GPS external radiation source target echo efficient accumulation method based on PSO and LVD

Abstract

The invention discloses a GPS external radiation source target echo efficient accumulation method based on PSO and LVD. The method comprises the following steps: acquiring an echo signal, and performing cross-correlation processing on the echo signal to obtain a pulse compression signal; according to the pulse compression signal, estimating the speed and acceleration of target motion by using a PSO algorithm to obtain the estimated speed and acceleration; performing first-order range migration, second-order range bending and Doppler frequency migration correction on the pulse compression signal according to the estimated speed and acceleration to obtain a corrected pulse compression signal; and performing coherent accumulation on the corrected pulse compression signal by using LVD to obtain a target detection result. According to the invention, a higher signal-to-noise ratio can be obtained, and the gain is high.

Description

Technical Field

[0001] The invention belongs to the field of radar technology, and in particular relates to a method for efficiently accumulating GPS external radiation source target echoes based on PSO and LVD. Background Art

[0002] Since navigation satellites are located outside the atmosphere, using navigation satellites as external radiation sources means that the radar receives relatively less clutter, which is conducive to target detection. In addition, navigation satellites have the obvious advantages of global coverage and safe and stable operation. Taking the United States' Global Positioning System (GPS) as an example, its satellite constellation consists of more than 20 satellites, which can ensure that any location on the surface is covered by at least 4 satellites. In addition, it also provides timing services to facilitate time synchronization. Therefore, GPS-based external radiation source radars are particularly suitable for silent surveillance of key coastal areas, and typical targets are large low-altitude penetration fighters and surface ships.

[0003] GPS external emitter radar also has defects. The main defect is that the power of the satellite signal reaching the ground is too small, resulting in the target echo signal often being submerged in noise and multipath clutter, and the signal-to-noise ratio and signal-to-clutter ratio are low. This makes the GPS-based external emitter radar require a longer (tens of seconds) accumulation time to detect the target compared to the active radar. During the long-term accumulation process, the movement of the target will cause the echo signal to migrate across the range unit and across the Doppler unit, causing the echo energy to disperse and affecting the radar's detection performance. Therefore, studying the long-term accumulation technology when there are range migration and Doppler frequency migration problems is of great significance to improving the detection power of GPS external emitter radar.

[0004] Long-term integration algorithms are mainly divided into two categories: coherent integration and incoherent integration, depending on whether the phase information of the echo signal is retained. Due to the loss of useful echo phase information, the gain of the incoherent integration algorithm is low, while the coherent integration algorithm has better detection performance due to the compensation of phase differences.

[0005] The main problems to be solved in the coherent integration algorithm are range migration and Doppler frequency migration. For range migration, the common processing method is to perform keystone transformation, which can perform first-order range migration correction under the condition of unknown speed. For Doppler frequency migration, the usual practice is to transform the signal into the frequency domain and use time-frequency analysis tools to estimate and detect signal parameters. After the range migration and Doppler frequency migration correction, the slow time-dimensional FFT method is generally used to achieve coherent integration.

[0006] When performing long-term multi-pulse accumulation, target motion will cause range migration and Doppler frequency migration problems, where range migration can be divided into first-order range migration and second-order range bending. Usually, the traditional keystone transform only corrects the first-order range migration, and does not correct the second-order range bending. When the keystone transform is used to correct the first-order range migration and FFT is used to perform coherent accumulation in the slow time dimension, the second-order range bending and Doppler frequency migration will cause the signal energy to disperse on the range-Doppler spectrum (RD spectrum), affecting the detection performance of the radar. Compared with FFT, LVD is only affected by the first-order range migration and the second-order range bending, but not by the Doppler frequency migration. However, the resolution of the frequency modulation dimension of LVD in the CFCR domain is relatively low, and the detection accuracy of the target motion acceleration is poor. When the frequency modulation corresponding to the acceleration of the target motion is in the center range of the frequency modulation resolution unit, the problem of peak drop will occur. In short, the existing coherent integration algorithm will have the problem of gain drop when the accumulation time becomes longer. Summary of the invention

[0007] In order to solve the above problems existing in the prior art, the present invention provides a method for efficiently accumulating GPS external radiation source target echoes based on PSO and LVD.

[0008] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0009] The present invention provides a method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD, comprising:

[0010] Acquiring an echo signal, and performing cross-correlation processing on the echo signal to obtain a pulse compression signal;

[0011] According to the pulse compression signal, the velocity and acceleration of the target motion are estimated by using the PSO algorithm to obtain the estimated velocity and acceleration;

[0012] According to the estimated speed and acceleration, the pulse compression signal is corrected for first-order range migration, second-order range bending, and Doppler frequency migration to obtain a corrected pulse compression signal;

[0013] The corrected pulse compression signal is coherently accumulated by using LVD to obtain a target detection result.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The present invention uses a PSO algorithm to make an accurate estimate of the initial velocity and acceleration of the target motion, and then uses the obtained velocity and acceleration parameters to perform first-order range migration and second-order range bending correction on the cross-correlation spectrum data, so that the signal energy can be concentrated in the CFCR domain of the LVD, and uses the obtained acceleration parameters to correct multiple pairs of Pulser frequency migrations, so that the peak value of the LVD in the CFCR domain appears at the zero frequency position of the frequency modulation dimension, which effectively alleviates the problem of LVD peak drop, thereby obtaining a higher signal-to-noise ratio and high gain.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD provided by an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of the construction principle of the objective function provided by an embodiment of the present invention;

[0019] Figure 3 is a flow chart of the LVD algorithm provided by an embodiment of the present invention;

[0020] Figure 4 It is the result of performing KT+FFT on the cross-correlation spectra of different time lengths at a signal-to-noise ratio of -40 dB provided in an embodiment of the present invention;

[0021] Figure 5 It is the result of performing KT+LVD on sequences of different time lengths at a signal-to-noise ratio of -40 dB provided by an embodiment of the present invention;

[0022] Figure 6 This is the result of performing PSO+LVD on sequences of different time lengths at a signal-to-noise ratio of -40 dB provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] Figure 1 FIG. 1 is a flow chart of a method for efficiently accumulating GPS external radiation source target echoes based on PSO and LVD provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0025] S101, acquiring an echo signal, and performing cross-correlation processing on the echo signal to obtain a pulse compression signal.

[0026] S102 , estimating the speed and acceleration of the target motion by using the PSO algorithm according to the pulse compression signal to obtain estimated speed and acceleration.

[0027] S103 , performing first-order range migration, second-order range bending and Doppler frequency migration corrections on the pulse compression signal according to the estimated velocity and acceleration, to obtain a corrected pulse compression signal.

[0028] S104, using LVD to perform coherent accumulation on the corrected pulse compression signal to obtain a target detection result.

[0029] For the above S101, the reference signal is used to perform cross-correlation processing on the echo signal, so as to obtain a pulse compression signal (also called a cross-correlation spectrum).

[0030] For example, the reference signal S ref (τ,η) and the echo signal S r The expressions of (τ,η) are as follows:

[0031]

[0032] Among them, A ref is the amplitude of the reference signal, A r is the amplitude of the echo signal, u is the envelope of the signal, c is the speed of light, λ is the wavelength, τ∈[0,T] is the fast time, T is the pulse repetition period, η∈[0,T D ] is the slow time and its value is an integer multiple of T, T D is the pulse accumulation time, L(η) is the distance from the receiver to the satellite, R t (η) is the distance from the satellite to the target, R r (η) is the distance from the receiver to the target.

[0033] Exemplarily, the reference signal S is used ref (τ,η) for the echo signal S r After cross-correlation processing of (τ,η), the expression of the pulse compression signal PC(τ,η) is as follows:

[0034]

[0035] Among them, A PC is the amplitude of the pulse compression signal, CF is the periodic correlation function, ΔR(0) is the initial distance difference between the receiver and the target, ΔR(η) is the distance difference between the receiver and the target at the slow time η, v0 represents the true speed of the target movement, and a0 represents the true acceleration of the target movement.

[0036] Here, the above S102 can be implemented by the following steps:

[0037] S1021. Determine, based on the estimated target motion speed and acceleration, an expression for the number of units translated in the opposite direction of range migration along the fast time dimension when performing range migration correction on the pulse compression signal.

[0038] Here, the range migration can be corrected by performing translation envelope alignment on the pulse compression signal. Exemplarily, the expression of the number of units is: F S is the fast time sampling frequency, represents the speed of the target motion used in the process of estimating v0, Represents the acceleration of the target motion used in the estimation of a0.

[0039] S1022. According to the expression of the number of units, determine the expression of the signal obtained after the pulse compression signal is corrected for range migration.

[0040] S1023: Construct a Doppler frequency migration compensation function according to the acceleration of the target motion that needs to be estimated.

[0041] Exemplarily, the expression of the Doppler frequency migration compensation function is:

[0042]

[0043] S1024. According to the expression of the signal obtained after performing range migration correction on the pulse compression signal and the Doppler frequency migration compensation function, determine the expression of the corrected pulse compression signal obtained after performing first-order range migration, second-order range bending and Doppler frequency migration correction on the pulse compression signal.

[0044] Here, the expression of the signal obtained after the pulse compression signal is corrected for range migration can be multiplied by the Doppler frequency migration compensation function to obtain the expression of the corrected pulse compression signal. Exemplarily, the expression of the corrected pulse compression signal is as follows:

[0045]

[0046] Among them, η∈[0,T D ] is the slow time and its value is an integer multiple of T, τ∈[0,T] is the fast time, T is the pulse repetition period, T D is the pulse accumulation time, ΔR(0) is the initial distance difference between the receiver and the target, λ is the wavelength, c is the speed of light, and CF is the periodic correlation function.

[0047] S1025. Construct an objective function according to the expression of the corrected pulse compression signal.

[0048] For example, Figure 2 The construction principle diagram of the objective function is as follows: Figure 2 As shown, the corrected pulse compression signal can be FFT-ed along the slow time dimension η to convert the cross-correlation spectrum into an RD spectrum, and the peak energy on the RD spectrum is used as the mapping value of the objective function f(X). and Then the range migration and Doppler frequency migration are completely corrected, the peak energy on the RD spectrum will reach the maximum, and the objective function f(X) will also reach the maximum value. Therefore, the expression of the objective function is as follows: FFT{·} denotes fast Fourier transform of the slow time dimension.

[0049] S1026. According to the objective function, the PSO algorithm is used to estimate the speed and acceleration of the target motion to obtain the estimated speed and acceleration.

[0050] Specifically, S1026 can be implemented through steps S1 to S4:

[0051] S1. Initialize the position vector, velocity vector and historical optimal position vector of n particles to obtain the initial position vector of each particle Initial velocity vector and the initial historical optimal position vector Among them, the position vector of each particle is a two-dimensional parameter composed of the speed and acceleration of the target movement.

[0052] Here, the position vectors, velocity vectors and historical optimal position vectors of the n particles can be initialized randomly.

[0053] S2, in the kth iteration, when k = 1, according to the initial historical optimal position vector of all particles and the objective function, determine the maximum function value of the kth time The k-th historical optimal position vector of the group And the kth historical optimal position vector of each particle The kth iteration is completed, where i is a positive integer and the value of i ranges from 1 to n.

[0054] Specifically, the process of the first iteration is as follows:

[0055] 1) The initial historical optimal position vector of all particles Substitute the objective function f(X) into the objective function respectively, and get n objective function values ​​in total;

[0056] 2) The maximum value among the obtained n objective function values ​​is taken as the maximum function value of the kth time;

[0057] 3) The initial historical optimal position vector corresponding to the k-th maximum function value is used as the k-th group historical optimal position vector

[0058] 4) For each particle, the initial historical optimal position vector of the particle is As the kth historical optimal position vector of the particle

[0059] S3. When performing the kth iteration and k is greater than 1, for each particle, the maximum function value based on the k-1th iteration The k-1th historical optimal position vector of the group And the particle's k-1th historical optimal position vector The k-1th velocity vector of the particle The k-1th position vector and the k-1th historical optimal position vector Update to get the k-th velocity vector of the particle The k-th position vector and the kth historical optimal position vector The kth iteration is completed, where k is a positive integer greater than or equal to 1.

[0060] Specifically, except for the first iteration, the process of each iteration is as follows:

[0061] 1) For each particle, based on the k-1th historical optimal position vector of the group The k-1th historical optimal position vector of the particle The k-1th velocity vector of the particle The k-1th position vector of the particle The inertia weight, acceleration coefficient and preset coefficient generate the k-th velocity vector of the particle.

[0062] For example, the k-th velocity vector of the particle The expression is as follows:

[0063]

[0064] Wherein, ω is the inertia weight, c1 and c2 are the acceleration coefficients, r1 and r2 are the preset coefficients, and r1 and r2 are both random numbers between 0 and 1. The value range is v max Indicates the preset maximum velocity vector.

[0065] 2) According to the k-th velocity vector of the particle and the k-1th position vector of the particle Generate the k-th position vector of the particle

[0066] Exemplarily, the k-th position vector of the particle The expression is:

[0067] 3) Using the objective function, calculate the k-th position vector of the particle The function value of And the k-1th historical optimal position vector of the particle The function value of

[0068] 4) According to the function value With function value The size relationship between them determines the k-th historical optimal position vector of the particle

[0069] For example, the kth historical optimal position vector of the particle is The expression is:

[0070]

[0071] 5) Set the kth historical optimal position vector of all particles Substitute the objective function f(X) into the objective function respectively, and obtain n objective function values ​​in total. The maximum value of the n objective function values ​​is taken as the k-th maximum function value, and the k-th historical optimal position vector corresponding to the k-th maximum function value is taken as the k-th group historical optimal position vector

[0072] S4. Determine whether the result of the kth iteration or k reaches the iteration termination condition. If not, set k=k+1 and continue iterating until the iteration termination condition is finally reached. The historical optimal position vector of the group obtained when the iteration termination condition is reached is used as the estimated speed and acceleration.

[0073] Here, it is possible to determine whether the iteration termination condition is currently reached by judging whether the historical optimal position vectors of the group obtained from several consecutive iterations are the same. For example, when the historical optimal position vectors of the group obtained from several consecutive iterations are the same, it indicates that the iteration termination condition is currently reached. Otherwise, the iteration termination condition is not reached and the iteration needs to be continued until the iteration termination condition is reached. It is also possible to determine whether the iteration termination condition is currently reached by judging whether the number of iterations k is less than or equal to the preset number of iterations N. For example, when k is N, it indicates that the iteration termination condition is currently reached. Otherwise, the iteration termination condition is not reached and the iteration needs to be continued until the iteration termination condition is reached.

[0074] For the above S103, after the speed and acceleration are estimated, the pulse compression signal can be corrected for first-order range migration, second-order range bending and Doppler frequency migration in sequence according to the estimated speed and acceleration, so as to obtain a corrected pulse compression signal.

[0075] For the above S103, specifically, a Doppler frequency migration compensation function is determined according to the estimated speed and acceleration; and according to the Doppler frequency migration compensation function, the Doppler frequency migration correction is performed on the initially corrected signal to obtain a corrected pulse compression signal.

[0076] Here, if Figure 3 As shown, the above S104 can be implemented by the following steps:

[0077] S1041. Select the slow time series of each distance unit from the corrected pulse compression signal.

[0078] Exemplarily, the slow time series PC(η) of each distance unit m ) is as follows:

[0079] v0 represents the real speed of the target motion, that is, the speed of the target motion estimated by the above process, and a0 represents the real acceleration of the target motion, that is, the acceleration of the target motion estimated by the above process.

[0080] S1042, calculating the parameter-containing instantaneous symmetric autocorrelation function of the slow time series of each selected distance unit.

[0081] Exemplarily, the expression of the parameterized instantaneous symmetric autocorrelation function of the slow time series of each distance unit is as follows:

[0082]

[0083] Among them, the delay variable μ∈[-b,T D-b], the delay variable changes at an interval of 2T, b is the preset delay parameter, and is generally set to 1.

[0084] S1043, performing variable substitution on the parameter-containing instantaneous symmetric autocorrelation function of the slow time series of each distance unit to obtain a transformed parameter-containing instantaneous symmetric autocorrelation function.

[0085] Specifically, η can be used k =η m (μ+b) Substitute the variables of the parameter-containing instantaneous symmetric autocorrelation function of the slow time series of each distance unit, and the expression of the transformed parameter-containing instantaneous symmetric autocorrelation function is as follows:

[0086]

[0087] S1044, performing FFT on each transformed parameter-containing instantaneous symmetric autocorrelation function in the time delay dimension to transfer it to the CFCR domain, and obtaining a corresponding LVD result.

[0088] Exemplarily, the expression for each LVD result is as follows:

[0089]

[0090] in, Indicates each LVD result, f c is the center frequency, is the frequency modulation, CF is the period-dependent function, b is the preset delay parameter, and δ represents the impulse function.

[0091] S1045: Merge the obtained LVD results, and select the LVD result with the highest peak value from all the obtained LVD results.

[0092] S1046. Perform target detection according to the LVD result with the highest peak value to obtain a target detection result.

[0093] Specifically, a two-dimensional constant false alarm (2D-CFAR) detection can be performed on the LVD result with the highest peak value. If a target is detected, the estimated speed and acceleration are the speed and acceleration of the target when it is moving, and the distance difference with the target can be determined based on the distance unit corresponding to the LVD result with the highest peak value, thereby achieving detection of the target.

[0094] The effect of the method of the present invention is verified by simulation experiments as follows.

[0095] 1. Simulation conditions

[0096] Set the initial target distance to 2000m, the initial radial velocity of the target relative to the radar to 19m / s, the target motion acceleration to 0.045m / s2, the pulse repetition period to 1ms, the sampling rate to 20.46MHz, the carrier frequency to 1576.42MHz, the signal-to-noise ratio to -40dB, and the total dwell time to 10s.

[0097] 2. Simulation experiment content and result analysis

[0098] Figure 4 These are the results of KT+FFT on the cross-correlation spectra of different durations at a signal-to-noise ratio of -40 dB, where Figure a is the plane RD spectrum of the first 2 seconds (s), Figure c is the plane RD spectrum of the first 5s, and Figure e is the complete 10s plane RD spectrum. Figure 5 These are the results of KT+LVD on sequences of different lengths at a signal-to-noise ratio of -40 dB, where Figure a is the first 2 s plane CFCR spectrum, Figure c is the first 5 s plane CFCR spectrum, and Figure e is the complete 10 s plane CFCR spectrum. Figure 6 These are the results of PSO+LVD on sequences of different lengths at a signal-to-noise ratio of -40 dB, where Figure a is the first 2s planar CFCR spectrum, Figure c is the first 5s planar CFCR spectrum, and Figure e is the complete 10s planar CFCR spectrum. Figure 4 , 5 In 6, the frequency profiles of the first 2s are shown in Figure b, the frequency profiles of the first 5s are shown in Figure d, and the frequency profiles of the complete 10s are shown in Figure f. Table 1 shows the results of 2D-CFAR for sequences of different lengths processed by three methods at a signal-to-noise ratio of -40dB.

[0099] Table 1 2D-CFAR detection results for sequences of different lengths at a signal-to-noise ratio of -40dB

[0100] Duration(s) KT+FFT KT+LVD PSO+LVD 2 17.859dB 21.570dB 22.536dB 5 17.633dB 24.967dB 25.325dB 10 15.729dB 22.304dB 27.453dB

[0101] It can be seen from the simulation results that as the accumulation time increases, the gain of KT+FFT will continue to decrease due to the influence of second-order range bending and Doppler frequency migration. Although LVD is not affected by Doppler frequency migration, when the accumulation time is 10s, the frequency modulation resolution is 0.1Hz / s, and the frequency modulation corresponding to the target acceleration is 0.2365Hz / s. The theoretical peak of LVD is located near the center of the third resolution unit of the frequency modulation dimension of the CFCR domain, and the peak value shows a significant decrease at this time. Under the same conditions, the PSO+LVD method not only has the highest peak-to-noise ratio, but also increases with the extension of the accumulation time. It is an efficient long-term accumulation method.

[0102] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0103] 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.

[0104] In the specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components. Certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good effects.

[0105] 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 efficiently accumulating GPS external radiation source target echo based on PSO and LVD, characterized in that: include: Acquiring an echo signal, and performing cross-correlation processing on the echo signal to obtain a pulse compression signal; According to the pulse compression signal, the velocity and acceleration of the target motion are estimated by using the PSO algorithm to obtain the estimated velocity and acceleration; According to the estimated speed and acceleration, the pulse compression signal is corrected for first-order range migration, second-order range bending, and Doppler frequency migration to obtain a corrected pulse compression signal; The corrected pulse compression signal is coherently accumulated by using LVD to obtain a target detection result.

2. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 1 is characterized in that: The method of estimating the speed and acceleration of the target motion by using the PSO algorithm according to the pulse compression signal to obtain the estimated speed and acceleration includes: According to the speed and acceleration of the target motion to be estimated, determining the expression for the number of units translated in the opposite direction of range migration along the fast time dimension when the pulse compression signal is subjected to range migration correction; Determine, according to the expression of the number of units, an expression of a signal obtained after performing range migration correction on the pulse compression signal; According to the acceleration of the target motion that needs to be estimated, a Doppler frequency migration compensation function is constructed; Determine, based on the expression of the signal obtained after performing range migration correction on the pulse compression signal and the Doppler frequency migration compensation function, the expression of the corrected pulse compression signal obtained after performing first-order range migration, second-order range bending and Doppler frequency migration correction on the pulse compression signal; Constructing an objective function according to the expression of the corrected pulse compression signal; According to the objective function, the velocity and acceleration of the target motion to be estimated are estimated using the PSO algorithm to obtain the estimated velocity and acceleration.

3. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 2 is characterized in that: The expression of the objective function is as follows: Wherein, f(X) represents the objective function, FFT{·} represents fast Fourier transform of the slow time dimension, v0 represents the speed of the target motion to be estimated, a0 represents the acceleration of the target motion to be estimated, represents the speed of the target motion used in the process of estimating v0, represents the acceleration of the target motion used in the process of estimating a0, η∈[0,T D ] is the slow time and its value is an integer multiple of T, τ∈[0,T] is the fast time, T is the pulse repetition period, T D is the pulse accumulation time, ΔR(0) is the initial distance difference between the receiver and the target, λ is the wavelength, c is the speed of light, and CF is the periodic correlation function.

4. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 2 is characterized in that: The method of estimating the speed and acceleration of the target motion to be estimated by using the PSO algorithm according to the objective function to obtain the estimated speed and acceleration includes: Initialize the position vector, velocity vector and historical optimal position vector of n particles to obtain the initial position vector of each particle Initial velocity vector and the initial historical optimal position vector Among them, the position vector of each particle is a two-dimensional parameter composed of the speed and acceleration of the target movement; In the kth iteration, when k = 1, according to the initial historical optimal position vector of all particles and the objective function, determine the maximum function value of the kth time The k-th historical optimal position vector of the group And the kth historical optimal position vector of each particle Complete the kth iteration, where i is a positive integer and the value of i ranges from 1 to n; In the kth iteration, when k is greater than 1, for each particle, the maximum function value based on the k-1th iteration The k-1th historical optimal position vector of the group And the k-1th historical optimal position vector of the particle The k-1th velocity vector of the particle The k-1th position vector and the k-1th historical optimal position vector Update to get the k-th velocity vector of the particle The k-th position vector and the kth historical optimal position vector Based on the kth historical optimal position vector of all particles and the objective function to determine the k-th historical optimal position vector of the group Complete the kth iteration, where k is a positive integer greater than or equal to 1; Determine whether the result of the kth iteration or k reaches the iteration termination condition. If not, set k=k+1 and continue iterating until the iteration termination condition is finally reached. The historical optimal position vector of the group obtained when the iteration termination condition is reached is used as the estimated speed and acceleration.

5. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 4 is characterized in that: In the kth iteration, when k=1, the initial historical optimal position vectors of all particles are and the objective function, determine the maximum function value of the kth time The k-th historical optimal position vector of the group And the kth historical optimal position vector of each particle include: In the kth iteration, when k = 1, the initial historical optimal position vectors of all particles are Substitute into the objective function respectively to obtain n objective function values ​​in total; The maximum value among the n objective function values ​​is taken as the k-th maximum function value; The initial historical optimal position vector corresponding to the k-th maximum function value is used as the k-th group historical optimal position vector For each particle, the initial historical optimal position vector of the particle is As the kth historical optimal position vector of the particle 6. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 2, characterized in that: The expression of the corrected pulse compression signal includes: the velocity v0 and acceleration a0 of the target motion to be estimated, the velocity of the target motion used in the process of estimating v0 The acceleration of the target motion used in the estimation of a0 The constructing of the objective function according to the expression of the corrected pulse compression signal comprises: Performing FFT on the expression of the corrected pulse compression signal along the slow time dimension to convert the cross-correlation spectrum into an RD spectrum; The peak energy on the RD spectrum is used as the mapping value of the objective function to obtain the objective function, where if and Then, both the range migration and the Doppler frequency migration are completely corrected, the peak energy on the RD spectrum will reach the maximum, and the objective function will also reach the maximum value.

7. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 6, characterized in that: The expression for the number of units is as follows: Among them, F S is the fast time sampling frequency.

8. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 1, characterized in that: The method of performing first-order range migration, second-order range bending and Doppler frequency migration correction on the pulse compression signal according to the estimated speed and acceleration to obtain a corrected pulse compression signal comprises: According to the estimated speed and acceleration, the pulse compression signal is subjected to first-order range migration and second-order range bending to obtain an initially corrected signal; determining a Doppler frequency migration compensation function based on the estimated velocity and acceleration; According to the Doppler frequency migration compensation function, the Doppler frequency migration correction is performed on the initially corrected signal to obtain a corrected pulse compression signal.

9. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 1, characterized in that: The method of using the LVD to coherently accumulate the corrected pulse compression signal to obtain a target detection result includes: Selecting a slow time series of each range unit from the corrected pulse compression signal; Calculate the parameter-containing instantaneous symmetric autocorrelation function of the slow time series of each selected distance unit; Perform variable substitution on the parameter-containing instantaneous symmetric autocorrelation function of the slow time series of each distance unit to obtain a transformed parameter-containing instantaneous symmetric autocorrelation function; Performing FFT on each of the transformed parameter-containing instantaneous symmetric autocorrelation functions in the time delay dimension to transfer to the CFCR domain, and obtaining a corresponding LVD result; Select the LVD result with the highest peak value from all the obtained LVD results; Target detection is performed according to the LVD result with the highest peak value to obtain a target detection result.

10. The method for efficiently accumulating GPS external radiation source target echo based on PSO and LVD according to claim 9, characterized in that: The expression for each LVD result is as follows: in, Indicates each LVD result, f c is the center frequency, is the frequency modulation, CF is the periodic correlation function, b is the preset delay parameter, δ represents the impulse function, v0 represents the estimated target motion speed, and a0 represents the estimated target motion acceleration.

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