Method and device for quickly realizing long-time coherent accumulation of GPS (Global Positioning System) external radiation source signal

Abstract

The invention discloses a method and a device for quickly realizing long-time coherent accumulation of a GPS (Global Positioning System) external radiation source signal. The method comprises the following steps: acquiring a cross-correlation spectrum, and acquiring distance and Doppler frequency migration; performing parabola sampling on the cross-correlation spectrum to obtain a sampling sequence; constructing an objective function, and performing parabola sampling, frequency compensation and Fourier transform on the cross-correlation spectrum to obtain a peak value and a corresponding speed; using an improved particle swarm optimization algorithm to maximize an objective function value to obtain an estimated equivalent velocity and an estimated equivalent acceleration, and correcting the distance and Doppler frequency migration to obtain a corrected cross-correlation spectrum; fourier transform is carried out on the corrected cross-correlation spectrum along slow time, the corrected cross-correlation spectrum is converted into a distance and Doppler frequency spectrum, peak detection is carried out, and the peak signal-to-noise ratio, the distance difference and the speed are obtained. According to the method, the calculation amount and the operation time can be greatly reduced, the accuracy of the improved particle swarm optimization algorithm for estimating the migration correction parameters is improved, and multiple targets can be detected.

Description

Technical Field

[0001] The invention belongs to the technical field of signal processing, and in particular relates to a method and a device for quickly realizing long-term coherent accumulation of GPS external radiation source signals. Background Art

[0002] The global positioning system has the advantages of global coverage and stable operation. Among them, GPS external radiation source radars are deployed in coastal ports, islands and reefs and other key areas for silent air and sea surveillance. However, the power of GPS satellite transmission signals reaching the ground is too small, and the signal-to-noise ratio (SNR) of the target echo signal is extremely low. It takes a long time of coherent accumulation to make the signal energy exceed the detection threshold. For example, the traditional pulse Doppler processing method first converts the one-dimensional sequence of continuous direct waves and target echo signals into a two-dimensional matrix according to 1ms period equivalent pulses, and then performs cross-correlation operation on the direct waves and target echo equivalent pulse signals along the fast time dimension. Finally, the cross-correlation spectrum is Fourier transformed (FFT) along the slow time dimension to obtain the range-Doppler frequency (RD) spectrum. In this process, the coherent accumulation gain is theoretically proportional to the accumulation time, but as the accumulation time increases, the range and Doppler frequency migration caused by the target movement intensifies, resulting in the dispersion of RD spectrum peak energy, serious loss of accumulation gain, and even the gain decreases after the accumulation time exceeds a certain limit.

[0003] In order to concentrate and maximize the accumulated peak energy, motion correction is essential. Existing methods can be divided into parametric methods and non-parametric methods. Commonly used non-parametric methods are Keystone Transform (KT) and Radon-Fourier Transform (RFT), which can realize blind correction of first-order range motion when the target's prior motion information is unknown, but the second-order range curvature and Doppler frequency migration are not corrected. Parametric methods include Wigner-Ville Distribution (WVD), Lv's Distribution (LVD), Fractional Fourier Transform (FrFT), etc., which regard the slow time series of the cross-correlation spectrum as a linear frequency modulation (LFM) signal, estimate its center frequency and Doppler frequency modulation two parameters, and realize accurate range and Doppler frequency migration correction. Since peak energy maximization is an optimization problem, a swarm intelligence optimization algorithm can be used to estimate the parameters. However, the existing particle swarm optimization (PSO) algorithm can estimate the migration correction parameters, but it has the problems of large computational complexity, long running time, possible convergence to local optimum, and inability to search for weak target parameters in multi-target detection scenarios.

[0004] Therefore, there is an urgent need to provide a method for quickly implementing long-term coherent integration of GPS external radiation source signals to improve the above-mentioned defects. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method and device for quickly implementing long-term coherent accumulation of GPS external radiation source signals. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a method for quickly implementing long-term coherent accumulation of GPS external radiation source signals, comprising:

[0007] Obtain a cross-correlation spectrum, convert the distance difference associated with the cross-correlation spectrum so that the distance difference is related to the initial distance difference, the equivalent velocity and the equivalent acceleration, and obtain the distance and Doppler frequency migration;

[0008] Construct an objective function, input the initial distance difference, equivalent velocity and equivalent acceleration, perform parabolic sampling and frequency compensation on the cross-correlation spectrum, and obtain a sampling sequence; perform Fourier transform on the sampling sequence, and output the peak value of the cross-correlation spectrum and the velocity corresponding to the peak value;

[0009] The improved particle swarm optimization algorithm is used to maximize the objective function value and obtain the estimated equivalent velocity and the estimated equivalent acceleration;

[0010] According to the estimated equivalent velocity and the estimated equivalent acceleration, the distance and Doppler frequency migration are corrected to obtain a corrected cross-correlation spectrum; the corrected cross-correlation spectrum is Fourier transformed along the slow time to be converted into a distance and Doppler frequency spectrum; the distance and Doppler frequency spectrum is peak detected to obtain a peak signal-to-noise ratio, a distance difference and a velocity.

[0011] In a second aspect, the present invention further provides a device for quickly implementing long-term coherent accumulation of GPS external radiation source signals, comprising:

[0012] A data acquisition unit, configured to acquire a cross-correlation spectrum, convert a distance difference associated with the cross-correlation spectrum so that the distance difference is associated with an initial distance difference, an equivalent velocity and an equivalent acceleration, and acquire a distance and a Doppler frequency migration amount;

[0013] A data processing unit is used to construct an objective function, input an initial distance difference, an equivalent velocity and an equivalent acceleration, perform parabolic sampling and frequency compensation on the cross-correlation spectrum, and obtain a sampling sequence; perform Fourier transform on the sampling sequence, and output a peak value of the cross-correlation spectrum and a velocity corresponding to the peak value;

[0014] A data optimization unit, used for maximizing the objective function value by using an improved particle swarm optimization algorithm, to obtain an estimated equivalent velocity and an estimated equivalent acceleration;

[0015] The result acquisition unit is used to correct the distance and Doppler frequency migration according to the estimated equivalent speed and the estimated equivalent acceleration to obtain a corrected cross-correlation spectrum; perform Fourier transform on the corrected cross-correlation spectrum along slow time to convert it into a distance and Doppler frequency spectrum; perform peak detection on the distance and Doppler frequency spectrum to obtain a peak signal-to-noise ratio, a distance difference and a speed.

[0016] Beneficial effects of the present invention:

[0017] The invention provides a method and device for quickly realizing long-term coherent accumulation of GPS external radiation source signals. In the objective function, accurate migration correction of distance and Doppler frequency is realized by performing parabolic sampling and frequency compensation on the cross-correlation spectrum, and then Fourier transform is performed on the one-dimensional sampling sequence, thereby greatly reducing the amount of calculation and the running time. An adaptive weight coefficient and a mutation mechanism are introduced into the improved particle swarm optimization algorithm process to avoid it from falling into the local optimum, thereby improving the accuracy of the improved particle swarm optimization algorithm in estimating the migration correction parameters. A restricted area is set in the objective function, and in the process of repeatedly running the improved particle swarm optimization algorithm, new restricted areas are continuously added according to the searched parameter results, thereby realizing multi-target detection.

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided by an embodiment of the present invention;

[0020] Figure 2 It is a schematic diagram of a method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided by an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of constructing an objective function provided by an embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of an improved particle swarm optimization algorithm provided by an embodiment of the present invention;

[0023] Figure 5 a~5f are schematic diagrams of processing cross-correlation spectrum results using pulse Doppler and PSO+FFT method provided by an embodiment of the present invention;

[0024] Figure 6 a~6f are schematic diagrams of RD spectrum results after PSO+FFT in a multi-target scenario provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0026] The existing PSO migration correction parameter estimation algorithm needs to reconstruct the entire two-dimensional matrix of the cross-correlation spectrum based on the two motion parameters of velocity and acceleration, and then perform Fourier transform along the slow time dimension to output the distance and Doppler frequency spectrum peak energy. The number of calls to the objective function is the product of the number of iterations and the number of particles. Therefore, the existing particle swarm optimization algorithm has a large amount of calculation, a long running time, and is prone to falling into the local optimum, which is an inherent problem. Because all particles have a strong tendency to converge under the guidance of the historical optimal position of the group, inappropriate parameter settings or unreasonable distribution of randomly initialized particles may lead to premature convergence. Since the optimization goal of the PSO algorithm is to maximize the RD spectrum peak energy, in the multi-target detection scenario of GPS external radiation source radar, the PSO algorithm can only search for the motion parameters corresponding to the target with the strongest energy, while ignoring other targets with weaker energy.

[0027] In view of this, the present invention provides a method for quickly realizing long-term coherent accumulation of GPS external radiation source signals. Firstly, in the objective function, accurate migration correction of distance and Doppler frequency is realized by performing parabolic sampling and frequency compensation on the cross-correlation spectrum, and then Fourier transform is performed on the one-dimensional sampling sequence, which greatly reduces the amount of calculation and running time; secondly, adaptive weight coefficients and mutation mechanisms are introduced into the improved particle swarm optimization algorithm process to avoid it from falling into local optimum, thereby improving the accuracy of the algorithm in estimating migration correction parameters; thirdly, a restricted area is set in the objective function, and in the process of repeatedly running the improved particle swarm optimization algorithm, new restricted areas are continuously added according to the searched parameter results to realize multi-target detection.

[0028] See also Figure 1 , Figure 1 This is a flow chart of a method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided by an embodiment of the present invention. Figure 2 1 is a schematic diagram of a method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided by an embodiment of the present invention. The method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided by the present invention comprises:

[0029] S101, obtaining a cross-correlation spectrum, converting a distance difference related to the cross-correlation spectrum so that the distance difference is related to an initial distance difference, an equivalent velocity and an equivalent acceleration, and obtaining a distance and a Doppler frequency migration amount.

[0030] Specifically, in this embodiment, the continuous wave signal emitted by the GPS external radiation source radar is expressed as:

[0031] S t (t)=A·C(t)·D(t)·exp(j2πf L t);

[0032] Where A represents the signal amplitude, C(t) represents the C / A code, which is a pseudo-random spread spectrum sequence with a code rate of 1.023MHz and a period of 1ms. D(t) represents the navigation message data modulated by BPSK, and the carrier frequency f L It is 1575.42MHz.

[0033] According to the continuous wave signal, the continuous direct wave signal and the target echo signal are obtained, and the one-dimensional sequence of the continuous direct wave signal and the target echo signal is converted into a two-dimensional matrix according to the preset period equivalent pulse, and the expressions are:

[0034]

[0035] Among them, S ref (τ,η) represents the two-dimensional matrix corresponding to the continuous direct wave signal, S r(τ,η) represents the two-dimensional matrix corresponding to the target echo signal, τ∈[0,T] represents the fast time, T represents the preset period, which can be 1ms, η∈[0,T D ] represents the slow time, and the value of η is an integer multiple of T, T D represents the total accumulation time, A ref Represents the amplitude of the continuous direct wave signal, A r represents the amplitude of the target echo signal, u(·) represents the continuous wave signal envelope emitted by the GPS external radiation source radar, that is, C(·)D(·), L(η) represents the distance between the receiver of the GPS external radiation source radar and the satellite, R t (η) represents the distance between the satellite and the target, R r (η) represents the distance between the GPS external radiation source radar receiver and the target, c represents the speed of light, and λ represents the wavelength.

[0036] Along the fast time dimension, the two-dimensional matrix corresponding to the continuous direct wave signal and the two-dimensional matrix corresponding to the target echo signal are conjugate multiplied in the frequency domain to realize the cross-correlation operation and obtain the cross-correlation spectrum, which is expressed as follows:

[0037]

[0038] ΔR(η)=R t (η)+R r (η)-L(η);

[0039] in, represents convolution, U ref (f τ ,η) represents the direct wave frequency domain signal, U r * (f τ ,η) represents the conjugate of the target echo frequency domain signal, and the product of the two is inverse Fourier transformed into a time domain cross-correlation spectrum;

[0040]

[0041] Where PC(τ,η) represents the cross-correlation spectrum, CF(·) represents the periodic correlation function of the C / A code of the GPS external emitter radar, and ΔR(η) represents the range difference that varies with slow time.

[0042] In this embodiment, the distance difference related to the cross-correlation spectrum is converted so that the distance difference is related to the initial distance difference, the equivalent velocity and the equivalent acceleration, and the distance and Doppler frequency migration are obtained, including:

[0043] The distance difference that changes with slow time is Taylor expanded at η = 0 and converted into a quadratic polynomial, which is expressed as:

[0044]

[0045] Among them, R0 represents the initial distance difference, v0 represents the equivalent velocity, and a0 represents the equivalent acceleration;

[0046] According to the quadratic polynomial, the distance and Doppler frequency migration are obtained, and the expression is:

[0047]

[0048] Wherein, RM(η) represents the range migration, and DFM(η) represents the Doppler frequency migration.

[0049] S102, constructing an objective function, and inputting an initial distance difference, an equivalent velocity and an equivalent acceleration, performing parabolic sampling and frequency compensation on the cross-correlation spectrum to obtain a sampling sequence; performing Fourier transform on the sampling sequence, and outputting the peak value of the cross-correlation spectrum and the velocity corresponding to the peak value.

[0050] Specifically, see Figure 3 , Figure 3 is a schematic diagram of constructing an objective function provided by an embodiment of the present invention. In this embodiment, the cross-correlation spectrum is parabolically sampled, and each sampling point is multiplied by The sampling sequence s(η) is obtained, and its expression is:

[0051]

[0052] in, represents the estimated initial distance difference, represents the estimated speed, represents the estimated acceleration, c represents the speed of light, λ represents the wavelength, and η represents the slow time;

[0053] Perform Fourier transform on the sampling sequence s(η) and take the modulus to obtain the spectrum amplitude |U(f d )|,f d Indicates the Doppler frequency, Doppler frequency f d The range is fixed at -500~500Hz, and the Doppler frequency unit resolution is 1 / T D . ;

[0054] Take the maximum value of the spectrum amplitude, corresponding to the peak position of the cross-correlation spectrum; convert the frequency corresponding to the peak position of the cross-correlation spectrum into the speed v d =f d λ, and at the same time, obtain the peak value E of the cross-correlation spectrum to construct the objective function f(X). The parameters of the objective function f(X) are Its expression is:

[0055]

[0056] Here, FFT(·) represents Fourier transform.

[0057] In this embodiment, performing parabolic sampling on the cross-correlation spectrum includes:

[0058] For the fast time unit of the cross-correlation spectrum, the data of the mth fast time unit is taken as the sampling data;

[0059] Among them, the data of the mth fast time unit is Rounding, F S Indicates the sampling rate. The fast time unit resolution of the cross-correlation spectrum is 1 / F S ;

[0060] For the slow time unit of the cross-correlation spectrum, η=nT is taken as the sampling data; wherein n represents the pulse sequence number, T represents the preset period, and the sampling sequence length is the pulse number N p =T D / T.

[0061] S103. Using an improved particle swarm optimization algorithm, the objective function value is maximized to obtain an estimated equivalent velocity and an estimated equivalent acceleration.

[0062] Specifically, see Figure 4 , Figure 4 It is a schematic diagram of the improved particle swarm optimization algorithm provided by an embodiment of the present invention. In this embodiment, the basic parameters are first set, the total number of particles n=30, the maximum number of iterations N=150, and the upper and lower bounds of the distance difference-speed-acceleration three-dimensional search space are 0.5~5km, -25~25m / s and -1~1m / s respectively. 2 .

[0063] Randomly initialize the positions of n particles within the preset boundaries and speed i=1,2,…,n represents the particle number, k represents the number of iterations, and the historical optimal position of each particle is Initialize to

[0064] Calculate the objective function of the i-th particle Determine the historical global optimal position of the particle swarm And the objective function of the particle swarm Among them, the objective function of the particle swarm is The expression is:

[0065]

[0066] Calculate the distance between the position of each particle and the historical global optimal position of the particle swarm during the kth iteration And sort the particles from far to near to get the distance order ranking;

[0067] judge Whether it converges, where the convergence condition is:

[0068]

[0069] If it does not converge, continue to update the particle position speed And the historical optimal position of the particle And determine whether the number of iterations k+1 reaches the maximum number of iterations N; if so, calculate the k+1th iteration process, the particle swarm historical global optimal position And the objective function of the particle swarm And output the largest group history global optimal position X gbest and the objective function of the particle swarm f(X gbest ); if not, continue to update the particle's position speed And the historical optimal position of the particle

[0070] If converged, record the historical global optimal position of the particle swarm And the objective function of the particle swarm And select any particle to perform mutation operation; after the mutation operation, continue to update the position of the particle speed And the historical optimal position of the particle And determine whether the number of iterations k+1 reaches the maximum number of iterations N; if so, calculate the k+1th iteration process, the particle swarm historical global optimal position And the objective function of the particle swarm And output the largest group history global optimal position X gbest and the objective function of the particle swarm f(X gbest ); if not, continue to update the particle's position speed And the historical optimal position of the particle

[0071] Furthermore, update the particle position speed And the historical optimal position of the particle include:

[0072] Update the particle's velocity, the expression is:

[0073]

[0074] Among them, c1 and c2 represent acceleration coefficients, r1,r2∈[0,1] represent random numbers, Represents the adaptive weight coefficient, and its expression is:

[0075]

[0076] in, represents the ranking of particle i in the distance order, and the distance The closer, the The smaller the min and ω max The values ​​are 0.4 and 0.9 respectively;

[0077]

[0078] Among them, v max Indicates the upper limit of the particle speed. If it exceeds the limit, Set to -v max or v max , v max Taken as 0.1 times the difference between the upper and lower bounds;

[0079] Update the particle's position:

[0080]

[0081] Update the historical optimal position of the particle:

[0082]

[0083] Furthermore, any particle is selected for mutation operation, including:

[0084] The selection strategy includes: in the early stage of iteration, particles with a distance order ranking less than k·n / N are selected for mutation operation; in the late stage of iteration, particles with a distance order ranking greater than k·n / N are selected for mutation operation;

[0085] The mutation operation includes: changing the position of the selected particle and the best historical position Update to a specific value, speed randomly reset, specific value selected The estimated equivalent speed Updated to the objective function of the particle swarm Output v d , and then add the random perturbation ε as a whole, that is,

[0086] S104. Correct the distance and Doppler frequency migration according to the estimated equivalent velocity and the estimated equivalent acceleration to obtain a corrected cross-correlation spectrum; perform Fourier transform on the corrected cross-correlation spectrum along slow time to convert it into a distance and Doppler frequency spectrum; perform peak detection on the distance and Doppler frequency spectrum to obtain a peak signal-to-noise ratio, a distance difference and a velocity.

[0087] Specifically, in this embodiment, for the cross-correlation spectrum, each equivalent pulse is cyclically shifted to the left by m units along the fast time dimension and multiplied by To correct the distance and Doppler frequency migration, the expression is:

[0088]

[0089] m is Round up.

[0090] Then, Fourier transform (FFT) is performed along the slow time dimension to convert it into RD spectrum. Finally, two-dimensional constant false alarm probability (2D-CFAR) detection is performed on the RD spectrum to output peak SNR, distance and speed.

[0091] In summary, the invention provides a method for quickly implementing the long-term coherent accumulation of GPS external radiation source signals. Compared with the existing particle swarm algorithm process, the invention uses an adaptive weight coefficient ω i , the particle position is X away from the historical optimal position of the group gbest The closer, the i The smaller the value, the more distant the particles are, the stronger their global search capability is, while the closer the particles are, the faster they converge. In order to avoid premature convergence to the local optimum, a mutation mechanism is introduced. In the early stage of iteration, particles at close distances are selected and their positions are updated to Can escape from local optimum.

[0092] By analyzing and comparing the objective function computational complexity of the improved PSO algorithm of the present invention and the traditional PSO algorithm. The traditional PSO algorithm only estimates two motion parameters Motion correction is achieved by cyclic shifting and frequency compensation of the equivalent pulse, which is equivalent to reconstructing the entire two-dimensional matrix of the cross-correlation spectrum, and then converting it into RD spectrum by FFT along the slow time dimension. τ =F S T, pulse number N p =T D / T, then the migration correction calculation complexity of the traditional PSO algorithm is O(N τ N p ), the FFT computational complexity is O(N τ N p logN p ), the total computational complexity is O(N τ Np logN p ). The improved fast PSO algorithm of the present invention estimates three motion parameters The motion correction is achieved by parabolic sampling and frequency compensation of the cross-correlation spectrum, and only FFT is performed on the one-dimensional sampling sequence. The computational complexity of the motion correction of the improved PSO algorithm is O(N p ), the FFT computational complexity is O(N p logN p ), the total computational complexity is O(N p logN p ), then theoretically, the improved PSO algorithm is faster than the traditional PSO algorithm by N τ times.

[0093] Based on the same inventive concept, the present invention further provides a device for quickly implementing long-term coherent accumulation of GPS external radiation source signals, which is used to implement the method for quickly implementing long-term coherent accumulation of GPS external radiation source signals provided in the above embodiment of the present invention. The embodiment of the method is referred to above and will not be repeated here. The device comprises:

[0094] A data acquisition unit, configured to acquire a cross-correlation spectrum, convert a distance difference associated with the cross-correlation spectrum so that the distance difference is associated with an initial distance difference, an equivalent velocity and an equivalent acceleration, and acquire a distance and a Doppler frequency migration amount;

[0095] A data processing unit is used to construct an objective function, input an initial distance difference, an equivalent velocity and an equivalent acceleration, perform parabolic sampling and frequency compensation on the cross-correlation spectrum, and obtain a sampling sequence; perform Fourier transform on the sampling sequence, and output a peak value of the cross-correlation spectrum and a velocity corresponding to the peak value;

[0096] A data optimization unit, used for maximizing the objective function value by using an improved particle swarm optimization algorithm, and obtaining an estimated equivalent velocity and an estimated equivalent acceleration;

[0097] The result acquisition unit is used to correct the distance and Doppler frequency migration according to the estimated equivalent speed and the estimated equivalent acceleration to obtain a corrected cross-correlation spectrum; perform Fourier transform on the corrected cross-correlation spectrum along slow time to convert it into a distance and Doppler frequency spectrum; perform peak detection on the distance and Doppler frequency spectrum to obtain a peak signal-to-noise ratio, a distance difference and a speed.

[0098] In an optional embodiment of the present invention, the effect of the method for quickly implementing the long-term coherent accumulation of GPS external radiation source signals provided in the above embodiment is verified by simulation experiments, specifically:

[0099] Simulation experiment 1

[0100] The scenario of simulation experiment 1 is single target detection. The initial distance difference of the target is 2000.1m, and the equivalent speed is -19.07m / s. Negative values ​​indicate that it is approaching the radar receiver. The equivalent acceleration is 0.045m / s. 2 , set the target echo signal SNR to -40dB, the sampling rate to 20.46MHz, and the total accumulation time to 10s.

[0101] The cross-correlation spectrum is processed using pulse Doppler and PSO+FFT methods respectively. The results are as follows: Figure 5 a~5f and Table 1, the migration correction parameters searched by the PSO algorithm are [2009.0m, -17.665m / s, 0.04685m / s 2 ]. 5a is the original cross-correlation spectrum, 5b is the cross-correlation spectrum after migration correction according to PSO estimation parameters, indicating that PSO estimation parameters are basically correct, and migration correction makes the cross-correlation spectrum peaks basically aligned, 5c is the RD spectrum after pulse Doppler processing, 5d is the RD spectrum after PSO+FFT, indicating that migration correction concentrates the energy of the RD spectrum peak, 5e is the Doppler frequency dimension profile of the RD spectrum after pulse Doppler processing, and 5f is the Doppler frequency dimension profile of the RD spectrum after PSO+FFT, indicating that PSO+FFT can achieve a higher peak SNR. Table 1 shows that the SNR improvement value is 6.5dB.

[0102] Table 1 RD spectrum detection results obtained by pulse Doppler and PSO+FFT

[0103] Peak SNR(dB) Distance(m) Speed ​​(m / s) Pulse Doppler 15.5 1964.8 -18.471 PSO+FFT 22.0 2008.8 -18.547

[0104] The traditional PSO and improved PSO algorithms are used to estimate the migration correction parameters respectively, and the running time of the test program is shown in Table 2. The improved PSO algorithm is nearly 160 times faster than the traditional PSO algorithm.

[0105] Table 2 Program test results of traditional PSO and improved PSO algorithms for migration correction parameter estimation

[0106]

[0107]

[0108] Keep the basic parameters of traditional PSO and improved PSO algorithms consistent, such as the total number of particles, maximum number of iterations, upper and lower bounds of the search space, acceleration coefficient, and maximum speed limit. The traditional PSO algorithm uses a fixed weight coefficient of 0.6, and the improved PSO algorithm introduces an adaptive weight coefficient and mutation mechanism to perform a Monte Carlo experiment on migration correction parameter estimation, and repeat the operation 500 times. If a certain final result f(X gbest) is significantly lower than other results and is considered an error. The statistical accuracy is shown in Table 3, which shows that the adaptive weight coefficient and mutation mechanism proposed in the present invention can effectively prevent the PSO algorithm from falling into the local optimum and improve the accuracy by 3.4%.

[0109] Table 3 Statistical results of the accuracy of migration correction parameter estimation by traditional PSO and improved PSO algorithms

[0110] Correct number of times Number of errors Correct rate (%) Error rate (%) Traditional PSO 474 26 94.8 5.2 Improved PSO 491 9 98.2 1.8

[0111] Simulation Experiment 2

[0112] The scenario of simulation experiment 2 is multi-target detection. The motion parameters of the three targets are shown in Table 4. The closer the distance, the stronger the target echo signal energy. The target echo signal SNR is set to -40dB, the sampling rate is 20.46MHz, and the total accumulation time is 10s.

[0113] Table 4 Motion parameters of the three targets

[0114] Initial distance difference (m) Equivalent speed (m / s) <![CDATA[Equivalent acceleration (m / s 2 )]]> Goal 1 1375.1 -8.57 0.075 Goal 2 1541.7 -22.61 0.119 Goal 3 2000.1 -19.07 0.045

[0115] The migration correction parameters first searched by the PSO algorithm are [1385.4m, -8.323m / s, 0.07371m / s 2 ], the RD spectrum results of PSO+FFT are as follows Figure 6 6a is an overview, 6b is a local zoom-in at the position of target 1. Repeated PSO can only obtain the motion parameters of target 1 with the strongest energy, and the energy of the other two targets on the RD spectrum is still scattered. Therefore, a restricted area is set in the objective function. If the input motion parameter is within the neighborhood of the motion parameter of the searched strong target, it will directly output 0 to ensure that the two PSOs will not search the same area.

[0116] If the first PSO search parameter result is [1385.4m, -8.323m / s, 0.07371m / s 2 ], the restricted area for the second PSO search is set to [1385±300m, 0.0737±0.04m / s 2 ], the motion parameters of target 2 can be searched [1550.6m, -21.52m / s, 0.1396m / s 2 ], after the motion correction, the new RD spectrum is obtained by FFT. Figure 6c is an overview of the RD spectrum, and Figure 6d is a local enlargement of the position of target 2.

[0117] Similarly, the restricted area for the third PSO search is set to [1385±300m, 0.0737±0.04m / s 2 ] and [1550±300m, 0.1396±0.04m / s 2], we can search for the motion parameters of target 3 [2008.4m, -17.622m / s, 0.0481m / s 2 ], 6e is an overview of the RD spectrum, and 6f is a local zoom-in at the position of target 3. The multi-target RD spectrum detection results after PSO+FFT are shown in Table 5.

[0118] Table 5 Multi-target RD spectrum detection results after PSO+FFT

[0119] Peak SNR(dB) Distance(m) Speed ​​(m / s) Goal 1 20.6 1378.3 -8.569 Goal 2 19.0 1554.3 -22.661 Goal 3 17.2 2008.8 -19.081

[0120] In short, if there are several targets, PSO needs to be run repeatedly several times, and the restricted area range needs to be continuously added until all targets are detected.

[0121] In summary, the present invention has the following beneficial effects:

[0122] The improved PSO algorithm proposed in the present invention greatly reduces the amount of calculation and running time of migration correction in the objective function. The embodiment proves that the improved PSO algorithm reduces the running time from about 50 minutes to less than 20 seconds, and the running speed is increased by about 160 times.

[0123] The present invention introduces an adaptive weight coefficient and a mutation mechanism into the PSO algorithm process, which can effectively prevent the PSO algorithm from falling into a local optimum. Monte Carlo experiments prove that the convergence accuracy of the improved PSO algorithm is increased by 3.4%.

[0124] The present invention sets a restricted zone in the objective function to achieve multi-target detection. The embodiment proves that the motion parameters of three targets can be searched by repeating the improved PSO algorithm three times and updating the restricted zone twice, and the peak energy at the corresponding position on the RD spectrum is concentrated.

[0125] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant is intended to cover non-exclusive inclusion, so that the article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the article or device including the elements. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The orientation or position relationship indicated by "up", "down", "left", "right", etc. is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0126] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0127] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for quickly implementing long-term coherent integration of GPS external radiation source signals, characterized in that: include: Acquire a cross-correlation spectrum, convert a distance difference associated with the cross-correlation spectrum so that the distance difference is associated with an initial distance difference, an equivalent velocity, and an equivalent acceleration, and acquire a distance and Doppler frequency migration amount; Constructing an objective function, and inputting the initial distance difference, equivalent velocity and equivalent acceleration, performing parabolic sampling and frequency compensation on the cross-correlation spectrum to obtain a sampling sequence; performing Fourier transform on the sampling sequence, and outputting the peak value of the cross-correlation spectrum and the velocity corresponding to the peak value; The improved particle swarm optimization algorithm is used to maximize the objective function value and obtain the estimated equivalent velocity and the estimated equivalent acceleration; According to the estimated equivalent velocity and the estimated equivalent acceleration, the distance and Doppler frequency migration are corrected to obtain a corrected cross-correlation spectrum; the corrected cross-correlation spectrum is Fourier transformed along the slow time to be converted into a distance and Doppler frequency spectrum; the distance and Doppler frequency spectrum is peak detected to obtain a peak signal-to-noise ratio, a distance difference and a velocity.

2. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 1, characterized in that: Obtain cross-correlation spectra, including: A continuous direct wave signal and a target echo signal are obtained, and the one-dimensional sequences of the continuous direct wave signal and the target echo signal are converted into two-dimensional matrices according to a preset period equivalent pulse, and the expressions thereof are: Among them, S ref (τ,η) represents the two-dimensional matrix corresponding to the continuous direct wave signal, S r (τ,η) represents the two-dimensional matrix corresponding to the target echo signal, τ∈[0,T] represents the fast time, T represents the preset period, η∈[0,T D ] represents the slow time, and the value of η is an integer multiple of T, T D represents the total accumulation time, A ref Represents the amplitude of the continuous direct wave signal, A r represents the amplitude of the target echo signal, u(·) represents the continuous wave signal envelope emitted by the GPS external radiation source radar, L(η) represents the distance between the receiver of the GPS external radiation source radar and the satellite, R t (η) represents the distance between the satellite and the target, R r (η) represents the distance between the GPS external radiation source radar receiver and the target, c represents the speed of light, and λ represents the wavelength; Along the fast time dimension, the two-dimensional matrix corresponding to the continuous direct wave signal and the two-dimensional matrix corresponding to the target echo signal are conjugate multiplied in the frequency domain to implement cross-correlation operation and obtain the cross-correlation spectrum, which is expressed as follows: ΔR(η)=R t (h)+R r (n)-L(n); in, represents convolution, U ref (f τ ,η) represents the direct wave frequency domain signal, represents the conjugate of the target echo frequency domain signal, and the product of the two is inverse Fourier transformed into a time domain cross-correlation spectrum, and ΔR(η) represents the distance difference that changes with slow time; Where PC(τ,η) represents the cross-correlation spectrum, and CF(·) represents the periodic correlation function of the C / A code of the GPS external emitter radar.

3. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 2, characterized in that: Converting the distance difference associated with the cross-correlation spectrum so that the distance difference is associated with the initial distance difference, the equivalent velocity and the equivalent acceleration, and obtaining the distance and Doppler frequency migration, including: The distance difference that changes with slow time is Taylor expanded at η = 0 and converted into a quadratic polynomial, which is expressed as: Among them, R0 represents the initial distance difference, v0 represents the equivalent velocity, and a0 represents the equivalent acceleration; According to the quadratic polynomial, the distance and Doppler frequency migration are obtained, and the expression is: Wherein, RM(η) represents the range migration, and DFM(η) represents the Doppler frequency migration.

4. The method for rapidly implementing long-term coherent accumulation of GPS external radiation source signals according to claim 1, characterized in that: Constructing an objective function, and inputting the initial distance difference, equivalent velocity and equivalent acceleration, performing parabolic sampling and frequency compensation on the cross-correlation spectrum, and obtaining a sampling sequence; Performing Fourier transform on the sampling sequence and outputting the peak value of the cross-correlation spectrum and the speed corresponding to the peak value, including: Perform parabolic sampling on the cross-correlation spectrum and multiply each sampling point by The sampling sequence s(η) is obtained, and its expression is: in, represents the estimated initial distance difference, represents the estimated speed, represents the estimated acceleration, c represents the speed of light, λ represents the wavelength, and η represents the slow time; The sampling sequence s(η) is subjected to Fourier transform and modulo to obtain the spectrum amplitude |U(f d )|,f d represents the Doppler frequency; Take the maximum value of the spectrum amplitude, corresponding to the peak position of the cross-correlation spectrum; convert the frequency corresponding to the peak position of the cross-correlation spectrum into the speed v d =f d λ, and at the same time, obtain the peak value E of the cross-correlation spectrum to construct the objective function f(X), the parameters of the objective function f(X) are Its expression is: Here, FFT(·) represents Fourier transform.

5. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 4, characterized in that: Performing parabolic sampling on the cross-correlation spectrum includes: For the fast time unit of the cross-correlation spectrum, taking the data of the mth fast time unit as sampling data; Among them, the data of the mth fast time unit is Rounding, F S Indicates the sampling rate; For the slow time unit of the cross-correlation spectrum, η=nT is taken as sampling data; wherein n represents the pulse sequence number, and T represents the preset period.

6. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 1, characterized in that: The improved particle swarm optimization algorithm is used to maximize the objective function value and obtain the estimated equivalent velocity and estimated equivalent acceleration, including: Randomly initialize the positions of n particles within the preset boundaries and speed i=1,2,…,n represents the particle number, and the historical optimal position of each particle is Initialize to Calculate the objective function of the i-th particle Determine the historical global optimal position of the particle swarm And the objective function of the particle swarm Among them, the objective function of the particle swarm is The expression is: Calculate the distance between the position of each particle and the historical global optimal position of the particle swarm during the kth iteration And sort the particles from far to near to get the distance order ranking; judge Whether it converges, where the convergence condition is: If it does not converge, continue to update the particle position speed And the historical optimal position of the particle And determine whether the number of iterations k+1 reaches the maximum number of iterations N; if so, calculate the k+1th iteration process, the particle swarm historical global optimal position And the objective function of the particle swarm And output the largest group history global optimal position X gbest and the objective function of the particle swarm f(X gbest ); if not, continue to update the particle's position speed And the historical optimal position of the particle If converged, record the historical global optimal position of the particle swarm And the objective function of the particle swarm And select any particle to perform mutation operation; after the mutation operation, continue to update the position of the particle speed And the historical optimal position of the particle And determine whether the number of iterations k+1 reaches the maximum number of iterations N; if so, calculate the k+1th iteration process, the particle swarm historical global optimal position And the objective function of the particle swarm And output the largest group history global optimal position X gbest and the objective function of the particle swarm f(X gbest ); if not, continue to update the particle's position speed And the historical optimal position of the particle 7. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 6, characterized in that: Update the particle's position speed And the historical optimal position of the particle include: Update the particle's velocity, the expression is: Among them, c1 and c2 represent acceleration coefficients, r1,r2∈[0,1] represent random numbers, Represents the adaptive weight coefficient, and its expression is: in, represents the ranking of particle i in the distance order; Among them, v max Indicates the upper limit of the particle speed. If it exceeds the limit, Set to -v max or v max ; Update the particle's position: Update the historical optimal position of the particle:

8. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 6, characterized in that: Select any particle to perform mutation operations, including: The selection strategy includes: in the early stage of iteration, particles with a distance order ranking less than k·n / N are selected for mutation operation; in the late stage of iteration, particles with a distance order ranking greater than k·n / N are selected for mutation operation; The mutation operation includes: changing the position of the selected particle and the best historical position Update to a specific value, speed randomly reset, specific value selected The estimated equivalent speed Updated to the objective function of the particle swarm Output v d , and then add the random perturbation ε as a whole, that is, 9. The method for rapidly implementing long-term coherent integration of GPS external radiation source signals according to claim 1, characterized in that: According to the estimated equivalent velocity and the estimated equivalent acceleration, the distance and the Doppler frequency migration are corrected to obtain a corrected cross-correlation spectrum, including: For the cross-correlation spectrum, each equivalent pulse is cyclically shifted leftward by m units along the fast time dimension and multiplied by To correct the distance and Doppler frequency migration, the expression is: m is Round up.

10. A device for quickly implementing long-term coherent accumulation of GPS external radiation source signals, characterized in that: include: A data acquisition unit, configured to acquire a cross-correlation spectrum, convert a distance difference associated with the cross-correlation spectrum so that the distance difference is associated with an initial distance difference, an equivalent velocity and an equivalent acceleration, and acquire a distance and a Doppler frequency migration amount; A data processing unit is used to construct an objective function, input the initial distance difference, equivalent velocity and equivalent acceleration, perform parabolic sampling and frequency compensation on the cross-correlation spectrum to obtain a sampling sequence; perform Fourier transform on the sampling sequence, and output the peak value of the cross-correlation spectrum and the velocity corresponding to the peak value; A data optimization unit, used for maximizing the objective function value by using an improved particle swarm optimization algorithm, and obtaining an estimated equivalent velocity and an estimated equivalent acceleration; The result acquisition unit is used to correct the distance and Doppler frequency migration according to the estimated equivalent speed and the estimated equivalent acceleration to obtain a corrected cross-correlation spectrum; perform Fourier transform on the corrected cross-correlation spectrum along slow time to convert it into a distance and Doppler frequency spectrum; perform peak detection on the distance and Doppler frequency spectrum to obtain a peak signal-to-noise ratio, a distance difference and a speed.

Images