is expressed as follows:

wherein m represents the matrix A_M×NM-th row in the drawing, n represents the matrix A_M×NThe nth column;

in order to suppress the interference caused by background to the target radiation signal, the matrix A is used_M×NSubtracting the background estimation value obtained in the formula (2) from each value to obtain a background-suppressed value:

in order to further suppress static interference in the signal, the following processing is performed:

U[m,n]＝δA[m,n-1]+(1-δ)A[m,n](4)

wherein, δ represents a weighting factor, the value range is 0-1, and δ is 0.7 in the invention.

5. The automatic target detection algorithm based on millimeter wave/terahertz wave radiation as claimed in claim 4, wherein the specific method of step (3) is as follows:

enhancing signals of each row of the matrix by using a local optimization algorithm, wherein the enhanced signals are expressed as:

wherein, U [ tau ]_max(0),n]Represents U [ i, n ]]I 1, …, M first local optimum, τ_max(0) A matrix index value representing a local optimum value;

if τ_max(0) If < M, then:

wherein, U [ tau ]_max(1),n]Represents U [ i, n ]]I is 1, …, M local optima, i is τ_max(0)+1,…,M；

The above-mentioned cycle is up to tau_max(k) Until M;

similarly, each column of signals of the matrix is enhanced by using a local optimization algorithm, and the enhanced signals can be represented as:

wherein, Bm, upsilon_max(0)]Represents B [ m, j ]]J 1.. cndot.n is the first local optimum, v_max(0) A matrix index value representing a local optimum value;

if upsilon_max(0) If < N, then:

wherein, Bm, upsilon_max(1)]Represents B [ m, j ]]J-1, …, N local optima, j-upsilon_max(0)+1,…,N；

The above-mentioned circulation is up to upsilon_max(k) And obtaining the signal matrix R finally when the signal matrix is N.

6. The automatic target detection algorithm based on millimeter wave/terahertz wave radiation as claimed in claim 5, wherein the specific method of step (4) is as follows:

the warp of the target radiation signal is expressed as:

where σ represents the signal variance, γ represents the signal mean, R_m[n]Represents the mth row of the signal matrix R;

the standard deviation of the target radiation signal is expressed as:

a one-dimensional warping spectrum based on the standard deviation of the target radiation signal is constructed according to the formula (9) and the formula (10), so that a mixed characteristic spectrum of the target radiation signal, which is denoted as KSD, can be obtained and is expressed as:

time-frequency analysis: performing short-time Fourier transform on the KSD to obtain a mixed time-frequency characteristic spectrum of the target radiation signal, wherein the expression is as follows:

where P ═ (0, 1., P-1) denotes the P-th discrete frequency component, λ denotes the Hamming window function, expressed as:

in the invention, α is equal to 0.42, β is equal to 0.58, and O represents Hamming window width.

7. The automatic target detection algorithm based on millimeter wave/terahertz wave radiation as claimed in claim 6, wherein the convolutional neural network in step (5) comprises 5 layers, each layer comprises convolution, batch normalization, pooling and mapping operation in sequence, and the optimization and extraction of target feature spectrum are realized through five cycles.

8. The automatic target detection algorithm based on millimeter wave/terahertz wave radiation as claimed in claim 7, wherein the specific method of step (5) is as follows:

suppose that

taking layer 1 as an example, for the kth filter, theWith r⁽¹⁾The convolution step of (c) is applied to K to obtain the following signature:

y^(k,1)＝f(K*W^(k,1)+b^(k,1)) (14)

wherein, denotes a convolution operation, b^(k,1)Representing the k characteristic diagram deviation, wherein f (.) is an activation function;

at the distance unit (i ', j'), the corresponding optimization result is:

where H1, …, H, W1, …, W, ⊙ denote Hadamard products, ReLU denotes the activation function, expressed as:

ReLU(η)＝max(0,η) (16)

the feature diagram of the 1 st layer in the pooling layer is H, and

target detection is achieved using the Softmax algorithm, which is expressed as:

where, Φ represents the number of categories,

Technical Field

The invention relates to the field of automatic target detection, in particular to an automatic target detection algorithm based on millimeter wave/terahertz wave radiation.

Background

The millimeter wave/terahertz wave has strong capability of penetrating fog, rain and smoke, and can still normally work all weather in severe weather environment. In view of the unique advantages of millimeter wave/terahertz wave, the terahertz wave/terahertz wave has been widely applied to the fields of security inspection, communication, remote sensing, military and the like. In various applications of millimeter wave/terahertz wave, millimeter wave/terahertz wave imaging is considered as an application technology with the greatest application prospect, and increasingly plays an important role in the aspects of security inspection, airplane landing, collision avoidance systems and the like.

The target detection mode generally comprises two typical means of active and passive. The active mode is a mode in which a target is irradiated with a specific signal source and detection of the target and an image are realized by detecting a reflected or transmitted signal of the signal. In an active system, the system is usually complex, and it is difficult to realize fast and simple imaging.

According to the blackbody radiation theory, all objects above absolute zero in nature radiate electromagnetic energy outwards, and the energy spectrum covers the whole electromagnetic spectrum range. In a plurality of radiation systems, millimeter wave/terahertz wave radiation detection is considered as a novel passive detection technology capable of passively identifying natural radiation of potential targets in application scenes. Millimeter wave/terahertz wave is an electromagnetic wave between microwave and infrared light, and the electromagnetic radiation of millimeter wave/terahertz wave commonly studied belongs to the far infrared and submillimeter wave categories. Generally, millimeter wave/terahertz wave radiation detection has greater advantages, mainly expressed in: compared with microwaves, under the condition of the same radiation imaging resolution, the millimeter wave/terahertz wave radiation system has smaller antenna size, larger bandwidth and stronger anti-interference capability, and is extremely easy to realize the detection and identification of tiny targets; compared with infrared and visible light, the difference of the radiation capability of different objects in the millimeter wave/terahertz wave frequency band is more obvious than that of the infrared frequency band, the millimeter wave/terahertz wave radiation can work day and night, the capacity of limiting the performance of common visible light, infrared and other visible devices in severe weather such as fog and dust is achieved, for example, the transmission attenuation of terahertz waves in the frequency band smaller than 300GHz under the severe weather condition is only one million of visible light and infrared radiation, and the true all-weather and all-day detection is possible.

In addition, the passive working mechanism of millimeter wave/terahertz wave radiation enables the concealment to be excellent, and the method is particularly suitable for application scenes such as battlefields and anti-terrorism. Compared with an active system, the radiation system is simple and can quickly detect and image. However, due to the limitation of the aperture of the detector antenna of the system, the resolution of the radiation system is low, high frequency components in the signal are easily lost, and the effect is seriously influenced by noise. In order to improve the performance of a passive detection system, the invention provides an automatic target detection algorithm based on millimeter wave/terahertz wave radiation.

Disclosure of Invention

In order to solve the technical problems, the invention provides an automatic target detection algorithm based on millimeter wave/terahertz wave radiation, so that the purposes of obviously improving the signal-to-noise ratio and realizing accurate target identification can be achieved.

In order to achieve the purpose, the technical scheme of the invention is as follows:

an automatic target detection algorithm based on millimeter wave/terahertz wave radiation comprises the following processes:

(1) acquiring data of a target radiation signal by using a millimeter wave/terahertz wave radiometer array;

(2) carrying out background estimation on the environment where the target is located by utilizing a background filtering algorithm, and carrying out background suppression on the acquired data;

(3) performing signal enhancement on the data subjected to background suppression by using a local optimization algorithm to obtain a signal matrix;

(4) performing characteristic analysis of the skewness and standard deviation on each column of signals of the signal matrix after signal enhancement, constructing a mixed characteristic spectrum of the target radiation signal on the basis of the characteristic analysis, namely the skewness spectrum based on the standard deviation, performing time-frequency analysis to obtain a mixed time-frequency characteristic spectrum of the target radiation signal,

(5) and optimizing and extracting the mixed time-frequency characteristic spectrum by utilizing a five-layer convolutional neural network, and detecting the target by utilizing a Softmax algorithm.

In the above scheme, in the step (1), a radiometer array composed of radiometers is used for data acquisition of target radiation signals, the radiometer array has 9 radiation modules in total, and a single radiation module has 4 radiation channels and 36 radiation channels in total.

In the above scheme, in the step (1), an oversampling method is used to acquire data, and downsampling is performed on the acquired digital signal, that is, starting from the first digitized signal obtained in the sampling process, each P number of values is a group, and the median of the P number of values is taken as an effective signal to be stored, which is marked as a_M×NWherein M represents the number of rows and N represents the number of columns;

and in the sampling process, the sampling rate and the pulse period of the radiation array controller meet the following requirements:

f_s＞＞γ_num×f_m(1)

wherein f is_sRepresenting the sampling rate, gamma_numNumber of median points, f_mRepresenting the pulse period of the radiating array controller.

In a further technical solution, in the step (2), the background estimation value is expressed as follows:

wherein m represents the matrix A_M×NM-th row in the drawing, n represents the matrix A_M×NThe nth column;

in order to suppress the interference caused by background to the target radiation signal, the matrix A is used_M×NSubtracting the background estimation value obtained in the formula (2) from each value to obtain a background-suppressed value:

in order to further suppress static interference in the signal, the following processing is performed:

U[m,n]＝δA[m,n-1]+(1-δ)A[m,n](4)

wherein, δ represents a weighting factor, the value range is 0-1, and δ is 0.7 in the invention.

In a further technical scheme, the specific method in the step (3) is as follows:

enhancing signals of each row of the matrix by using a local optimization algorithm, wherein the enhanced signals are expressed as:

wherein, U [ tau ]_max(0),n]Represents U [ i, n ]]1, M first local optimum, τ_max(0) A matrix index value representing a local optimum value;

if τ_max(0) If < M, then:

wherein, U [ tau ]_max(1),n]Represents U [ i, n ]]1, M local optima, i τ_max(0)+1,...,M；

The above-mentioned cycle is up to tau_max(k) Until M;

similarly, each column of signals of the matrix is enhanced by using a local optimization algorithm, and the enhanced signals can be represented as:

wherein, Bm, upsilon_max(0)]Represents B [ m, j ]]J 1.. cndot.n is the first local optimum, v_max(0) A matrix index value representing a local optimum value;

if upsilon_max(0) If < N, then:

wherein, Bm, upsilon_max(1)]Represents B [ m, j ]]J 1.. times, N local optima, j υ_max(0)+1,...,N。

As described aboveCirculate until upsilon_max(k) And obtaining the signal matrix R finally when the signal matrix is N.

In a further technical scheme, the specific method in the step (4) is as follows:

the warp of the target radiation signal is expressed as:

where σ represents the signal variance, γ represents the signal mean, R_m[n]Represents the mth row of the signal matrix R;

the standard deviation of the target radiation signal is expressed as:

a one-dimensional warping spectrum based on the standard deviation of the target radiation signal is constructed according to the formula (9) and the formula (10), so that a mixed characteristic spectrum of the target radiation signal, which is denoted as KSD, can be obtained and is expressed as:

time-frequency analysis: and performing short-time Fourier transform on the KSD to obtain a mixed time-frequency characteristic spectrum of the target radiation signal, wherein the expression is as follows:

where P ═ (0, 1., P-1) denotes the P-th discrete frequency component, λ denotes the Hamming window function, expressed as:

in the invention, α is equal to 0.42, β is equal to 0.58, and O represents Hamming window width.

In a further technical scheme, the convolutional neural network in the step (5) comprises 5 layers, each layer sequentially comprises convolution, batch normalization, pooling and mapping operation, and the optimization and extraction of the target feature spectrum are realized through five cycles.

In a further technical scheme, the specific method of the step (5) is as follows:

suppose that

For convolutional neural network input values, where C1, H and W represent the dimensions of the image in the horizontal and vertical directions, respectively, and the L-th layer has K ∈ {1_lA filter;

taking layer 1 as an example, for the kth filter, theWith r⁽¹⁾The convolution step of (c) is applied to K to obtain the following signature:

y^(k,1)＝f(K*W^(k,1)+b^(k,1)) (14)

wherein, denotes a convolution operation, b^(k,1)Representing the k characteristic diagram deviation, wherein f (.) is an activation function;

at the distance unit (i ', j'), the corresponding optimization result is:

where H1., H, W1., W, ⊙ denote Hadamard products, ReLU denotes an activation function, expressed as:

ReLU(η)＝max(0,η) (16)

the feature diagram of the 1 st layer in the pooling layer is H, and

the kth filter characteristic diagram is

The operation sequentially traverses all layers to obtain a target characteristic spectrum Z;

target detection is achieved using the Softmax algorithm, which is expressed as:

where, Φ represents the number of categories,

denotes Z belongs to

The probability of a class, θ, represents the Softmax parameter.

Through the technical scheme, the target automatic detection algorithm based on millimeter wave/terahertz wave radiation adopts the radiometer array composed of the radiometers with four channels to collect signals, can receive more data simultaneously, and collects the signals more comprehensively; by means of radiation background suppression and effective enhancement of weak radiation signals, automatic detection of target radiation signals can be achieved, and detection results are accurate.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

Fig. 1 is a schematic diagram of an automatic target detection system based on millimeter wave/terahertz wave radiation according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of an automatic target detection algorithm based on millimeter wave/terahertz wave radiation according to an embodiment of the present invention;

FIG. 3 is a two-dimensional radiation image acquired by a radiometer array;

FIG. 4 is a graph of a single-row target radiation terahertz signal as disclosed in an embodiment of the present invention;

FIG. 5 is a result image after background suppression as disclosed in the embodiments of the present invention;

FIG. 6 is a signal matrix R after signal enhancement according to an embodiment of the present invention;

FIG. 7 shows the results of feature analysis;

FIG. 8 is a schematic view of a single convolution.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

The invention provides an automatic target detection algorithm based on millimeter wave/terahertz wave radiation, which comprises the following specific embodiments:

when a millimeter wave/terahertz radiometer is used for target detection, a schematic diagram of a system is shown in fig. 1, and the system mainly comprises a terahertz radiometer array (an antenna is integrated in the radiometer), a three-dimensional precision console, data acquisition equipment, signal processing equipment and display equipment.

The radiometer array adopted by the invention is a W-band radiometer, 9 radiation modules are provided in total, and a single radiation module is provided with 4 radiation channels and 36 radiation channels in total. The radiometer operates in a differential mode to better show the contrast of the target to the background. The acquisition frequency of the data acquisition card is 500KHz, each 500 sampling points form a group, and the median value is taken as an effective point.

As shown in fig. 2, the target automatic detection algorithm of the present invention is as follows:

firstly, acquiring data of a target radiation signal by using a millimeter wave/terahertz wave radiometer array;

according to the invention, an oversampling method is adopted for data acquisition, and the acquired two-dimensional data is shown in fig. 3, so that the signal-to-noise ratio of a radiation image is extremely low, and the detection of the circular hole is difficult to realize.

On the basis of oversampling, the acquired digital signal is down-sampled, that is, starting from the first digitized signal obtained in the sampling process, every P numerical values are a group, and the median or mean of the P numerical values is taken as an effective signal to be stored and recorded as A_M×NWherein M represents the number of rows and N represents the number of columns; the single-line target radiates a terahertz signal as shown in fig. 4.

And in the sampling process, the sampling rate and the pulse period of the radiation array controller meet the following requirements:

f_s＞＞γ_num×f_m(1)

wherein f is_sRepresenting the sampling rate, gamma_numNumber of median points, f_mRepresenting the pulse period of the radiating array controller.

Secondly, performing background estimation on the environment where the target is located by using a background filtering algorithm, and performing background suppression on the acquired data;

the background estimate is represented as follows:

wherein m represents the matrix A_M×NM-th row in the drawing, n represents the matrix A_M×NThe nth column;

in order to suppress the interference caused by background to the target radiation signal, the matrix A is used_M×NSubtracting the background estimation value obtained in the formula (2) from each value to obtain a background-suppressed value:

in order to further suppress static interference in the signal, the following processing is performed:

U[m,n]＝δA[m,n-1]+(1-δ)A[m,n](4)

wherein, δ represents a weighting factor, the value range is 0-1, and δ is 0.7 in the invention. The results after background suppression are shown in fig. 5.

Thirdly, performing signal enhancement on the data subjected to background suppression by using a local optimization algorithm to obtain a signal matrix;

enhancing signals of each row of the matrix by using a local optimization algorithm, wherein the enhanced signals are expressed as:

wherein, U [ tau ]_max(0),n]Represents U [ i, n ]]1, M first local optimum, τ_max(0) A matrix index value representing a local optimum value;

if τ_max(0) If < M, then:

wherein, U [ tau ]_max(1),n]Represents U [ i, n ]]1, M local optima, i τ_max(0)+1,...,M；

The above-mentioned cycle is up to tau_max(k) Until M;

similarly, each column of signals of the matrix is enhanced by using a local optimization algorithm, and the enhanced signals can be represented as:

wherein, Bm, upsilon_max(0)]Represents B [ m, j ]]J 1.. times.n is the first local optimum, v_max(0) A matrix index value representing a local optimum value;

if upsilon_max(0) If < N, then:

wherein, Bm, upsilon_max(1)]Represents B [ m, j ]]J 1.. times, N local optima, j υ_max(0)+1,...,N。

The above-mentioned circulation is up to upsilon_max(k) Up to N, the signal matrix R is finally obtained, as shown in fig. 6.

Fourthly, performing characteristic analysis of the warping degree and the standard deviation on each column of signals of the signal matrix after the signal enhancement, constructing a mixed characteristic spectrum of the target radiation signal on the basis of the characteristic analysis, namely the warping degree spectrum based on the standard deviation, and performing time-frequency analysis on the warping degree spectrum to obtain the mixed time-frequency characteristic spectrum of the target radiation signal;

the warp of the target radiation signal is expressed as:

where σ represents the signal variance, γ represents the signal mean,R_m[n]representing the mth row of the signal matrix R.

The standard deviation of the target radiation signal is expressed as:

a one-dimensional warping spectrum based on the standard deviation of the target radiation signal is constructed according to the equations (9) and (10), so as to obtain a mixed characteristic spectrum of the target radiation signal, which is denoted as KSD in fig. 7 and is expressed as:

time-frequency analysis: and performing short-time Fourier transform on the KSD to obtain a mixed time-frequency characteristic spectrum of the target radiation signal, wherein the expression is as follows:

where P ═ (0, 1., P-1) denotes the P-th discrete frequency component, λ denotes the Hamming window function, expressed as:

in the invention, α is equal to 0.42, β is equal to 0.58, and O represents Hamming window width.

And fifthly, optimizing and extracting the mixed time-frequency characteristic spectrum by utilizing a five-layer convolutional neural network, and detecting the target by utilizing a Softmax algorithm.

The convolutional neural network comprises 5 layers, each layer sequentially comprises convolution, batch normalization, pooling and mapping operation, and target feature spectrum optimization and extraction are achieved through five times of circulation. The schematic diagram of the single convolution is shown in fig. 8.

The specific method comprises the following steps:

suppose that

For convolutional neural network input valuesWhere C is 1, H and W represent the dimensions of the image in the horizontal and vertical directions, respectively, and the L ∈ {1_lA filter;

taking layer 1 as an example, for the kth filter, the

With r⁽¹⁾The convolution step of (c) is applied to K to obtain the following signature:

y^(k,1)＝f(K*W^(k,1)+b^(k,1)) (14)

wherein, denotes a convolution operation, b^(k,1)Representing the k characteristic diagram deviation, wherein f (.) is an activation function;

at the distance unit (i ', j'), the corresponding optimization result is:

where H1., H, W1., W, ⊙ denote Hadamard products, ReLU denotes an activation function, expressed as:

ReLU(η)＝max(0,η) (16)

the feature diagram of the 1 st layer in the pooling layer is H, and

the kth filter characteristic diagram is

The operation sequentially traverses all layers to obtain a target characteristic spectrum Z;

target detection is achieved using the Softmax algorithm, which is expressed as:

where, Φ represents the number of categories,

denotes Z belongs to

The probability of a class, θ, represents the Softmax parameter.

To measure the performance of the algorithm of the present invention, the concept of a specific signal-to-noise ratio is as follows:

wherein, I and J respectively represent the size of the current region.

The specific test results are shown in Table 1.

TABLE 1 comparison of the algorithm of the present invention with the constant false alarm probability algorithm

Algorithm	Constant false alarm probability	Generalized likelihood ratio test	Algorithm of the invention
				Signal-to-noise ratio (dB)	-37.62	-34.45	-25.72
Percent identification (%)	70	50	70

Compared with the constant false alarm probability and generalized likelihood ratio test algorithm, the target detection algorithm involved in the invention can significantly improve the signal-to-noise ratio of the signal, as shown in table 1. Through 10 identification experiments on the same target in different environments, the identification probability of the target identification based on the convolutional network designed by the invention reaches 70 percent, which is obviously higher than that of other constant false alarm probability algorithms.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.