Unmanned aerial vehicle target detection and micro-Doppler parameter estimation method and device

Abstract

The invention discloses an unmanned aerial vehicle target detection and micro-Doppler parameter estimation method and device, and the method comprises the steps: carrying out the target detection of an unmanned aerial vehicle through detecting an echo signal by employing a GLRT detector; when the unmanned aerial vehicle target is detected, estimating the Doppler frequency shift of the unmanned aerial vehicle target by using a first estimator; the first estimator is designed based on a DE algorithm; the Doppler frequency shift is determined based on the radar position of the unmanned aerial vehicle main body part, the unmanned aerial vehicle position and the unmanned aerial vehicle speed; estimating the micro-Doppler frequency shift of the unmanned aerial vehicle target by using a second estimator; the second estimator is designed based on a PSO algorithm; the micro-Doppler frequency shift is determined based on the rotating speed and the initial phase of the rotor wing part of the unmanned aerial vehicle, so that the efficient and high-precision unmanned aerial vehicle target detection and micro-Doppler parameter estimation method is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a method and device for unmanned aerial vehicle (UAV) target detection and micro-Doppler parameter estimation. Background Art

[0002] With the rapid development of the drone industry, it has been widely used in both military and civilian fields. However, the increasing number of drones has raised some security concerns, such as the potential threat to air traffic management and the threat to individuals or the public from allowing unauthorized drones to enter private or sensitive areas. Therefore, accurate detection and parameter estimation of small drones are of great significance.

[0003] Drones are small in size, and most are designed with plastic as the fuselage material for lightness. Their radar cross section (RCS) is small, and their flight speed is slow. The Doppler characteristics are easily hidden in clutter and noise, increasing the difficulty of detection. In addition, the complex environment in cities makes it difficult to detect multi-rotor drones.

[0004] Existing research is mainly based on micro-Doppler analysis, which uses the frequency modulation of radar signals generated by target vibration or rotation to perform target detection. Combined with time-frequency analysis, target detection is converted into micro-Doppler feature extraction. The short-time Fourier transform time-frequency analysis results of the drone rotor echo can obtain information such as blade speed and blade length for target detection and recognition. However, in a low signal-to-noise ratio environment, it is affected by multiple targets and interference, resulting in generally low detection accuracy and low computational efficiency.

[0005] Therefore, how to provide a UAV target detection and micro-Doppler parameter estimation method with strong environmental adaptability, high detection accuracy and efficient computing performance has become an important issue. Summary of the Invention

[0006] In order to solve the above problems existing in the prior art, the present invention provides a method and device for UAV target detection and micro-Doppler parameter estimation.

[0007] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a method for detecting a target on a drone and estimating micro-Doppler parameters, the method comprising:

[0009] Using GLRT detector, drone target detection is performed by detecting echo signals;

[0010] When a UAV target is detected, a Doppler shift of the UAV target is estimated using a first estimator; the first estimator is designed based on a DE algorithm; the Doppler shift is determined based on a radar position of a main part of the UAV, a position of the UAV, and a speed of the UAV;

[0011] The micro-Doppler frequency shift of the UAV target is estimated by using a second estimator; the second estimator is designed based on a PSO algorithm; and the micro-Doppler frequency shift is determined based on the rotation speed and initial phase of the UAV rotor part.

[0012] Optionally, a GLRT detector is used to detect drone targets by detecting echo signals, including:

[0013] Using the GLRT detector, drone target detection is performed by comparing the likelihood ratio between the echo signal and the drone echo pulse signal; the drone echo pulse signal is determined based on the distributed radar system model.

[0014] Optionally, a GLRT detector is used to detect drone targets by comparing the likelihood ratio between the echo signal and the drone echo pulse signal, including:

[0015] Constructing a binary hypothesis test for drone target detection; in the binary hypothesis test, the H0 hypothesis indicates that no drone target is detected in the echo signal; the H1 hypothesis indicates that the drone target is detected in the echo signal;

[0016] Calculating a first probability density function of the UAV target under the H0 hypothesis and a second probability density function under the H1 hypothesis, and constructing a GLRT decision rule based on the first probability density function and the second probability density function;

[0017] Derived a first maximum likelihood estimate of the RCS of the drone main body part of the drone target and a second maximum likelihood estimate of the RCS of the rotor part of the drone target;

[0018] The first maximum likelihood estimate and the second maximum likelihood estimate are brought into the GLRT decision rule to obtain a GLRT detector, and UAV target detection is performed based on the GLRT detector.

[0019] Optionally, the drone echo pulse signal includes:

[0020] y a =y a,body +y a,blade +w a ;

[0021] Among them, y a Represents the UAV echo pulse signal; y a,bodyrepresents the main part of the drone echo pulse signal received by the a-th radar; y a,blade represents the echo pulse signal of the rotor part of the UAV received by the a-th radar; w a represents the additive noise received in the a-th radar.

[0022] Optionally, when a UAV target is detected, estimating the Doppler shift of the UAV target using a first estimator includes:

[0023] When a drone target is detected, a Doppler shift optimization model is constructed;

[0024] The first estimator is used to optimize the Doppler frequency shift of the UAV target through the DE algorithm based on the Doppler frequency shift optimization model, wherein the optimization process aims to maximize the detection statistic of the main part of the UAV.

[0025] Optionally, estimating the micro-Doppler shift of the UAV target using a second estimator includes:

[0026] Construct a micro-Doppler frequency shift optimization model;

[0027] The micro-Doppler frequency shift of the UAV target is obtained by optimizing the micro-Doppler frequency shift optimization model using a second estimator through a PSO algorithm. The optimization process aims to maximize the detection statistic of the UAV rotor part.

[0028] In a second aspect, the present invention provides a UAV target detection and micro-Doppler parameter estimation device, the UAV target detection and micro-Doppler parameter estimation device comprising:

[0029] The detection module is used to detect UAV targets by detecting echo signals using the GLRT detector;

[0030] a first estimation module, configured to estimate a Doppler shift of a UAV target using a first estimator when a UAV target is detected; the first estimator being designed based on a DE algorithm; and the Doppler shift being determined based on a radar position of a main UAV body, a UAV position, and a UAV speed;

[0031] The second estimation module is used to estimate the micro-Doppler frequency shift of the UAV target using a second estimator; the second estimator is designed based on the PSO algorithm; the micro-Doppler frequency shift is determined based on the rotation speed and initial phase of the UAV rotor part.

[0032] In a third aspect, the present invention provides an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0033] Memory for storing computer programs;

[0034] The processor is used to implement the method steps described in any of the above-mentioned UAV target detection and micro-Doppler parameter estimation methods when executing the computer program stored in the memory.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method steps described in any of the above-mentioned UAV target detection and micro-Doppler parameter estimation methods are implemented.

[0036] The present invention provides a method for detecting and estimating micro-Doppler parameters of unmanned aerial vehicles (UAVs). This method uses a GLRT detector to detect echo signals, effectively enabling UAV target detection in low signal-to-noise ratio (SNR) or complex clutter environments, improving robustness. Upon detecting a UAV target, a first estimator designed based on a DE algorithm efficiently and accurately estimates the UAV target's Doppler shift. A second estimator designed based on a PSO algorithm improves the convergence rate of the UAV target's micro-Doppler shift calculation process. The collaborative work of the GLRT detector, the first estimator, and the second estimator effectively improves the detection performance of UAV targets.

[0037] The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 1 is a flow chart of a method for detecting and estimating micro-Doppler parameters of a UAV provided by an embodiment of the present invention;

[0039] Figure 2 It is a schematic diagram of the geometric relationship between radar a and the UAV rotor blades;

[0040] Figure 3 1 is a flow chart of Algorithm 1 for estimating the Doppler shift of a UAV target using a first estimator according to an embodiment of the present invention;

[0041] Figure 4 2 is a flow chart of Algorithm 2 for estimating the micro-Doppler shift of a UAV target using a second estimator provided by an embodiment of the present invention;

[0042] Figure 5 It is a schematic diagram of the simulation scenario of each node and target location of the distributed radar system;

[0043] Figure 6 This is a schematic diagram of the UAV target simulation results;

[0044] Figure 7This is a schematic diagram of the simulation results of micro-Doppler parameter estimation based on DE and PSO algorithms;

[0045] Figure 8 This is the simulation result diagram of micro-Doppler parameter estimation;

[0046] Figure 9 This is a schematic structural diagram of a UAV target detection and micro-Doppler parameter estimation device provided by an embodiment of the present invention;

[0047] Figure 10 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0049] In order to solve the problem that the existing UAV target detection technology is affected by multiple targets and interference in a low signal-to-noise ratio environment, resulting in generally low detection accuracy and low computational efficiency, the embodiment of the present invention provides a UAV target detection and micro-Doppler parameter estimation method, see Figure 1 , Figure 1 : This is a flow chart of a method for drone target detection and micro-Doppler parameter estimation provided by an embodiment of the present invention, which specifically includes the following steps:

[0050] In step S101, a GLRT detector is used to detect the UAV target by detecting the echo signal.

[0051] In the embodiment of the present invention, the drone target detection by detecting the echo signal is achieved by detecting whether the drone target exists in the echo signal.

[0052] In an embodiment of the present invention, a GLRT (Generalized Likelihood Ratio Test) detector is used to detect the echo signal to perform drone target detection, including:

[0053] Using the GLRT detector, UAV target detection is performed by comparing the likelihood ratio between the echo signal and the UAV echo pulse signal; the UAV echo pulse signal is determined based on the distributed radar system model.

[0054] First, the modeling of the drone echo pulse signal is explained:

[0055] Construct a distributed radar system model and derive the drone echo pulse signal model based on the distributed radar system model.

[0056] In order to simplify the system design and reduce the complexity of the system, it is assumed that each node of the distributed radar system in the embodiment of the present invention is in the self-transmitting and self-receiving mode. The radar is composed of radars, where the position of the ath radar is is the transpose operation.

[0057] In order to separate different radar signals, the carrier frequencies of the transmitted signals of different radars are different. There are two independent transmit and receive channels, that is, the radar observes from different angles.

[0058] The echo of the main part of the drone can be expressed as:

[0059]

[0060] Among them, y a,body (t) represents the echo signal of the main part of the UAV received by the a-th radar at time t; represents the imaginary part; c represents the speed of light; is the carrier frequency; α a,body represents the RCS of the main part of the drone when the drone target is observed by the ath radar; G a represents the propagation gain of the UAV target when it is observed by the ath radar; p=0,1,...P-1, P represents the total number of pulses; T represents the pulse width; E represents the energy of the transmitted signal; R a represents the radar distance between the UAV target and the ath radar; It represents the Doppler frequency shift of the drone body in the ath radar.

[0061] In the embodiment of the present invention, R a is calculated as follows:

[0062] R a =||Θ(0)-Θ a ||2;

[0063] Among them, ||·||2 represents the 2-norm of the vector; Θ a represents the position of the ath radar; the initial position of the UAV target is The speed of the drone target is X represents the position of the drone target in the horizontal direction; Y represents the position of the drone target in the vertical direction; Z represents the position of the drone target in the vertical direction; v X Represents the velocity component of the UAV target in the horizontal axis direction; v Y Represents the velocity component of the UAV target in the longitudinal direction; v Z Represents the velocity component of the UAV target in the vertical axis direction.

[0064] In an embodiment of the present invention, is calculated as follows:

[0065]

[0066] Among them, the pitch angle β in the ath radar a and azimuth angle α a It can be expressed as:

[0067]

[0068] Among them, X a Indicates the position of the ath radar in the horizontal axis direction; Y a Indicates the position of the ath radar in the longitudinal direction; Z a Indicates the position of the ath radar in the vertical axis direction.

[0069] When considering the micro-Doppler characteristics of the rotor part of the UAV in a stationary state, see Figure 2 , Figure 2 is a schematic diagram of the geometric relationship between radar a and the UAV rotor blade, where the ath radar is at the origin O, and the pitch angle and azimuth angle of the ath radar are represented by β a and α a , any scattering point on the rotor blade is point B, the arm length from the blade center to the center of the drone is L, the rotor blade length is l, and l is used b Indicates the distance from the rotor center to the scattering point B. The distance between the UAV and the ath radar is R a .

[0070] When considering the micro-Doppler characteristics of the UAV rotor, the initial phase of the rotor blade where any scattering point B is located is The horizontal angle between the drone arm and the xy plane is ψ, the arm length from the blade center to the drone center is L, the rotor blade length is l, and the rotor blade speed is f r , use l b represents the distance from the rotor center to the scattering point B, and the position of the scattering point OB can be obtained as:

[0071]

[0072] Therefore, the distance-time conversion equation of the scattering point B is:

[0073]

[0074] When the target is in the radar far field, the target distance R is much larger than L and l b ,Right now The distance-time conversion equation of the scattering point B can be simplified as:

[0075]

[0076] Among them, the rotor blades can be regarded as a structure composed of multiple scattering points. By integrating the echo signals of these scattering points, a line target model describing the horizontal rotor can be established. For the overall rotor echo, it is the result of adding the echo signals of all blades on each rotor. Therefore, it is assumed that the drone has multiple rotors, m = 1, 2, ..., M, and the number of rotors is M. There are N blades on the rotor, so the phase difference between the echo signals of two adjacent blades can be expressed as 2π / N. Take the blade at point B as the reference blade (the initial phase is ), then the initial phase of the nth blade is α a,blade represents the RCS of the rotor part of the target when it is observed by the ath radar. The echo of the rotor part of the UAV can be expressed as:

[0077]

[0078] Assuming that the blades rotate in the horizontal plane, that is, ψ = 0, the echo integral of the UAV rotor can be simplified to:

[0079]

[0080] in,

[0081] The rotor part of the P echo pulse signals received by the a-th radar, that is, the rotor part of the drone echo pulse signal can be expressed as the following vector:

[0082] y a,blade =α a,blade h a d a,blade ;

[0083] The main part of the P echo pulse signals received by the a-th radar, that is, the main part of the drone echo pulse signal, can be expressed as the following vector:

[0084] y a,body =α a,body h a d a,body ;

[0085] The specific expansion of each component is:

[0086] h a =[h a1 ,…,h ap ,…,h aP ]

[0087] d a,body =[d a1,body ,…,d ap,body,…,d aP,body ];

[0088] d a,blade =[d a1,blade ,…,d ap,blade ,…,d aP,blade ]

[0089] in,

[0090]

[0091] In the embodiment of the present invention, the overall echo signal of the rotor UAV can be expressed as the superposition of the blade and fuselage signals, that is, the final UAV echo pulse signal is:

[0092]

[0093] In the embodiment of the present invention, it is assumed that the additive noise w received by the a-th radar is a It can be modeled as an independent and identically distributed circularly symmetric complex Gaussian variable, whose distribution is:

[0094]

[0095] That is, the variance is I represents the identity matrix, represents the variance of the additive noise received in the a-th radar.

[0096] In one implementation, a GLRT detector is used to detect a drone target by comparing the likelihood ratio between the echo signal and the drone echo pulse signal, including:

[0097] Construct a binary hypothesis test for drone target detection; in the binary hypothesis test, the H0 hypothesis indicates that the drone target is not detected in the echo signal; the H1 hypothesis indicates that the drone target is detected in the echo signal;

[0098] Calculate the first probability density function of the UAV target under the H0 hypothesis and the second probability density function under the H1 hypothesis, and construct the GLRT decision rule based on the first and second probability density functions;

[0099] deriving a first maximum likelihood estimate of the RCS of the drone main body part of the drone target and a second maximum likelihood estimate of the RCS of the rotor part of the drone target respectively;

[0100] The first maximum likelihood estimate and the second maximum likelihood estimate are introduced into the GLRT decision rule to obtain a GLRT detector, and UAV target detection is performed based on the GLRT detector.

[0101] Without considering special application scenarios such as target occlusion and interference suppression, the detection problem of any state target can be transformed into Therefore, for the ath radar observation data, that is, the drone echo pulse signal y a , the UAV target detection problem can be transformed into the test of the following binary hypothesis:

[0102]

[0103] Where ⊙ represents the Hadamard operator. Assume that the unknown parameters represents the first probability density function of the UAV target under the H0 hypothesis, Represents the second probability density function of the UAV target under the H1 hypothesis.

[0104] In an embodiment of the present invention, They can be expressed as:

[0105]

[0106] Where det{·} represents the determinant of the matrix; K represents the total number of samples of the discrete signal reflected by the target.

[0107] In an embodiment of the present invention, the standard GLRT follows the following GLRT decision rules:

[0108]

[0109] Among them, Λ(y a ) represents y a The test statistic of γ Λ Represents the detection threshold of the detection statistic.

[0110] In the embodiment of the present invention, the RCSα of the drone main body of the drone target is calculated respectively. a,body The first maximum likelihood estimate (ML) And the rotor part RCSα of the drone target a,blade The second maximum likelihood estimate of for:

[0111]

[0112] The scattering coefficient of the drone fuselage is about 100 to 300 times that of the blades in the rotor. Since the reflection coefficient of the rotor is small and has little impact on the detection results, the secondary estimation is used separately to estimate the micro-Doppler frequency shift. The parameter f in r and

[0113] Will and Substituting into the GLRT decision rule, we can construct the drone target GLRT detector:

[0114]

[0115] Choose the method that maximizes the detection statistic Λ(y a ) corresponds to the estimated parameter combination: (Θ(0),Θ a ,V) is regarded as the ath radar in Λ(y a ), Λ(y a,blade ) corresponds to the estimated parameter combination As the a-th channel in-measurement data y a,blade Therefore, the detection statistic is defined as the objective function, and the optimization algorithm is used to calculate the five variables related to the Doppler and micro-Doppler frequency shift of the UAV. Make an estimate and record the estimated result as: This completes the drone target detection.

[0116] Step S102, when a UAV target is detected, the Doppler shift of the UAV target is estimated using a first estimator; the first estimator is designed based on a DE algorithm; the Doppler shift is determined based on the radar position of the UAV main body, the UAV position and the speed.

[0117] In an embodiment of the present invention, a first estimator based on DE (Differential Evolution) is used to estimate the Doppler shift of the drone in the corresponding radar. The DE algorithm is a random search algorithm based on individual differences within a population, with the optimal individual fitness as the optimization goal. It is a multi-objective optimization algorithm that can be used to solve the overall optimal solution in a multidimensional space.

[0118] In an embodiment of the present invention, when a UAV target is detected, estimating the Doppler shift of the UAV target using a first estimator includes:

[0119] When a drone target is detected, a Doppler shift optimization model is constructed;

[0120] The first estimator is used to optimize the Doppler frequency shift of the UAV target through the DE algorithm based on the Doppler frequency shift optimization model. The goal of the optimization process is to maximize the detection statistics of the main part of the UAV.

[0121] In the scenario considered, the Doppler shift By Θ(0),Θ aDetermined together with V, differential evolution includes the steps of initializing the population, mutation operation, patching operation, crossover operation and selection operation. Assume that the Doppler shift optimization model is as follows:

[0122]

[0123] Among them, Λ(y a ;Θ(0),Θ a ,V) can be understood as the detection statistics of the main part of the drone;

[0124] For the convenience of representation, the function is expressed as follows in the following algorithm 1:

[0125]

[0126] Among them, the vector dimension D = 3, x1 = Θ(0), x2 = Θ a , x3=V.

[0127] In this embodiment of the present invention, the process of estimating the Doppler shift of the UAV target using the first estimator is as follows:

[0128] Set the number of individuals M', dimension D, mutation operator F, crossover operator Cr and maximum evolutionary generations of the differential evolution algorithm, that is, the number of iterations T', initialize the position vector of each individual in the population, and the value range of each dimension is determined according to the physical constraints of the Doppler frequency shift parameter. The objective function f(x1, x2, ..., x D ), which is used to characterize the error performance of parameter estimation.

[0129] In each generation iteration, the following operations are performed:

[0130] For each target individual x i , x i for x1,x2,...,x D One of the three individuals x is randomly selected. r1 、x r2 、x r3 , generate mutation vector v i :v i =x r1 +F·(x r2 -x r3 ), for the mutation vector v i and target individual x i Perform crossover to generate the test vector u i Among them, x r1 、x r2 、x r3 It can be understood as three different individuals x randomly selected from the population that are different from the current target individual x i individuals.

[0131] The crossover rules are:

[0132]

[0133] Where j = j rand is the randomly selected dimension index; u i,j represents the j-th dimension component of the i-th trial vector; v i,j represents the j-th dimension component of the i-th mutation vector; x i,j Represents the j-th dimension component of the i-th target individual.

[0134] Comparison test vector u i and target individual x i The fitness value of the population is retained, and the better individuals are retained to enter the next generation population. When the maximum number of iterations T' is reached or the fitness value converges to the preset threshold, the iteration is stopped and the optimal estimated parameter, i.e., the Doppler shift, is output.

[0135] See also Figure 3 , Figure 3 1 is a flow chart of Algorithm 1 for estimating the Doppler shift of a UAV target using a first estimator according to an embodiment of the present invention. First, the number of individuals M, dimension D, mutation operator F, crossover operator Cr, and maximum evolutionary number T' of the differential evolution algorithm are input, and the evolutionary number is initialized to 1.

[0136] Then initialize the population Randomly generate an initial population of M' individuals, where represents the global minimum value of the j-th dimension under physical constraints; Represents the global maximum value of the j-th dimension under physical constraints.

[0137] Next, the main loop process is executed. When (t'≤T'), the mutation and crossover operations are performed on each individual to generate the test vector. The details are as described above and will not be repeated here. Where f(Δ) represents the fitness value; the subscript t' represents the parameter in the t'th evolution, such as u i,t' represents the trial vector in the t'th evolution.

[0138] Compare and calculate the fitness of the test vector f(u i,t' ) and the original individual fitness f(x i,t' ), if -f(u i,t' )<-f(x i,t' ), then use u i,t' Replace x i,t' ; Update the global optimal solution, if the new individual x i,t' The fitness is better than the current fitness f(Δ), that is, -f(xi,t' )<-f(Δ), then use the new individual to update the current optimal solution until the evolutionary generation reaches the maximum evolutionary generation, and output the final optimal solution, and get

[0139] Step S103, using a second estimator to estimate the micro-Doppler shift of the UAV target; the second estimator is designed based on the PSO algorithm; the micro-Doppler shift is determined based on the rotation speed and initial phase of the UAV rotor part.

[0140] In this embodiment of the present invention, a second estimator designed based on the Particle Swarm Optimization (PSO) algorithm is used to estimate the micro-Doppler shift of the drone target. The PSO algorithm is a swarm intelligence optimization algorithm that can approximate solutions to optimization problems with complex objective functions. It optimizes individual fitness as the optimization goal and has strong global search capabilities within the search space, avoiding local optimal solutions.

[0141] In an embodiment of the present invention, estimating the micro-Doppler shift of the UAV target using the second estimator includes:

[0142] Construct a micro-Doppler frequency shift optimization model;

[0143] The micro-Doppler frequency shift of the UAV target is obtained by optimizing the second estimator based on the micro-Doppler frequency shift optimization model through the PSO algorithm; wherein, the optimization process aims to maximize the detection statistic of the rotor part of the UAV.

[0144] In the scenario considered, the micro-Doppler shift By f r and Together, we determine that the micro-Doppler frequency shift optimization model can be expressed as:

[0145]

[0146] in, It can be understood as the detection statistic of the rotor part of the drone.

[0147] In the embodiment of the present invention, p id,pbest represents the optimal solution obtained by searching the d-th dimension of the i-th particle, p d,gbest It represents the optimal solution obtained by searching the dth dimension in the entire particle group. There are two dimensions in total. r and Solve for the optimal value, where the particle can be understood as solving the optimal f r and The optimal solution is the final solution f r and

[0148] In this embodiment of the present invention, the process of estimating the micro-Doppler shift of the UAV target using the second estimator is as follows:

[0149] Set the particle swarm size N P , dimension D = 2, randomly initialize particle velocity v id , randomly initialize the particle position τ id , the maximum number of iterations η loop , individual learning factor ζ1, group learning factor ζ2, inertia weight w. Randomly initialize the position τ of each particle i and speed v i , and record the individual's historical optimal position p id,pbest and the historical optimal position p of the group d,gbest .

[0150] The fitness value of each particle is evaluated according to the objective function. In each iteration, the particle velocity is updated first:

[0151] v id ←wv id +ζ1r i,1 (p id,pbest -τ id )+ζ2r i,2 (p d,gbest -τ id );

[0152] Among them, r i,1 and r i,2 A uniform random number between 0 and 1 is used to update the velocity of the i-th particle to prevent the algorithm from falling into a local optimum.

[0153] Update the particle position again:

[0154] τ id ←τ id +v id ;

[0155] If the current fitness value is better than the individual's historical optimal value, update p id,pbest ; If it is better than the historical optimal value of the group, update p d,gbest When the maximum number of iterations η is reached loop When , the optimal estimated parameter is output, that is, the micro-Doppler frequency shift

[0156] See also Figure 4 , Figure 4 This is a flow chart of Algorithm 2 for estimating the micro-Doppler frequency shift of a drone target using a second estimator provided by an embodiment of the present invention. First, the particle swarm size N is set. P , dimension D = 2, randomly initialize particle velocity vid , randomly initialize the particle position τ id , the maximum number of iterations η loop , individual learning factor ζ1, group learning factor ζ2, inertia weight w. Randomly initialize the position τ of each particle i and speed v i , and record the individual's historical optimal position p id,pbest and the historical optimal position p of the group d,gbest .

[0157] The following steps are executed repeatedly until the maximum number of iterations η is reached. loop :

[0158] The speed and position of each particle i are updated. The specific steps are as described above and will not be repeated here.

[0159] If the current fitness value λ(τ id ) is better than the individual historical optimal value λ(p id,pbest ), then use τ id Update p id,pbest ; If it is better than the historical optimal value of the group, use Update p d,gbest .

[0160] When the maximum number of iterations η is reached loop When the output micro-Doppler frequency shift

[0161] In an embodiment of the present invention, a GLRT detector is used to detect drone targets by detecting echo signals. This effectively enables drone target detection in low signal-to-noise ratio or complex clutter environments, improving robustness. Upon detecting a drone target, a first estimator designed based on the DE algorithm efficiently and accurately estimates the drone target's Doppler shift. A second estimator designed based on the PSO algorithm improves the convergence rate of the process for calculating the drone target's micro-Doppler shift. The collaborative operation of the GLRT detector, the first estimator, and the second estimator effectively improves drone target detection performance.

[0162] The simulation experiment of a UAV target detection and micro-Doppler parameter estimation method provided by an embodiment of the present invention is as follows:

[0163] First, set the simulation parameters:

[0164] The number of nodes in the distributed radar system is The radar positions are Θ1 = [0,0,0] T m、Θ2=[40,40,0] T m、Θ3=[80,80,0] Tm、Θ4=[120,120,0] T m、Θ5=[160,160,0] T m and Θ6 = [200, 200, 0] T m, there is a moving drone target, see Figure 5 , Figure 5 This is a schematic diagram of the simulation scenario of each node and target location of the distributed radar system. The radar location is marked with a red triangle in the simulation scenario diagram, and the green arrow indicates the direction of the drone's movement. The basic parameters of the simulation data are shown in Table 1:

[0165] Table 1 Basic parameters of simulation data

[0166]

[0167]

[0168] Simulation 1: In this simulation experiment, the detection of different numbers of radars and the estimation of the main body parameters are compared. The remaining simulation parameters are shown in Table 1.

[0169] See also Figure 6 , Figure 6 This is a schematic diagram of the UAV target simulation results, which reflects the detection and Doppler parameter estimation performance of the distributed radar. Figure 6 (a) shows the average detection probability diagram of the UAV target observed by the distributed radar. Figure 6 (b) and (c) in the figure are schematic diagrams of the estimated root mean square error (RMSE) curves of distance and speed, respectively. The position and speed parameters of the main part are estimated based on the DE algorithm, as shown in Figure 2. Figure 6 In (a), the output of the visible detector is basically consistent with the simulation settings, such as Figure 6 (b) and Figure 6 (c) in the middle shows the RMSE of the position and velocity of the main part of the drone, respectively. It can be seen that the performance of the detector and estimation will improve with the improvement of the signal-to-noise ratio. When the number of radars increases, the detection and parameter estimation performance will be better.

[0170] Simulation 2, in this simulation experiment Comparative test of different parameter estimation algorithms for the parameter estimation of the UAV rotor part:

[0171] See also Figure 7 , Figure 7 This is a schematic diagram of the simulation results of micro-Doppler parameter estimation based on DE and PSO algorithms, comparing the estimation effects of different estimators. Figure 7(a) and (b) are schematic diagrams of the RMSE curves of the blade speed and blade initial phase on the UAV rotor using different algorithms.

[0172] Based on DE and PSO algorithm, the f r and For estimation, the DE algorithm has better estimation effect under low signal-to-noise ratio, but as the signal-to-noise ratio increases, the PSO algorithm can estimate parameters more accurately and stably. In the simulation experiment, the convergence speed of the DE algorithm is significantly higher than that of the PSO. Overall, the accuracy of the PSO algorithm is higher.

[0173] Simulation 3: In this simulation, a comparative test is conducted on the rotor parameter estimation of UAVs with different numbers of radars:

[0174] See also Figure 8 , Figure 8 This is the simulation result of micro-Doppler parameter estimation, which reflects the estimation accuracy of the PSO estimator in the distributed radar system. Figure 8 Figures (a) and (b) show the RMSE curves for the rotor blade speed and initial phase, respectively, on the UAV rotor. The estimated range velocity from Simulation 1 yields the ath channel, representing the radar's pitch and azimuth angles. The PSO algorithm is then used to estimate the rotor blade speed and initial phase during micro-Doppler frequency shift. The designed PSO-based algorithm significantly reduces computational complexity compared to traditional state traversal algorithms, resulting in faster convergence and improved detection performance. Increasing the number of channels to achieve multi-angle observations can improve system detection performance.

[0175] In this embodiment of the present invention, a distributed radar system is used to detect and estimate multi-rotor drones. Observing targets from multiple angles significantly improves parameter estimation. Unknown parameters, such as Doppler and micro-Doppler parameters, are estimated using a GLRT detector. A multidimensional detection and estimation framework can effectively estimate the rotor speed of a drone and improve the convergence rate of the estimation, effectively enhancing drone detection, tracking, and identification performance.

[0176] The present invention conducts research on the detection task of UAV targets in a distributed radar system, fully utilizing the echo information from different observation angles to improve the accuracy of target detection and parameter estimation, and enhance the overall performance.

[0177] Based on the same inventive concept, the embodiment of the present invention also provides a UAV target detection and micro-Doppler parameter estimation device, see Figure 9 , Figure 9 : This is a schematic diagram of the structure of a UAV target detection and micro-Doppler parameter estimation device provided by an embodiment of the present invention. The UAV target detection and micro-Doppler parameter estimation device includes:

[0178] The detection module 901 is used to detect the UAV target by detecting the echo signal using the GLRT detector;

[0179] A first estimation module 902 is configured to estimate the Doppler shift of a UAV target using a first estimator when the UAV target is detected; the first estimator is designed based on a DE algorithm; and the Doppler shift is determined based on a radar position of a main UAV body, the UAV position, and the UAV speed.

[0180] The second estimation module 903 is used to estimate the micro-Doppler shift of the UAV target using a second estimator; the second estimator is designed based on the PSO algorithm; the micro-Doppler shift is determined based on the rotation speed and initial phase of the UAV rotor part.

[0181] In an embodiment of the present invention, a GLRT detector is used to detect drone targets by detecting echo signals. This effectively enables drone target detection in low signal-to-noise ratio or complex clutter environments, improving robustness. Upon detecting a drone target, a first estimator designed based on the DE algorithm efficiently and accurately estimates the drone target's Doppler shift. A second estimator designed based on the PSO algorithm improves the convergence rate of the process for calculating the drone target's micro-Doppler shift. The collaborative operation of the GLRT detector, the first estimator, and the second estimator effectively improves drone target detection performance.

[0182] Optional detection module, specifically used for:

[0183] Using the GLRT detector, drone target detection is performed by comparing the likelihood ratio between the echo signal and the drone echo pulse signal; the drone echo pulse signal is determined based on the distributed radar system model.

[0184] Optionally, the detection module is further used to:

[0185] A binary hypothesis test for UAV target detection is constructed; in the binary hypothesis test, the H0 hypothesis indicates that no UAV target is detected in the echo signal; the H1 hypothesis indicates that the UAV target is detected in the echo signal; a first probability density function of the UAV target under the H0 hypothesis and a second probability density function under the H1 hypothesis are calculated, and a GLRT decision rule is constructed based on the first probability density function and the second probability density function; a first maximum likelihood estimate of the RCS of the UAV main body part of the UAV target and a second maximum likelihood estimate of the RCS of the rotor part of the UAV target are derived respectively; the first maximum likelihood estimate and the second maximum likelihood estimate are introduced into the GLRT decision rule to obtain a GLRT detector, and UAV target detection is performed based on the GLRT detector.

[0186] Optionally, the drone echo pulse signal includes:

[0187] y a =y a,body +y a,blade +w a ;

[0188] Among them, y a Represents the UAV echo pulse signal; y a,body represents the main part of the drone echo pulse signal received by the a-th radar; y a,blade represents the echo pulse signal of the rotor part of the UAV received by the a-th radar; w a represents the additive noise received in the a-th radar.

[0189] Optionally, the first estimation module is specifically configured to:

[0190] When a UAV target is detected, a Doppler shift optimization model is constructed; a first estimator is used to perform optimization based on the Doppler shift optimization model through a DE algorithm to obtain the Doppler shift of the UAV target; wherein the optimization process aims to maximize the detection statistic of the main part of the UAV.

[0191] Optionally, the second estimation module is specifically configured to:

[0192] A micro-Doppler frequency shift optimization model is constructed; and a second estimator is used to perform optimization through a PSO algorithm based on the micro-Doppler frequency shift optimization model to obtain the micro-Doppler frequency shift of the UAV target; wherein the optimization process aims to maximize the detection statistic of the UAV rotor part.

[0193] The embodiment of the present invention further provides an electronic device, such as Figure 10 As shown, Figure 10 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, including a processor 1001, a communication interface 1002, a memory 1003 and a communication bus 1004, wherein the processor 1001, the communication interface 1002, and the memory 1003 communicate with each other through the communication bus 1004.

[0194] Memory 1003, used for storing computer programs;

[0195] The processor 1001 is configured to implement the steps of any of the above-mentioned UAV target detection and micro-Doppler parameter estimation methods when executing the program stored in the memory 1003 .

[0196] The communication bus mentioned in the electronic device mentioned above may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus.

[0197] The communication interface is used for communication between the above electronic device and other devices.

[0198] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0199] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0200] The present invention also provides a computer-readable storage medium having a computer program stored therein, which, when executed by a processor, implements the method steps of any of the above-mentioned methods for drone target detection and micro-Doppler parameter estimation.

[0201] Optionally, the computer-readable storage medium may be a non-volatile memory (NVM), such as at least one disk memory.

[0202] Optionally, the computer-readable storage medium may also be at least one storage device located away from the processor.

[0203] In another embodiment of the present invention, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the method steps described in any of the above-mentioned methods for drone target detection and micro-Doppler parameter estimation.

[0204] It should be noted that the terms "first," "second," and the like are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0205] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0206] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other variations of the disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the word "comprising" does not exclude other components or steps, "one" or "a" does not exclude multiple situations, and "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0207] The method provided in the embodiments of the present invention can be applied to electronic devices. Specifically, the electronic devices can be desktop computers, portable computers, smart mobile terminals, servers, etc. This is not limited here; any electronic device that can implement the present invention falls within the scope of protection of the present invention.

[0208] As for the device / electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0209] It should be noted that the device, electronic device and storage medium of the embodiments of the present invention are respectively the device, electronic device and storage medium for applying the above-mentioned method for drone target detection and micro-Doppler parameter estimation. All embodiments of the above-mentioned method for drone target detection and micro-Doppler parameter estimation are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0210] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for UAV target detection and micro-Doppler parameter estimation, characterized in that: The UAV target detection and micro-Doppler parameter estimation method includes: Using GLRT detector, drone target detection is performed by detecting echo signals; When a UAV target is detected, a Doppler shift of the UAV target is estimated using a first estimator; the first estimator is designed based on a DE algorithm; the Doppler shift is determined based on a radar position of a main part of the UAV, a position of the UAV, and a speed of the UAV; The micro-Doppler frequency shift of the UAV target is estimated by using a second estimator; the second estimator is designed based on a PSO algorithm; and the micro-Doppler frequency shift is determined based on the rotation speed and initial phase of the UAV rotor part.

2. The method for UAV target detection and micro-Doppler parameter estimation according to claim 1, characterized in that: Using the GLRT detector, drone target detection is performed by detecting echo signals, including: Using the GLRT detector, drone target detection is performed by comparing the likelihood ratio between the echo signal and the drone echo pulse signal; the drone echo pulse signal is determined based on the distributed radar system model.

3. The method for UAV target detection and micro-Doppler parameter estimation according to claim 2, characterized in that: Using the GLRT detector, drone target detection is performed by comparing the likelihood ratio between the echo signal and the drone echo pulse signal, including: Constructing a binary hypothesis test for drone target detection; in the binary hypothesis test, the H0 hypothesis indicates that no drone target is detected in the echo signal; the H1 hypothesis indicates that the drone target is detected in the echo signal; Calculating a first probability density function of the UAV target under the H0 hypothesis and a second probability density function under the H1 hypothesis, and constructing a GLRT decision rule based on the first probability density function and the second probability density function; Derived a first maximum likelihood estimate of the RCS of the drone main body part of the drone target and a second maximum likelihood estimate of the RCS of the rotor part of the drone target; The first maximum likelihood estimate and the second maximum likelihood estimate are brought into the GLRT decision rule to obtain a GLRT detector, and UAV target detection is performed based on the GLRT detector.

4. The method for UAV target detection and micro-Doppler parameter estimation according to claim 2, wherein: The UAV echo pulse signal includes: and a =and a,body +y a,blade +w a ; Among them, y a Represents the UAV echo pulse signal; y a,body represents the main part of the drone echo pulse signal received by the a-th radar; y a,blade represents the echo pulse signal of the rotor part of the UAV received by the a-th radar; w a represents the additive noise received in the a-th radar.

5. The method for UAV target detection and micro-Doppler parameter estimation according to claim 1, wherein: When a UAV target is detected, estimating the Doppler shift of the UAV target by using a first estimator includes: When a drone target is detected, a Doppler shift optimization model is constructed; The first estimator is used to optimize the Doppler frequency shift of the UAV target through the DE algorithm based on the Doppler frequency shift optimization model, wherein the optimization process aims to maximize the detection statistic of the main part of the UAV.

6. The method for UAV target detection and micro-Doppler parameter estimation according to claim 1, wherein: The method of estimating the micro-Doppler shift of the UAV target by using a second estimator includes: Construct a micro-Doppler frequency shift optimization model; The micro-Doppler frequency shift of the UAV target is obtained by optimizing the micro-Doppler frequency shift optimization model using a second estimator through a PSO algorithm. The optimization process aims to maximize the detection statistic of the UAV rotor part.

7. A UAV target detection and micro-Doppler parameter estimation device, characterized in that: The UAV target detection and micro-Doppler parameter estimation device includes: The detection module is used to detect UAV targets by detecting echo signals using the GLRT detector; a first estimation module, configured to estimate a Doppler shift of a UAV target using a first estimator when a UAV target is detected; the first estimator being designed based on a DE algorithm; and the Doppler shift being determined based on a radar position of a main UAV body, a UAV position, and a UAV speed; The second estimation module is used to estimate the micro-Doppler frequency shift of the UAV target using a second estimator; the second estimator is designed based on the PSO algorithm; the micro-Doppler frequency shift is determined based on the rotation speed and initial phase of the UAV rotor part.

8. The UAV target detection and micro-Doppler parameter estimation device according to claim 7, characterized in that: Detection module, specifically used for: Using the GLRT detector, drone target detection is performed by comparing the likelihood ratio between the echo signal and the drone echo pulse signal; the drone echo pulse signal is determined based on the distributed radar system model.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; The processor is configured to implement the UAV target detection and micro-Doppler parameter estimation method according to any one of claims 1 to 6 when executing the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for drone target detection and micro-Doppler parameter estimation according to any one of claims 1 to 6 is implemented.

Images