Phase coded signal side lobe suppression method based on particle swarm optimization cancellation algorithm

Abstract

The invention discloses a phase-coded signal sidelobe suppression method based on a particle swarm optimization cancellation algorithm, and aims to overcome the defects that a phase-coded signal is too high in main-sidelobe ratio and cannot adapt to a target echo scene with a relatively large dynamic state, an accurate optimization target function is constructed, a decoding element is solved by adopting a PSO algorithm, and the phase-coded signal sidelobe suppression method based on the particle swarm optimization cancellation algorithm is realized. In combination with over-threshold detection and a sidelobe cancellation suppression algorithm, sidelobe suppression of phase-coded signals of different coding modes is realized, and the method mainly comprises the following steps: step 1, constructing a target function; step 2, according to the constructed objective function, solving an optimal filter coefficient by adopting a PSO (Particle Swarm Optimization) algorithm: step 3, carrying out side lobe suppression filtering; and 4, performing out-of-band side lobe cancellation suppression.

Description

Technical Field

[0003] The invention belongs to the field of radar signal processing, and in particular relates to a phase coded signal sidelobe suppression method based on a particle swarm optimization cancellation algorithm. Background Art

[0005] With the development of modern weapons and modern flight technology, higher and higher requirements are put forward for radar's performance indicators such as range resolution and measurement accuracy. In order to increase the detection capability of the radar system, it is required to increase the average transmission power of the radar. When the peak power is limited, the transmission pulse is required to be as wide as possible, while in order to improve the system's range resolution, the transmission pulse is required to be as narrow as possible. Improving the radar's range resolution and increasing the detection capability are a contradiction. The usual solution is to transmit a time-stretched signal at the transmitter end, perform necessary modulation inside the signal, and generate a narrow time pulse at the receiving end through a compression filter. This process is called pulse compression. As an important technology of modern radar, pulse compression effectively solves the contradiction between resolution and average power, and is widely used in modern radars.

[0006] According to the waveform used in the implementation of the pulse compression system, pulse compression can be divided into three types: linear frequency modulation pulse compression, nonlinear frequency modulation pulse compression and phase coded pulse compression. Linear frequency modulation pulse compression signals are easy to generate, and the compressed signal waveform and signal-to-noise ratio loss are insensitive to Doppler frequency shift. Its research and application have been quite mature. Its disadvantage is that it has large range sidelobes. Especially in a multi-target environment, the range sidelobes will drown out small echo signals, causing the target to be lost. In order to reduce the range sidelobes, the compressed pulse must be weighted. For linear frequency modulation pulse compression radar, weighting means that a strict matching filter cannot be used, which will cause signal-to-noise ratio loss and widen the main lobe of the compressed pulse. Nonlinear frequency modulation signals are used in pulse compression radars. Their main advantages are: (1) good sidelobe suppression performance can be obtained without any weighting, and there is no signal-to-noise ratio loss; (2) a good main-to-side ratio can still be obtained under the condition of small bandwidth product. Its disadvantage is that it is difficult to implement and the system is extremely complex.

[0007] Phase coded signals have a large main-to-sub ratio and good compression performance when the time-bandwidth product is small, so they have received more and more attention. In addition, due to the randomness of the signal waveform, easy implementation, and agility, they are beneficial to improving the anti-interception capability of the radar system. Phase coded signals can be divided into binary codes and polyphase codes according to the number of phase values. Among them, binary codes are the most important and widely studied type of phase coded signals, mainly including Barker codes, m-sequences, L-sequence codes, etc.

[0008] In practical applications, for phase-coded signals with small time-bandwidth product, even if the genetic optimization algorithm is used to search for the theoretical optimal solution, if matched filtering is used directly, the output main-side lobe ratio may still not be large enough to meet the actual requirements. Therefore, measures must be taken to reduce its output side lobes and increase the main-side lobe ratio. For linear frequency modulation signals or nonlinear frequency modulation signals, the most common and commonly used method to reduce side lobes is windowing. Common window functions include cosine window, Hanning window, Hamming window, Blackman window, Bartlett window, etc. However, in practice, we found that if the phase-coded signal is directly windowed, not only can the main-side lobe ratio not be increased, but the pulse pressure performance will be deteriorated. Therefore, other optimization algorithms can only be used to suppress side lobes. A typical example is the iterative weighted least squares algorithm. The algorithm uses a weighted iterative method to optimize the peak sidelobe level PSL, which can achieve a main-to-sidelobe ratio of about 40dB. However, the calculation time is relatively long. In addition, for large dynamic scenes, the amplitude difference between large targets and small targets exceeds 40dB, and small targets are easily covered by the echo signal of large targets. Summary of the invention

[0010] The purpose of the present invention is to provide a phase-coded sidelobe suppression method based on a PSO cancellation algorithm. In view of the shortcomings of a phase-coded signal having a main-to-sidelobe ratio that is too high and being unable to adapt to target echo scenes with large dynamics, an accurate optimization objective function is constructed, a PSO algorithm is used to obtain decoding elements, and over-threshold detection and sidelobe cancellation suppression algorithms are combined to achieve sidelobe suppression of phase-coded signals with different coding methods.

[0011] In order to achieve the above object, the technical solution adopted by the present invention is: a phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm, comprising the following steps:

[0012] Step 1: Construct the objective function;

[0013] Step 2: Based on the constructed objective function, the PSO algorithm is used to solve the optimal filter coefficients, including:

[0014] Step 2.1: Solve parameter settings;

[0015] Step 2.2: Initialize population individuals;

[0016] Step 2.3: Calculation of initial population fitness;

[0017] Step 2.4: Find the global optimum;

[0018] Step 2.5: Update the position and velocity of each particle;

[0019] Step 2.6: Update the individuals and fitness of the global optimal variables;

[0020] Step 2.7: Output the optimal filter coefficients;

[0021] Step 3: Sidelobe suppression filtering;

[0022] Step 4: Out-of-band sidelobe cancellation suppression.

[0023] As a preferred embodiment of the present invention, the step 1 of constructing the objective function comprises:

[0024] Step 2.1: Calculate the autocorrelation function of the code element and obtain a The row vector of , where sN is the Nth code element and n is the code element length;

[0025] (1)

[0026] Step 2.2: Determine the matrix X0 according to the autocorrelation function value;

[0027] (2)

[0028] In the formula, H represents the zero vector, and zero is added to the corresponding position to obtain A matrix of Corresponding to the length of the autocorrelation function, i is the serial index;

[0029] Step 2.3: Generate the filter expected output vector G:

[0030] (3)

[0031] Step 2.4: Construct the objective function f:

[0032] (4)

[0033] in, is the weighted filter coefficient to be estimated, For vector Calculate the second norm of .

[0034] As a preferred embodiment of the present invention, step 2 specifically includes:

[0035] Step 2.1: Solving parameter settings

[0036] Set the parameters required for solving, including: the number of individuals in the initial population (N), learning factor 1 (c1), learning factor 2 (c2), maximum number of iterations (M), and number of solution parameters (D).

[0037] Step 2.2: Initialize population individuals

[0038] The PSO algorithm performs iterative search in a given initialized population individual, using a normally distributed random number to initialize the initial position and initial velocity of each particle, that is, the position and velocity of each particle are initialized in turn as ( OK Columns) of a normally distributed random number matrix;

[0039] Step 2.3: Calculation of initial population fitness

[0040] Write a fitness calculation function. The evaluation function is determined according to the optimization goal of the problem. Since the optimal value of the problem to be solved is the parameter corresponding to the minimum value of the objective function, the objective function is used as the fitness calculation function. The smaller its value, the better the fitness. Substitute each initial population into the objective function in turn, and calculate the output objective function value, that is, the fitness.

[0041] Step 2.4: Find the global optimum

[0042] According to the calculated fitness, the fitness of various populations is sorted by the sorting function, the population with the largest fitness is selected, and the individual corresponding to the optimal fitness value is stored in In , the best fitness value is saved in the global optimal value gbest;

[0043] Step 2.5: Update the position and velocity of each particle

[0044] The position of each particle , and speed Update according to the following formula:

[0045] (5)

[0046] The phase:

[0047] Step 2.6: Update the individuals and fitness of the global optimal variables

[0048] Calculate the fitness of each particle after the update, and obtain the optimal fitness of the current population and the particle corresponding to the optimal fitness; when the fitness of the new particle is better than the fitness of the global optimal solution, replace the fitness of the global optimal solution with the optimal fitness of the current population, and replace the global optimal solution with the particle corresponding to the optimal fitness of the current population, otherwise no replacement is performed and enter the next stage; that is, update ; Update the particles Substitute it into the objective function, find the fitness of each particle, and find the minimum fitness value of all particles after the update by sorting. If the minimum fitness value is less than the global optimal value gbest, update the minimum fitness value to gbest, and update the particle corresponding to the optimal value to middle;

[0049] Step 2.7: Output the optimal filter coefficients

[0050] Determine whether the number of iterations is greater than the maximum number of iterations initially set. If so, output the optimal solution. And the corresponding optimal objective function value gbest, This is the solution to the desired objective function; at this time, the optimal solution of the objective function is output. That is the global optimum of the entire objective function, that is, the target position obtained by positioning.

[0051] As a preferred embodiment of the present invention, the step 3 is specifically: the signal after the pulse compression processing of the phase-coded echo signal is compared with the optimal filter coefficient Convolution is performed to obtain the signal after phase-coded pulse compression and sidelobe suppression.

[0052] As a preferred embodiment of the present invention, step 4 specifically includes:

[0053] Step 4.1: The sidelobe suppressed signal s1 is used for processing in two directions. On the one hand, it is used to determine whether there is a suppression signal s3 of the target to be detected, and on the other hand, it is used for the out-of-band sidelobe suppression signal s2 of the sidelobe cancellation. The two signals are updated with continuous iterations.

[0054] Step 4.2: Generate the corresponding signal according to the transmitted symbol sequence, and perform pulse compression and matched filtering on it to obtain the reference signal , and determine the maximum value of the signal And the corresponding phase ;

[0055] Step 4.3: Determine the maximum value of the suppression signal s3 And the corresponding phase ;

[0056] Step 4.4: Judgement Whether it exceeds the set threshold;

[0057] Step 4.5: Reference signal Calculate the cross-correlation with the signal s3 after sidelobe suppression to determine the maximum cross-correlation value of the two signals and the corresponding delay ;

[0058] Step 4.6: According to and The proportional relationship between them is used to obtain the reference signal corresponding to each target. ;

[0059] Step 4.7: According to the time domain characteristics of Fourier transform and the delay obtained by cross-correlation, the reference signal is time-shifted to coincide with the signal peak after sidelobe suppression. The time domain shift process of the reference signal is expressed as:

[0060] (6)

[0061] Step 4.8: Combine the phase difference between the reference signal and the signal after sidelobe suppression to make the phase of the suppressed signal and the signal after sidelobe suppression consistent, so as to facilitate subsequent cancellation. ;

[0062] Step 4.9: The ideal sidelobe suppression signal will be obtained Do two things: cancel directly with the suppression signal s3 and subtract , update s3 using the obtained result, and repeat steps 4.2 to 4.8;

[0063] Step 4.10: Ideal sidelobe suppression signal On the other hand, the target area is set to 0 to obtain a sidelobe suppression cancellation reference signal, which is cancelled with the out-of-band sidelobe suppression signal s2 to suppress the sidelobe at the corresponding position of s2, and the cancellation result is updated to s2;

[0064] Step 4.11: When the maximum value of s3 is lower than the set threshold, it is considered that all the side lobes of the target are suppressed, and the final out-of-band side lobe suppression signal s2 is output.

[0065] Compared with the prior art, the technical solution adopted by the present invention has the following beneficial effects:

[0066] 1. The present invention adopts a combination of optimization algorithm estimation and cancellation technology to achieve a main-to-sidelobe ratio suppression of more than 60dB.

[0067] 2. The present invention has high computational real-time performance, can effectively solve the dynamic requirements and computational real-time performance problems of phase-coded signal sidelobe suppression, and is of great significance to the waveform design and signal processing of radar systems.

[0068] 3. The present invention has strong versatility and can be used for sidelobe suppression of phase-coded signals of various code element modulations such as Barker code, M sequence, L sequence, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 14 is a flow chart of the phase coded sidelobe suppression method based on the PSO cancellation algorithm in this embodiment.

[0071] Figure 2 4 is a flow chart of the implementation of out-of-band sidelobe cancellation suppression in this embodiment.

[0072] Figure 3 1 is a time-frequency diagram of a 13-bit Barker code phase-coded echo signal in this embodiment.

[0073] Figure 4 1 is a schematic diagram of the sidelobe suppression effect of the 13-bit Barker code phase coded echo signal in this embodiment.

[0074] Figure 5 It is a partially enlarged schematic diagram of the sidelobe suppression effect of the 13-bit Barker code phase-coded echo signal in this embodiment.

[0075] Figure 6 31-bit M-sequence phase-coded echo signal time-frequency diagram in this embodiment.

[0076] Figure 7 31-bit M-sequence phase-coded echo signal sidelobe suppression effect schematic diagram in this embodiment.

[0077] Figure 8 It is a partially enlarged schematic diagram of the sidelobe suppression effect of the 31-bit M sequence phase-coded echo signal in this embodiment. DETAILED DESCRIPTION

[0079] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings and specific embodiments.

[0080] like Figure 1 As shown, a flow chart of a phase coded sidelobe suppression method based on a PSO cancellation algorithm in this embodiment includes the following steps:

[0081] Constructing the objective function

[0082] In the present invention, the construction of the objective function requires the use of the correlation operation of the modulation code element of the transmission signal. The process of constructing the objective function is as follows:

[0083] Calculate the autocorrelation function of the code element and get a The row vector of , where sN is the Nth code element and n is the code element length;

[0084] (1)

[0085] Determine the matrix X0 based on the autocorrelation function value

[0086] (2)

[0087] In the formula, H represents the zero vector, and zero is added to the corresponding position to obtain A matrix of Corresponding to the length of the autocorrelation function, i is the serial index.

[0088] Generate the expected output vector G of the filter

[0089] (3)

[0090] Construct the objective function f

[0091] (4)

[0092] in, is the weighted filter coefficient to be estimated, For vector Calculate the second norm of .

[0093] Optimal filter coefficients Optimization solution

[0094] According to the aforementioned objective function, the PSO algorithm is used to solve the optimal filter coefficients. The calculation process is as follows

[0095] Solution parameter settings

[0096] Set the parameters required for the solution, mainly including the following parameters: the number of individuals in the initial population (N), learning factor 1 (c1), learning factor 2 (c2), the maximum number of iterations (M), and the number of solution parameters (search space dimension D).

[0097] Initialize the population

[0098] The PSO algorithm performs iterative search in a given initialized population of individuals. In the present invention, a normally distributed random number is used to initialize the initial position and initial velocity of each particle, that is, the position and velocity of each particle are initialized to ( OK Columns) of a normally distributed random number matrix.

[0099] Initial population fitness calculation

[0100] Write a fitness calculation function, and the evaluation function is determined according to the optimization goal of the problem. In the present invention, since the optimal value of the problem to be solved is the parameter corresponding to the minimum value of the objective function, the objective function is used as the fitness calculation function, and the smaller its value, the better the fitness. Each initial population is sequentially brought into the objective function, and the output objective function value, i.e., the fitness, is calculated.

[0101] Finding the global optimum

[0102] According to the fitness calculated in step C, sort the fitness of each population using the sorting function. Select the population with the largest fitness and store the individual corresponding to the optimal fitness value in In the above example, the best fitness value is saved in the global optimal value gbest.

[0103] Update the position and velocity of each particle

[0104] The position of each particle , and speed Update according to the following formula:

[0105] (5)

[0106] The phase:

[0107] Update the individuals and fitness of the global optimal variables

[0108] Calculate the fitness of each particle after the update, and obtain the optimal fitness of the current population and the particle corresponding to the optimal fitness; when the fitness of the new particle is better than the fitness of the global optimal solution, replace the fitness of the global optimal solution with the optimal fitness of the current population, and replace the global optimal solution with the particle corresponding to the optimal fitness of the current population, otherwise no replacement is performed and the next stage is entered. ; Update the particles Substitute it into the objective function, find the fitness of each particle, and find the minimum fitness value of all particles after the update by sorting. If the minimum fitness value is less than the global optimal value gbest, update the minimum fitness value to gbest, and update the particle corresponding to the optimal value to middle.

[0109] Output optimal filter coefficients

[0110] Determine whether the number of iterations is greater than the maximum number of iterations initially set. If so, output the optimal solution. And the corresponding optimal objective function value gbest, This is the solution to the objective function. At this time, the optimal solution of the objective function is output. That is the global optimum of the entire objective function, that is, the target position obtained by positioning.

[0111] Sidelobe Suppression Filter

[0112] The signal after pulse compression of the phase-coded echo signal is compared with the optimal filter coefficient Convolution is performed to obtain the signal after phase-coded pulse compression and sidelobe suppression.

[0113] Out-of-band sidelobe cancellation

[0114] The signal s1 after sidelobe suppression is used for processing in two directions. On the one hand, it is used to determine whether there is a suppression signal s3 of the target to be detected, and on the other hand, it is used for the out-of-band sidelobe suppression signal s2 of the sidelobe cancellation. The two signals are updated with continuous iterations.

[0115] According to the transmitted code element sequence, the corresponding signal is generated, and pulse compression and matched filtering are performed on it to obtain the reference signal , and determine the maximum value of the signal And the corresponding phase ;

[0116] Determine the maximum value of the suppression signal s3 And the corresponding phase ;

[0117] judge Whether it exceeds the set threshold;

[0118] The reference signal Calculate the cross-correlation with the signal s3 after sidelobe suppression to determine the maximum cross-correlation value of the two signals and the corresponding delay ;

[0119] according to and The proportional relationship between them is used to obtain the reference signal corresponding to each target. ;

[0120] According to the time domain characteristics of Fourier transform and the delay obtained by cross-correlation, the reference signal is time-shifted to coincide with the signal peak after sidelobe suppression. The time domain shift process of the reference signal is expressed as:

[0121] (6)

[0122] Combine the phase difference between the reference signal and the signal after sidelobe suppression to make the phase of the suppressed signal and the signal after sidelobe suppression consistent, so as to facilitate subsequent cancellation.

[0123] The ideal sidelobe suppression signal will be obtained Do two things: cancel directly with the suppression signal s3 and subtract , update s3 using the obtained result, and repeat steps (B) to (H);

[0124] Ideal sidelobe suppression signal On the other hand, the target area is set to 0 to obtain a sidelobe suppression cancellation reference signal, which is cancelled with the out-of-band sidelobe suppression signal s2 to suppress the sidelobe at the corresponding position of s2, and the cancellation result is updated to s2;

[0125] When the maximum value of s3 is lower than the set threshold, it is considered that all the side lobes of the target are suppressed, and the final out-of-band side lobe suppression signal s2 is output.

[0126] like Figure 3-8 As shown, the simulated echo of a phase-coding simulator is processed, and the algorithm given in this embodiment is used to perform sidelobe suppression on the phase-coded echo signals modulated by 13-bit Barker code and M sequence, respectively. The sidelobe suppression effect of the matched filter of the phase-coded echo signal using this method is observed to verify the superiority of this method.

[0127] Figure 3 This is a time-frequency diagram of a 13-bit Barker code phase-coded echo signal, which contains 5 targets. To facilitate the verification of this method, the 5 targets are set to have an amplitude difference of 3dB and a distance interval of 1 km. This echo signal is processed using this method to obtain Figure 4 The target echo characteristic signals are shown in Figure 1. The blue represents the unsuppressed echo signal, and the red represents the echo suppressed by this method. The local area where the five targets are located is enlarged to obtain Figure 5 . Blue line: The five peaks are the locations of the five targets. The sidelobe intensity of the largest target is about -22dB, and the sidelobe intensity of the smallest target is about -10dB, which is very likely to cause missed reports. Red line: The sidelobe intensity of the target echo is about -70dB, and the suppression effect is significant, which is conducive to target extraction. In comparison, this method is far superior to the 40dB suppression effect of other algorithms.

[0128] Figure 6 This is the time-frequency diagram of the 31-bit M sequence phase-coded echo signal, which contains 5 targets. The echo signal is processed using this method to obtain Figure 6 The target echo characteristic signals are shown in Figure 1. The blue represents the unsuppressed echo signal, and the red represents the echo suppressed by this method. The local area where the five targets are located is enlarged to obtain Figure 8 . Blue line: The five peaks are the locations of the five targets, and the amplitudes are different. The sidelobe intensity of the largest target is about -13dB, and the sidelobe intensity of the smallest target is about -2dB, which has been submerged in the background noise and cannot be identified. Red line: The sidelobe intensity of the target echo is about -55dB, and the suppression effect is significant, which is conducive to the extraction of the target.

[0129] Analysis of measured data shows that, through the improvement and combination of the traditional sidelobe suppression algorithm and the use of a combination of optimized algorithm estimation and cancellation technology, this embodiment can achieve ultra-high main-sidelobe suppression and has high computational real-time performance. It can effectively solve the dynamic requirements and computational real-time problems of phase-coded signal sidelobe suppression. The overall algorithm has low complexity, short running time, and strong applicability.

[0130] Although the invention has been disclosed as above in terms of preferred embodiments, the embodiments and drawings are not intended to limit the invention. Anyone familiar with the art can make various changes or modifications without departing from the spirit and scope of the invention, but they are also within the scope of protection of the invention. Therefore, the scope of protection of the invention shall be based on the scope of protection of the claims of this application.

Claims

1. A phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm, characterized in that: The following steps are involved: Step 1: Construct the objective function; Step 2: Based on the constructed objective function, the PSO algorithm is used to solve the optimal filter coefficients, including: Step 2.1: Solve parameter settings; Step 2.2: Initialize population individuals; Step 2.3: Calculation of initial population fitness; Step 2.4: Find the global optimum; Step 2.5: Update the position and velocity of each particle; Step 2.6: Update the individuals and fitness of the global optimal variables; Step 2.7: Output the optimal filter coefficients; Step 3: Sidelobe suppression filtering; Step 4: Out-of-band sidelobe cancellation suppression.

2. The phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm according to claim 1, characterized in that: The step 1 of constructing the objective function includes: Step 2.1: Calculate the autocorrelation function of the code element and obtain a A row vector of s N is the Nth code element, n is the code element length; （1） Step 2.2: Determine the matrix X0 according to the autocorrelation function value; （2） In the formula, H represents the zero vector, and zero is added to the corresponding position to obtain A matrix of Corresponding to the length of the autocorrelation function, i is the serial index; Step 2.3: Generate the filter expected output vector G: （3） Step 2.4: Construct the objective function f: （4） in, is the weighted filter coefficient to be estimated, For vector Calculate the second norm of .

3. The phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm according to claim 1, characterized in that: The step 2 comprises: Step 2.1: Solving parameter settings Set the parameters required for solving, including: the number of individuals in the initial population (N), learning factor 1 (c1), learning factor 2 (c2), maximum number of iterations (M), and number of solution parameters (D); Step 2.2: Initialize population individuals The PSO algorithm performs iterative search in a given initialized population individual, using a normally distributed random number to initialize the initial position and initial velocity of each particle, that is, the position and velocity of each particle are initialized in turn as ( OK Columns) of a normally distributed random number matrix; Step 2.3: Calculation of initial population fitness Write a fitness calculation function. The evaluation function is determined according to the optimization goal of the problem. Since the optimal value of the problem to be solved is the parameter corresponding to the minimum value of the objective function, the objective function is used as the fitness calculation function. The smaller its value, the better the fitness. Substitute each initial population into the objective function in turn, and calculate the output objective function value, that is, the fitness. Step 2.4: Find the global optimum According to the calculated fitness, the fitness of various populations is sorted by the sorting function, the population with the largest fitness is selected, and the individual corresponding to the optimal fitness value is stored in In , the best fitness value is saved in the global optimal value gbest; Step 2.5: Update the position and velocity of each particle The position of each particle , and speed Update according to the following formula: （5） The phase is: ; Step 2.6: Update the individuals and fitness of the global optimal variables Calculate the fitness of each particle after the update, and obtain the optimal fitness of the current population and the particle corresponding to the optimal fitness; when the fitness of the new particle is better than the fitness of the global optimal solution, replace the fitness of the global optimal solution with the optimal fitness of the current population, and replace the global optimal solution with the particle corresponding to the optimal fitness of the current population, otherwise no replacement is performed and enter the next stage; that is, update ; Update the particles Substitute it into the objective function, find the fitness of each particle, and find the minimum fitness value of all particles after the update by sorting. If the minimum fitness value is less than the global optimal value gbest, update the minimum fitness value to gbest, and update the particle corresponding to the optimal value to middle; Step 2.7: Output the optimal filter coefficients ; Determine whether the number of iterations is greater than the maximum number of iterations initially set. If so, output the optimal solution. And the corresponding optimal objective function value gbest, This is the solution to the desired objective function; at this time, the optimal solution of the objective function is output. That is the global optimum of the entire objective function, that is, the target position obtained by positioning.

4. The phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm according to claim 3 is characterized in that: The step 3 comprises: comparing the signal after the pulse compression processing of the phase-encoded echo signal with the optimal filter coefficient Convolution is performed to obtain the signal after phase-coded pulse compression and sidelobe suppression.

5. The phase coded signal sidelobe suppression method based on particle swarm optimization cancellation algorithm according to claim 4, characterized in that: The step 4 comprises: Step 4.1: The sidelobe suppressed signal s1 is used for processing in two directions. On the one hand, it is used to determine whether there is a suppression signal s3 of the target to be detected, and on the other hand, it is used for the out-of-band sidelobe suppression signal s2 of the sidelobe cancellation. The two signals are updated with continuous iterations. Step 4.2: Generate the corresponding signal according to the transmitted symbol sequence, and perform pulse compression and matched filtering on it to obtain the reference signal , and determine the maximum value of the signal And the corresponding phase ; Step 4.3: Determine the maximum value of the suppression signal s3 And the corresponding phase ; Step 4.4: Judgement Whether it exceeds the set threshold; Step 4.5: Reference signal Calculate the cross-correlation with the signal s3 after sidelobe suppression to determine the maximum cross-correlation value of the two signals and the corresponding delay ; Step 4.6: According to and The proportional relationship between them is used to obtain the reference signal corresponding to each target. ; Step 4.7: According to the time domain characteristics of Fourier transform and the delay obtained by cross-correlation, the reference signal is time-shifted to coincide with the signal peak after sidelobe suppression. The time domain shift process of the reference signal is expressed as: （6） Step 4.8: Combine the phase difference between the reference signal and the signal after sidelobe suppression to make the phase of the suppressed signal and the signal after sidelobe suppression consistent, so as to facilitate subsequent cancellation. ; Step 4.9: The ideal sidelobe suppression signal will be obtained Do two things: cancel directly with the suppression signal s3 and subtract , update s3 using the obtained result, and repeat steps 4.2 to 4.8; Step 4.10: Ideal sidelobe suppression signal On the other hand, the target area is set to 0 to obtain a sidelobe suppression cancellation reference signal, which is cancelled with the out-of-band sidelobe suppression signal s2 to suppress the sidelobe at the corresponding position of s2, and the cancellation result is updated to s2; Step 4.11: When the maximum value of s3 is lower than the set threshold, it is considered that all the side lobes of the target are suppressed, and the final out-of-band side lobe suppression signal s2 is output.

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