阅读说明：本技术 基于物理光学建模的认知雷达隐身目标检测方法 (Cognitive radar stealth target detection method based on physical optical modeling ) 是由 汪清 李萌 郭钰章 高丽蓉 于 2020-10-09 设计创作，主要内容包括：本发明属于隐身目标检测领域,为提出一种基于物理光学建模的认知雷达隐身目标检测方法,与目前存在的方法相比该方法将认知雷达引入隐身目标检测中,能够更好的感知环境信息,提高检测概率。为此,本发明采用的技术方案是,基于物理光学建模的认知雷达隐身目标检测方法,先用物理光学的方法对隐身目标的雷达横截面积进行建模,然后布置发射机和接收机两个雷达,接收机根据接收到的回波信号功率计算信噪比,不断调整接收机与发射机和目标之间的角度,使得信噪比达到最大,从而提高检测概率。本发明主要应用于雷达设计制造场合。(The invention belongs to the field of stealth target detection, and provides a cognitive radar stealth target detection method based on physical optical modeling. Therefore, the technical scheme adopted by the invention is that the cognitive radar stealth target detection method based on physical optics modeling firstly models the radar cross-sectional area of the stealth target by using a physical optics method, then arranges two radars of a transmitter and a receiver, and the receiver calculates the signal-to-noise ratio according to the received echo signal power and continuously adjusts the angle between the receiver and the transmitter and the target to maximize the signal-to-noise ratio, thereby improving the detection probability. The invention is mainly applied to the occasions of designing and manufacturing the radar.)

1. A cognitive radar stealth target detection method based on physical optics modeling is characterized in that a physical optics method is used for modeling the radar cross-sectional area of a stealth target, then a transmitter and a receiver are arranged, the receiver calculates the signal-to-noise ratio according to the received echo signal power, and the angle between the receiver and the transmitter and the target is adjusted continuously, so that the signal-to-noise ratio is maximized, and the detection probability is improved.

2. The cognitive radar stealth target detection method based on physical optics modeling as claimed in claim 1, wherein the stealth target is modeled by a physical optics method, and the current density is as follows according to an electromagnetic field correlation theory:

whereinIs an outward unit of the normal vector,is the intensity of the incident magnetic field, wherein

Is a vector of propagation of the beam of light,is the intensity of the incident electric field, Z₀Is the free-space intrinsic impedance, from which the curl is derived:

from the rotation, the scattered field strength is obtained as

W is the angular frequency, r_sIs the distance of the receiver from the object,after dividing a target into small triangular surfaces, a receiver obtains a direction vector from the triangular surfaces;

as can be seen from the definition of the cross-sectional area of the radar,

wherein R is the distance from the transmitter to the target, R is the polarization, θ is the azimuth angle, and φ is the vertical angle, whereby the radar cross-sectional area is expressed in terms of incident field strength and scattered field strength as a function of R, θ, φ;

according to the radar cross-sectional area expression, when r and phi are fixed, the RCS is a function of theta, so that the radar transmitter continuously adjusts theta between the RCS and a target according to RCS information received by the receiver, so as to find theta which enables the RCS to be maximum, and the receiver calculates the signal-to-noise ratio according to the received RCS:

wherein, P_tIs transmitter power, G_tIs the transmitter gain, G_rIs the receiver gain, λ is the wavelength, K is the Boltzmann constant, T_sIs the system noise temperature, B is the transmit waveform bandwidth, R_tIs the distance of the transmitter to the target, R_rIs the target-to-receiver distance and L is the transmitter and receiver loss factor.

3. The method as claimed in claim 1, wherein the position of the transmitter and the receiver are adjusted to find the angle of maximum RCS, so as to maximize the received signal-to-noise ratio, thereby improving the detection probability, and the approximate relationship between the detection probability and the signal-to-noise ratio and the false-view probability is as follows:

P_dis the detection probability, P_faThe probability of the false scene is, and under the condition that the probability of the false scene is certain, the detection probability is increased along with the increase of the signal-to-noise ratio.

Technical Field

The invention belongs to the field of stealth target detection, and particularly relates to a detection method based on passive radar, which is characterized in that a stealth target is modeled by physical optics, and a cognitive radar network is arranged to find out the angle of the largest detected stealth target, so that the detection probability is improved.

Background

The cognitive radar system consists of a transmitter, a receiver and an environment and is a closed-loop system [1 ]. One of the key functions of the receiver is to feed back the received environment information to the transmitter, and the transmitter adjusts parameters according to the feedback information, thereby continuously improving the estimation and detection performance of the system.

The stealth target [2] is a target which is difficult to find by an enemy detection system by weakening characteristic information such as radar reflected waves, infrared radiation and the like by using various technologies. The radar cross-sectional area [3] is an important index for measuring the stealth characteristic of the target and represents a physical quantity of the intensity of an echo generated by the target under the irradiation of a radar wave. Is 4 pi times the ratio of the scattered power at the receiver to the power density of the incident wave on the target in a unit solid angle. The physical optics method [4] is a method for modeling the cross-sectional area of the radar, and can more intuitively observe the size of the cross street area of the radar with stealth targets at different angles.

The rapid development of stealth technology provides a serious challenge for tactical defense systems, and people are forced to think about how to destroy stealth weapons and study anti-stealth technology. Bistatic and polybase are an effective anti-stealth technique by exploiting the information of angular diversity [5 ]. Stratospheric balloon-loaded bistatic radar systems are used to improve the detection performance of stealth targets under the influence of under-nyquist scattering wave spoofing interference [6 ]. The bistatic radar system loaded by the stratospheric balloon detects the F-117 stealth target model [7 ].

On the basis of research and introduction of basic theories and methods, documents [8] and [9] summarize several technical characteristics and countermeasures of radar stealth and anti-stealth, analyze the development outline and the limitation of stealth technology, and introduce several approaches and methods of radar anti-stealth technology aiming at the limitation.

[1]S.Haykin,“Cognitive radar:a way of the future,”IEEE Signal Processing Magazine,vol. 23,no.1,pp.30–40,Jan.2006.

[2] Richardson modern stealth aircraft [ M ] science publishers, 1991.

[3]Knott E F,Shaeffer J F,Tuley M T.Radar Cross Section[J].2004,41(4):215-217.

[4] Han Lei, Wang Zi Ying, Radar stealth and reverse stealth technology [ J ] electronic warship countermeasure, 2006,29(2):34-38.

[5]H.Kuschel,J.Heckenbach,S.Muller,and R.Appel,“On the potentials of passive, multistatic,low frequency radars to counter stealth anddetect low flying targets,”in Radar Conference,2008.RADAR’08.IEEE,2008,pp.1–6.

[6]M.Barbary and P.Zong,“Novel anti-stealth on sub-nyquist scattering wave deception jammer with stratospheric balloon-borne bistatic radar using ka-stap-ftrab algorithm,”IEEE Sensors Journal,vol.15,no.11,pp.6437–6453,2015.

[7]M.A.Barbary and P.Zong,“A novel stealthy target detection based on stratospheric balloon-borne positional instability due to random wind,”Radioengineering,vol.23,no.4,pp. 1192–1202,2014.

[8] Han Lei, Wang Zi Ying, Radar stealth and reverse stealth technology [ J ] electronic warship countermeasure, 2006,29(2):34-38.

[9] Li development and implementation method of radar stealth technology [ J ] informatization research, 2008,34(8):3-5.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide a cognitive radar stealth target detection method based on physical optical modeling. Therefore, the technical scheme adopted by the invention is that the cognitive radar stealth target detection method based on physical optics modeling firstly models the radar cross-sectional area of the stealth target by using a physical optics method, then arranges two radars of a transmitter and a receiver, calculates the signal-to-noise ratio by the receiver according to the received echo signal power, and continuously adjusts the angle between the receiver and the transmitter and the target to maximize the signal-to-noise ratio, thereby improving the detection probability.

Modeling a stealth target by a physical optics method, wherein the current density is as follows according to an electromagnetic field correlation theory:

whereinIs an outward unit of the normal vector,is the intensity of the incident magnetic field, wherein

Is a vector of propagation of the beam of light,is the intensity of the incident electric field, Z₀Is the free-space intrinsic impedance, from which the curl is derived:

from the rotation, the scattered field strength is obtained as

W is the angular frequency, r_sIs the distance of the receiver from the object,after dividing the target into small triangular planes, receivingMachine to triangular face direction vector.

As can be seen from the definition of the cross-sectional area of the radar,

wherein R is the distance from the transmitter to the target, R is the polarization, θ is the azimuth angle, and φ is the vertical angle, whereby the radar cross-sectional area is expressed in terms of incident field strength and scattered field strength as a function of R, θ, φ;

according to the radar cross-sectional area expression, when r and phi are fixed, the RCS is a function of theta, so that a radar transmitter continuously adjusts theta between the RCS and a target according to RCS information received by a receiver, so that theta enabling the RCS to be maximum is found, and the receiver calculates the signal-to-noise ratio according to the received RCS:

wherein, P_tIs transmitter power, G_tIs the transmitter gain, G_rIs the receiver gain, λ is the wavelength, K is the Boltzmann constant, T_sIs the system noise temperature, B is the transmit waveform bandwidth, R_tIs the distance of the transmitter to the target, R_rIs the target-to-receiver distance and L is the transmitter and receiver loss factor.

The positions of the transmitter and the receiver are adjusted to find the maximum angle of the RCS, so that the receiving signal-to-noise ratio is maximum, the detection probability is improved, and the approximate relations among the detection probability, the signal-to-noise ratio and the virtual scene probability are as follows:

P_dis the detection probability, P_faThe probability of the false scene is the probability of the false scene, and under the condition that the probability of the false scene is certain, the detection probability is increased along with the increase of the signal-to-noise ratio.

The method comprises the following specific steps:

step 1: modeling the radar cross-sectional area of the stealth target by a physical optics method;

step 2: the transmitter sends a signal, and the receiver calculates the RCS of the stealth target according to the received signal;

and step 3: the receiver calculates the receiving signal-to-noise ratio of the receiver according to the RCS;

and 4, step 4: adjusting the angle between the transmitter and the receiver and the target;

and 5: finding an angle that maximizes the received signal-to-noise ratio;

step 6: the detection probability at this time is calculated.

The invention has the characteristics and beneficial effects that:

the algorithm has the advantages that the angle between the transmitter, the receiver and the target can be adaptively adjusted according to the received information, so that the receiving signal-to-noise ratio of the receiver is maximized, and the purpose of improving the detection probability is achieved.

Most of the existing stealth target detection methods are passive radar detection, and the cognitive radar has the advantages of being capable of self-adapting to environmental information, adjusting parameters in time and improving detection probability compared with the passive radar. The method is mainly characterized in that a complete method framework for detecting the stealth target by the cognitive radar is provided, and a foundation is laid for further recognizing the stealth target by the cognitive radar. Meanwhile, the stealth target detection angle is determined according to the receiving signal-to-noise ratio maximization, a theoretical basis can be provided for the arrangement of networking radars next time, the signal-to-noise ratio of a radar network can be better improved, and therefore the detection probability is improved.

Simulation results show that the stealth target can be well detected by the cognitive radar. Fig. 1 is a diagram of a physical optics constructed F-35 stealth target 3D model, fig. 2 is a diagram of a physical optics constructed F-35 stealth target 2D model, fig. 3 is a diagram of a trend of a radar cross-sectional area changing with a horizontal angle under a condition that a vertical angle is fixed, and fig. 4 is a diagram of a detection probability changing with a signal-to-noise ratio under a condition that a false scene probability is fixed.

Description of the drawings:

FIG. 1 physical optics constructs an F-35 stealth target 3D model.

FIG. 2 physical optics constructs an F-35 stealth target 2D model.

Fig. 3 shows the trend of the radar cross-sectional area with a fixed vertical angle as the horizontal angle changes.

Fig. 4 is a diagram showing the relationship between the detection probability and the change of the signal-to-noise ratio under a certain false scene probability.

FIG. 5 is a flow chart of the present invention.

Detailed Description

After cognitive radar was proposed, it was rapidly developed. It can perform the basic performance of radar on demand, such as target detection and tracking in increasingly complex and controversial radar environments (containing large amounts of clutter and various forms of interference). The cognitive radar system can obtain available information through priori knowledge, an external database and task priority, adjust the cognitive radar system according to the environment, and improve the tracking, detecting, estimating and identifying performance of the cognitive radar system through adaptively changing relevant parameters.

At present, the technology for detecting the stealth target in China is continuously developed, and most of the existing key technologies are used for identifying the stealth target by using passive radars. The technology for recognizing the stealth target by the cognitive radar is not mature, and the ability of the radar for sensing and adapting to the environment complexity can be improved by continuous exploration, so that the performance of the radar for detecting the stealth target is improved. The invention provides a method for recognizing a stealth target by a basic cognitive radar, and lays a foundation for the next better research.

In order to better detect the stealth target, the characteristics of the stealth target need to be modeled first. Modeling the radar cross-sectional area of the stealth target by a physical optics method, wherein the basic principle is as follows: according to the theory related to electromagnetic field, the current density is:

whereinIs an outward unit of the normal vector,is the incident magnetic field strength. Wherein

Is a vector of propagation of the beam of light,is the intensity of the incident electric field, Z₀Is the free space intrinsic impedance. The curl can thus be obtained:

from the rotation, a scattered field strength of

W is the angular frequency, r_sIs the distance of the receiver from the object,is the direction vector from the receiver to the triangular surface after dividing the object into small triangular surfaces.

The radar cross section is the reflection cross section of the radar, the principle of radar detection is that the transmitted electromagnetic wave irradiates the surface of an object and then reflects back to a receiving antenna, the less the electromagnetic wave which irradiates the surface of the object and returns along the original path, the smaller the radar cross section, the smaller the signal characteristic of the radar to a target, and the shorter the detection distance, so the radar cross section can be expressed by the following formula:

where R is the transmitter-to-target distance, R is the polarization, θ is the azimuth angle, and φ is the vertical angle. The cross-sectional area of the radar is thus expressed in terms of the incident field strength and the scattered field strength, which are functions of r, theta and phi.

According to the radar cross-sectional area expression, the RCS is a function of theta when r and phi are fixed, so that the radar transmitter can find theta which enables the RCS to be maximum by continuously adjusting theta between the RCS and a target according to RCS information received by the receiver.

The receiver calculates the signal-to-noise ratio from the received RCS:

wherein, P_tIs transmitter power, G_tIs the transmitter gain, G_rIs the receiver gain, λ is the wavelength, K is the Boltzmann constant, T_sIs the system noise temperature, B is the transmit waveform bandwidth, R_tIs the distance of the transmitter to the target, R_rIs the target-to-receiver distance and L is the transmitter and receiver loss factor.

The signal-to-noise ratio of the radar network is as follows:

where i is the number of transmitters and j is the number of receivers.

The cognitive radar adjusts horizontal angles according to the signal-to-noise ratio received by the receiver, finds several angles with the largest cross-sectional area of the radar, and arranges and receives the radar, so that the signal-to-noise ratio of the networking radar is improved better, and the detection probability of the networking radar is calculated.

The approximate relationship between the detection probability and the signal-to-noise ratio and the false scene probability is as follows:

P_dis the detection probability, P_faThe probability of the false scene is the probability of the false scene, and under the condition that the probability of the false scene is certain, the detection probability is increased along with the increase of the signal-to-noise ratio.

The specific scheme is as follows:

step 1: modeling the radar cross-sectional area of the stealth target by a physical optics method;

step 2: the transmitter sends a signal, and the receiver calculates the RCS of the stealth target according to the received signal;

and step 3: the receiver calculates the receiving signal-to-noise ratio of the receiver according to the RCS;

and 4, step 4: adjusting the angle between the transmitter and the receiver and the target;

and 5: finding several angles that maximize the received signal-to-noise ratio;

step 6: the receiver is arranged at the maximum reception angle.

And 7: calculating the signal-to-noise ratio of the networking radar;

step 6: calculating the detection probability of the networking radar at the moment;

one embodiment of the present invention is as follows:

the cognitive radar waveform optimization method comprises the following steps:

step 1: modeling the radar cross-sectional area of the stealth target by a physical optical method, and defining according to the radar cross-sectional area

Step 2: the receiver calculates the received signal-to-noise ratio of the receiver from the RCS,

and step 3: adjusting the angle between transmitter and receiver, target, i.e. horizontal angle at maximum signal-to-noise ratio

And 4, step 4: finding several angles that maximize the received signal-to-noise ratio;

and 5: the receiver is arranged at the maximum reception angle.

Step 6: computing networking radar signal-to-noise ratio

And 7: calculating the detection probability of the networking radar at the moment