MTD filter design method based on particle swarm optimization

Abstract

The invention relates to the technical field of radar signal processing, in particular to a particle swarm optimization-based MTD filter design method, which comprises the following steps of: S1, obtaining a Doppler center frequency range and a normalized half width according to a radar type and a clutter type so as to obtain a constraint condition, obtaining filter information according to the constraint condition and the global pilot vector matrix; s2, designing a fitness function according to the nonlinear function and a hard coding threshold; and S3, designing a filter according to the fitness function, and iteratively updating the filter according to a main lobe width dichotomy to obtain optimal filtering. According to the method, the linear and nonlinear multi-parameter fitness function is constructed, so that the optimal design is realized on the premise of meeting the requirements of stopband attenuation, null depth and clutter band width. The main lobe width is subjected to dichotomy iterative optimization, so that higher robustness in different clutter environments is realized, the design efficiency of the MTD filter is improved, the main lobe broadening of the filter is reduced, and the gain loss of the filter is reduced.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing technology, and in particular to a MTD filter design method, device and storage medium based on a particle swarm algorithm. Background Art

[0002] When pulse Doppler radar detects, distinguishes and identifies various aerial targets, its overall detection performance is often limited by the strong environmental clutter around the target. MTD filter is a key component in pulse Doppler radar signal processing, which is mainly used to detect and separate moving target signals.

[0003] Traditional MTD filter design methods mainly rely on classic FIR filter design techniques. These methods usually require manual adjustment of parameters, and their performance is not ideal when dealing with complex environments (such as multipath propagation, strong clutter interference, etc.). Typical clutter interference includes ground clutter, meteorological clutter, and sea clutter. These clutters have a certain radial velocity relative to the radar, so they will be slightly broadened in the Doppler dimension. Usually, the power of the clutter signal is very strong. If no processing is performed, the target echo will be completely submerged, seriously reducing the detection probability of the radar. In addition, if the clutter has a strong point in the distance dimension, it may be mistakenly identified as a real target, thereby significantly increasing the false alarm rate of the radar.

[0004] Conventional adaptive MTD filter design methods can meet the expected frequency response requirements, but they require manual repeated adjustments in terms of stopband level and clutter power, and cannot automatically and accurately adjust the relevant control parameters. In addition, within the weak suppression range of the designed filter, only the maximum values ​​of some sidelobes can reach or approach the expected stopband attenuation requirements, while the maximum values ​​of other sidelobes are less than the expected attenuation requirements. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and provide a MTD filter design method based on a particle swarm algorithm, comprising the following steps: S1: obtaining a Doppler center frequency range and a normalized half width according to the radar type and the clutter type, obtaining constraints including a center frequency point, a side lobe area, a null area, and a main lobe area according to the Doppler center frequency range and the normalized half width, and obtaining the filter information according to the constraints and the global pilot vector matrix; S2: designing a fitness function according to a nonlinear function and a hard-coded threshold for adjusting the filter information; S3: Designing a filter according to the fitness function, and iteratively updating the filter according to the main lobe width dichotomy to obtain an optimal filter.

[0006] Preferably, in step S1, obtaining the filter information according to the constraint condition and the global pilot vector matrix further comprises: S11: constructing the global pilot vector matrix according to the filter order and the normalized Doppler frequency full sampling points; S12: Calculate the filter frequency response amplitude according to the constraint condition and the global pilot vector matrix, obtain the frequency response gain according to the filter frequency response amplitude, and obtain the filter information according to the frequency response gain.

[0007] Preferably, in step S11, constructing the global pilot vector matrix according to the filter order and the normalized Doppler frequency full sampling points further includes: The calculation formula of the row vector of the global pilot vector matrix is ​​as follows: in, is the normalized Doppler frequency full sampling point, Represents a row vector whose elements are the indices of the filter order, that is, , is the order of the filter, the global pilot vector matrix No. The row represents the filter in The complex response vector of frequency points; The global pilot vector matrix is ​​obtained according to the row vector of the global pilot vector matrix, and the global pilot vector matrix The calculation formula is as follows: .

[0008] Preferably, in step S12, calculating the filter frequency response amplitude according to the constraint condition and the global pilot vector matrix further includes: According to the normalized Doppler center frequency Calculate an initial pilot vector, the initial pilot vector The calculation formula is as follows: in, is the imaginary number symbol; A complex weight vector is obtained according to the initial pilot vector, wherein the complex weight vector The calculation formula is as follows: Among them, the complex weight vector is the optimization variable With the initial pilot vector The element-by-element product of the complex weight vector The initial pilot vector information, which includes the filter amplitude, filter phase and filter center frequency, directly affects the frequency response characteristics of the filter; The filter frequency response amplitude is obtained according to the constraint condition, the global pilot vector matrix and the complex weight vector, and the filter frequency response amplitude The calculation formula is as follows: .

[0009] Preferably, in step S12, obtaining a frequency response gain according to the frequency response amplitude of the filter further comprises: The filter frequency response amplitude is normalized to obtain a normal frequency response gain. The calculation formula is as follows: According to the normal frequency response gain Get the frequency response gain, the frequency response gain The calculation formula is as follows: .

[0010] Preferably, in step S12, obtaining the filter information according to the frequency response gain further includes: According to the frequency response gain, the maximum level of the side lobe, the maximum level of the null notch and the maximum level of the main lobe are obtained; The maximum sidelobe level of the sidelobe region is calculated according to the frequency response gain, and the maximum sidelobe level The calculation formula is as follows: in, It is the zero-sink area; The maximum level of the null region is calculated according to the frequency response gain. The calculation formula is as follows: in, It is the zero-sink area; The maximum level of the main lobe in the main lobe area is calculated according to the frequency response gain. The calculation formula is as follows: .

[0011] Preferably, in step S3, designing a filter according to the fitness function further comprises: The fitness function designs the filter by adjusting the sidelobe level and the null level. The calculation formula of the fitness function is as follows: in, is the main lobe level, V is the hard-coded threshold, is the sidelobe level, is the clutter suppression zone level, The side lobe is weakly attenuated as, Attenuation of clutter band.

[0012] Preferably, in step S3, iteratively obtaining the optimal filter according to the main lobe width dichotomy method further comprises: The main lobe width corresponding to the unwindowed FFT is used as the lower limit , with the main lobe width corresponding to the Taylor window as the upper limit of the clutter suppression area level , and give the tolerance ζ, the main lobe width in the first optimization is =( + ) / 2; If the main lobe width meets the requirements in the first optimization, then ; If the main lobe width does not meet the requirements in the first optimization, let , and iterate according to the main lobe width dichotomy until - <ζ; The optimal filter is obtained according to the obtained optimal main lobe width.

[0013] Based on the same concept, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of the embodiments.

[0014] Based on the same concept, the present invention also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, enables the one or more processors to perform the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of the embodiments.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the Doppler center frequency range and the normalized half width according to the radar type and the clutter type, obtains the constraint conditions including the center frequency point, the side lobe area, the null area and the main lobe area according to the Doppler center frequency range and the normalized half width, and obtains the filter information according to the constraint conditions and the global pilot vector matrix, so that the amplitude and phase of the target filter can be optimized at the same time.

[0016] The invention realizes the optimized design by constructing linear and nonlinear multi-parameter fitness functions under the premise of meeting the requirements of stopband attenuation, null depth and clutter band width.

[0017] The present invention performs iterative optimization through binary division of the main lobe width, and design simulations show that the implemented algorithm has high robustness in different clutter environments, and has lower gain loss than the original Taylor window FFT-type MTD filter that directly adds the clutter attenuation level, has a strong clutter suppression effect, and has a narrower main lobe width, thereby improving the design efficiency of the MTD filter. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0019] Figure 1 A flow chart of a MTD filter design method based on a particle swarm algorithm of the present invention; Figure 2 It is the amplitude-frequency response diagram of the Ku-band radar ground clutter MTD filter of the present invention; Figure 3 It is an amplitude-frequency response diagram of the X-band radar weather clutter MTD filter of the present invention; Figure 4 It is the amplitude-frequency response diagram of the C-band radar sea clutter MTD filter of the present invention; Figure 5 It is a comparison diagram of the results before and after the binary division iteration in the present invention is completed; Figure 6 The main lobe of the present invention is compared with the main lobe of the conventional Taylor window MTD filter. DETAILED DESCRIPTION

[0020] Example 1 In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0022] In recent years, intelligent optimization algorithms (such as particle swarm optimization (PSO) and genetic algorithm (GA)) have shown superior performance in the field of signal processing, especially in optimization problems under nonlinear and multi-constrained conditions. These algorithms can quickly search for the global optimal solution of the problem by simulating the behavior of biological groups or the process of natural selection, thereby significantly improving the design efficiency and performance of MTD filters.

[0023] The classic FIR filter usually needs to adjust parameters manually and has unsatisfactory performance when dealing with complex clutter environments. However, before entering the particle swarm algorithm optimization, this embodiment first clarifies the four constraints related to the center frequency point, main lobe, strong suppression clutter band, and weak suppression side lobe, and then sets different radar operating frequencies, clutter velocity standard deviations, MTD filter orders, pulse repetition periods and other parameters to model the clutter in detail and establish a pilot vector matrix of the normalized Doppler full frequency, and then constrains the positions of the clutter band and the main lobe in the normalized Doppler full frequency. After that, the pilot vectors at the center frequency points of different MTD filters are constructed according to the set center frequency.

[0024] After entering the particle swarm algorithm design stage, the candidate solution of the particle swarm algorithm is made to contain the amplitude and phase information of the target filter at the same time, and the candidate solution is multiplied item by item with the pilot vector at the center frequency of the MTD filter to obtain a complex weight vector, and the vector is multiplied with the normalized Doppler full-band pilot vector matrix to obtain the frequency response of the target filter. At the same time, by debugging the number of particle swarms, hybrid optimizers, maximum number of iterations, initial population matrix and other parameters in the algorithm, and adding linear conditions to constrain the attenuation of the sidelobe and clutter area and nonlinear conditions to constrain the main lobe center frequency and width in the fitness function for multi-objective optimization, the preliminary design of the filter that determines the center frequency is completed. After that, the optimal main lobe width is found through binary iteration to obtain the optimal design result of the present invention.

[0025] See also Figure 1 As shown, the present embodiment provides a MTD filter design method based on a particle swarm algorithm, which realizes a series of excellent characteristics of the filter, such as strong attenuation of the clutter band, flexible adjustment of the use environment, and convenient and fast design process, including the following steps: S1: The Doppler center frequency range and normalized half width are obtained according to the radar type and clutter type. Constraints including the center frequency point, sidelobe area, null area and mainlobe area are obtained according to the Doppler center frequency range and the normalized half width. Filter information is obtained according to the constraints and the global pilot vector matrix.

[0026] Preferably, the filter order is , the normalized Doppler center frequency is , each MTD filter in the filter bank should have a constant gain of 0 dB at its center frequency, and an initial main lobe width of , then the main lobe in the normalized Doppler frequency range is , is the filter frequency response, the center frequency response (i.e. the center frequency area) should meet the following requirements: To prevent the center frequency of the filter main lobe from shifting, the main lobe frequency response (i.e., main lobe area) satisfies: Specifically, in this embodiment, it is satisfied that the center frequency of the main lobe of the filter does not shift. The clutter band width is calculated based on the radar operating frequency, pulse repetition period, standard deviation of ground clutter velocity and speed of light. The calculation formula is as follows: in, is the radar operating frequency, T is the pulse repetition period, is the standard deviation of ground clutter velocity, and c is the speed of light.

[0027] Normalize the clutter band width to get the normalized clutter band width , determine the clutter strong suppression area , sidelobe weak suppression area and the main lobe The minimum distance from the strong suppression area, so the clutter band is within the normalized Doppler frequency range. , assuming that the clutter band attenuation is Then, in order to achieve the most critical requirement of the MTD filter, this embodiment forms a strongly attenuated null in the clutter band near the zero frequency to perform strong constraints to suppress the clutter intensity in the clutter band, and the clutter band frequency response satisfies (i.e., the null area): Specifically, in this embodiment, a strong attenuation null is formed in the clutter band near the zero frequency to perform a strong constraint. To suppress the clutter intensity in the clutter band.

[0028] Except for the main lobe and clutter band areas with the above constraints, the remaining range in the full normalized Doppler frequency belongs to the weakly suppressed sidelobe part far from the zero frequency. Suppose the sidelobe weak attenuation is , so the side lobe area , the sidelobe frequency response satisfies (i.e. the sidelobe area): Specifically, in this embodiment, the side lobe part far from the zero frequency is weakly suppressed. .

[0029] After the above preliminary frequency response constraints for each region are obtained, this embodiment intends to use the fitness function of the particle swarm algorithm to constrain the above conditions. In order to obtain the frequency response amplitude, the present invention constructs a complex matrix A to represent the response of the filter at different frequency points, that is, the pilot vector matrix.

[0030] Preferably, in step S1, filter information is obtained according to the constraint condition and the global pilot vector matrix, further comprising: S11: construct a global pilot vector matrix according to the filter order and the normalized Doppler frequency full sampling points; S12: Calculate the filter frequency response amplitude according to the constraint conditions and the global pilot vector matrix, obtain the frequency response gain according to the filter frequency response amplitude, and obtain the filter information according to the frequency response gain.

[0031] Preferably, in step S11, constructing a global pilot vector matrix according to the filter order and the normalized Doppler frequency full sampling points further includes: The calculation formula of the row vector of the global pilot vector matrix is ​​as follows: in, is the normalized Doppler frequency full sampling point, Represents a row vector whose elements are the indices of the filter order, that is, , is the order of the filter, the global pilot vector matrix No. The row represents the filter in The complex response vector of frequency points; The global pilot vector matrix is ​​obtained according to the row vector of the global pilot vector matrix. The calculation formula is as follows: .

[0032] In the fitness function of the particle swarm algorithm of this embodiment, X is the candidate solution of the particle swarm, which is an array with a length of twice the filter order (2N); the upper limit of the 1st to Nth elements is 1 and the lower limit is -1, and the upper limit of the N+1th to 2Nth elements is i and the lower limit is -i. In this way, the amplitude and phase of the target filter can be optimized at the same time.

[0033] Preferably, in step S12, the filter frequency response amplitude is calculated according to the constraint condition and the global pilot vector matrix, further comprising: According to the normalized Doppler center frequency Calculate the initial pilot vector, the initial pilot vector The calculation formula is as follows: in, is the imaginary number symbol; The complex weight vector is obtained according to the initial pilot vector. The calculation formula is as follows: Among them, the complex weight vector is the optimization variable With the initial pilot vector The element-wise product of the complex weight vector The initial pilot vector information including the filter amplitude, filter phase and filter center frequency directly affects the frequency response characteristics of the filter. Specifically, in this embodiment, complex weights are introduced and composite weight phase optimization is used to dynamically evaluate the sidelobe level and clutter suppression zone level of the filter. According to the constraints, the global pilot vector matrix and the complex weight vector, the filter frequency response amplitude is obtained. The calculation formula is as follows: .

[0034] Preferably, in step S12, obtaining the frequency response gain according to the filter frequency response amplitude further includes: Normalize the filter frequency response amplitude to get the normal frequency response gain. The calculation formula is as follows: According to the normal frequency response gain Get the frequency response gain, frequency response gain The calculation formula is as follows: .

[0035] Preferably, in step S12, obtaining filter information according to the frequency response gain further includes: According to the frequency response gain, the maximum level of the side lobe, the maximum level of the null notch and the maximum level of the main lobe are obtained; Among them, the maximum sidelobe level in the sidelobe area is calculated according to the frequency response gain, and the maximum sidelobe level The calculation formula is as follows: in, It is the zero-sink area; Calculate the maximum level of the null zone according to the frequency response gain. The calculation formula is as follows: in, It is the zero-sink area; The maximum level of the main lobe in the main lobe area is calculated according to the frequency response gain. The calculation formula is as follows: .

[0036] S2: designing a fitness function according to the nonlinear function and the hard-coded threshold for adjusting the filter information. Specifically, in this embodiment, a hard-coded threshold V is introduced to prevent the numerical overflow of the nonlinear function during the optimization process; S3: Design a filter according to the fitness function, and iteratively update the filter according to the main lobe width dichotomy to obtain the optimal filter.

[0037] Preferably, in step S3, designing a filter according to a fitness function further comprises: The fitness function designs the filter by adjusting the sidelobe level and the null level. The calculation formula of the fitness function is as follows: in, is the main lobe level, V is the hard-coded threshold, is the sidelobe level, is the clutter suppression zone level, The side lobe is weakly attenuated as, For clutter band attenuation, specifically, in this embodiment, after determining the portion where the main lobe level is adjusted, a linear function is combined to adjust the side lobe level ( ) and the clutter suppression zone level ( ) to obtain multi-objective combination optimization effect.

[0038] The fitness function comprehensively considers the different requirements of the filter main lobe gain and the side lobe and null suppression, so as to ensure that the designed filter can meet specific performance indicators. The first item is to control the main lobe gain of the filter. By using an exponential function, this item can perform nonlinear compression on the gain of the main lobe. When the main lobe gain is high, the value of this item is small, indicating that the main lobe gain of the filter is close to the ideal value. In order to prevent the value of this item from being too large and causing the optimization process to fail, this embodiment sets a hard-coded upper limit V, V≥5. This upper limit ensures that in extreme cases, such as when the main lobe gain is far from the expected value or when the main lobe center frequency is offset, the fitness function can still maintain a reasonable range and avoid numerical instability caused by excessive values. The second item is used to evaluate the attenuation performance of the filter in the side lobe and null region. This item reflects the performance of the filter in the side lobe and null region by calculating the absolute error between the actual attenuation and the expected target. A smaller error means that the filter design is closer to the predetermined performance indicator.

[0039] The fitness function combines the exponential decay term and the absolute value term of the error, with the aim of optimizing both the mainlobe gain and the sidelobe attenuation performance of the filter. The optimization of the mainlobe gain is achieved through nonlinear function compression, while the attenuation performance of the sidelobes and nulls is ensured by minimizing the absolute error. The two work together to achieve a balanced effect in all performance dimensions. The introduction of V ensures that the numerical range of the fitness function will not be too extreme during the optimization process, thereby enhancing the stability and convergence of the algorithm.

[0040] Preferably, in step S3, iteratively obtaining the optimal filter according to the main lobe width dichotomy method further comprises: The main lobe width corresponding to the unwindowed FFT is used as the lower limit , with the main lobe width corresponding to the Taylor window corresponding to the level of the clutter suppression area as the upper limit , and give the tolerance ζ, the main lobe width in the first optimization is =( + ) / 2; If the main lobe width meets the requirements in the first optimization, then ; If the main lobe width does not meet the requirements in the first optimization, let , and iterate according to the main lobe width dichotomy until - <ζ; The optimal filter is obtained based on the obtained optimal main lobe width.

[0041] See also Figure 2 As shown in Figure 1, the amplitude-frequency response of the Ku-band radar ground clutter MTD filter is shown in Figure 2. is 64, the normalized Doppler center frequency is 0.3, and the lower limit of the main lobe width is =0.0140, the upper limit is =0.0245, the initial main lobe width is =0.0193, radar operating frequency The pulse repetition period PRT is 70μs, and the standard deviation of ground clutter velocity is selected is 0.32m / s, and the normalized clutter band width is obtained is 0.0065, assuming the clutter band is attenuated -65dB, weak side lobe attenuation The design result after the binary iteration is shown in the figure. After calculation and verification, the main lobe width of the filter designed in this embodiment is 0.0175, which is better than the main lobe width of 0.0232 with a -65dB Taylor window in the conventional MTD filter design, and better than the main lobe width of 0.0176 with a -30dB Taylor window in the conventional MTD filter design. The signal-to-noise ratio gain of the filter designed in this embodiment is 29.1664 dB, the signal-to-noise ratio gain with a -30dB Taylor window is 29.2609 dB, and the signal-to-noise ratio gain with a -65dB Taylor window is 28.1503 dB. The main lobe width and signal-to-noise ratio gain of this embodiment are better than the conventional design with a -65dB Taylor window.

[0042] See also Figure 3 The figure below shows the amplitude-frequency response of the X-band radar weather clutter MTD filter. The normalized Doppler center frequency is 10 GHz. is -0.4, the pulse repetition period PRT is 60μs, and the standard deviation of meteorological clutter velocity is 4m / s, normalized clutter band width 0.0503, clutter band attenuation The main lobe width of the filter designed in this embodiment is 0.0175, which is better than the main lobe width of 0.0210 with a -50dB Taylor window in the conventional MTD filter design, and better than the main lobe width of 0.0176 with a -30dB Taylor window in the conventional MTD filter design. The signal-to-noise ratio gain of the filter designed in this embodiment is 27.6036dB, the signal-to-noise ratio gain with a -30dB Taylor window is 27.8664 dB, and the signal-to-noise ratio gain with a -50dB Taylor window is 27.4658 dB. The main lobe width and signal-to-noise ratio gain of this embodiment are better than the conventional design with a -50dB Taylor window.

[0043] See also Figure 4 The figure below shows the amplitude-frequency response of the C-band radar sea clutter MTD filter. The normalized Doppler center frequency is 7 GHz. is -0.3, the pulse repetition period PRT is 80μs, and the standard deviation of sea clutter velocity is 1.2m / s, normalized clutter band width 0.0141, clutter band attenuation The final design result after binary iteration is shown in the figure. After calculation and verification, the main lobe width of the filter designed in this embodiment is 0.0174, which is much better than the main lobe width of 0.0228 with -60dB Taylor window in conventional MTD filter design, and better than the main lobe width of 0.0176 with -30dB Taylor window in conventional MTD filter design. The signal-to-noise ratio gain of the filter designed in this embodiment is 29.1717 dB, the signal-to-noise ratio gain with -30dB Taylor window is 29.2476 dB, and the signal-to-noise ratio gain with -60dB Taylor window is 28.5235 dB. The main lobe width and signal-to-noise ratio gain of this embodiment are better than the conventional design with -60dB Taylor window.

[0044] See also Figure 5 As shown, the comparison of the results before and after the binary division iteration of the Ku-band radar ground clutter MTD filter is completed. It can be seen that through the iteration, the present embodiment designs the optimal main lobe width that can meet various constraints.

[0045] See also Figure 6 As shown, the main lobe is compared with the main lobe of the conventional MTD filter with Taylor window. It can be seen that the main lobe width of the MTD filter designed in this embodiment is better than that of the conventional MTD filter.

[0046] Example 2 In some embodiments of the present application, a computer device is also provided, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of Example 1.

[0047] The present invention also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, enables the one or more processors to perform the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of Example 1.

[0048] It can be understood that, for the aforementioned MTD filter design method based on particle swarm algorithm, if it is implemented in the form of software function modules and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0049] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0050] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A MTD filter design method based on particle swarm algorithm, characterized in that: The following steps are involved: S1: obtaining a Doppler center frequency range and a normalized half width according to the radar type and the clutter type, obtaining constraints including a center frequency point, a side lobe area, a null area, and a main lobe area according to the Doppler center frequency range and the normalized half width, and obtaining the filter information according to the constraints and the global pilot vector matrix; S2: designing a fitness function according to a nonlinear function and a hard-coded threshold for adjusting the filter information; S3: Designing a filter according to the fitness function, and iteratively updating the filter according to the main lobe width dichotomy to obtain an optimal filter.

2. The MTD filter design method based on particle swarm algorithm according to claim 1, characterized in that: In step S1, the filter information is obtained according to the constraint condition and the global pilot vector matrix, further comprising: S11: constructing the global pilot vector matrix according to the filter order and the normalized Doppler frequency full sampling points; S12: Calculate the filter frequency response amplitude according to the constraint condition and the global pilot vector matrix, obtain the frequency response gain according to the filter frequency response amplitude, and obtain the filter information according to the frequency response gain.

3. The MTD filter design method based on particle swarm algorithm according to claim 2, characterized in that: In step S11, the global pilot vector matrix is ​​constructed according to the filter order and the normalized Doppler frequency full sampling points, further comprising: The calculation formula of the row vector of the global pilot vector matrix is ​​as follows: in, is the normalized Doppler frequency full sampling point, Represents a row vector whose elements are the indices of the filter order, that is, , is the order of the filter, the global pilot vector matrix No. The row represents the filter in The complex response vector of frequency points; The global pilot vector matrix is ​​obtained according to the row vector of the global pilot vector matrix, and the global pilot vector matrix The calculation formula is as follows: 。 4. The MTD filter design method based on particle swarm algorithm according to claim 3 is characterized in that: In step S12, the filter frequency response amplitude is calculated according to the constraint condition and the global pilot vector matrix, further comprising: According to the normalized Doppler center frequency Calculate an initial pilot vector, the initial pilot vector The calculation formula is as follows: in, is the imaginary number symbol; A complex weight vector is obtained according to the initial pilot vector, wherein the complex weight vector The calculation formula is as follows: Among them, the complex weight vector is the optimization variable With the initial pilot vector The element-by-element product of the complex weight vector The initial pilot vector information, which includes the filter amplitude, filter phase and filter center frequency, directly affects the frequency response characteristics of the filter; The filter frequency response amplitude is obtained according to the constraint condition, the global pilot vector matrix and the complex weight vector, and the filter frequency response amplitude The calculation formula is as follows: 。 5. The MTD filter design method based on particle swarm algorithm according to claim 4, characterized in that: In step S12, a frequency response gain is obtained according to the frequency response amplitude of the filter, further comprising: The filter frequency response amplitude is normalized to obtain a normal frequency response gain. The calculation formula is as follows: According to the normal frequency response gain Get the frequency response gain, the frequency response gain The calculation formula is as follows: 。 6. The MTD filter design method based on particle swarm algorithm according to claim 5, characterized in that: In step S12, obtaining the filter information according to the frequency response gain further includes: According to the frequency response gain, the maximum level of the side lobe, the maximum level of the null notch and the maximum level of the main lobe are obtained; The maximum sidelobe level of the sidelobe region is calculated according to the frequency response gain, and the maximum sidelobe level The calculation formula is as follows: in, It is the zero-sink area; The maximum level of the null region is calculated according to the frequency response gain. The calculation formula is as follows: in, It is the zero-sink area; The maximum level of the main lobe in the main lobe area is calculated according to the frequency response gain. The calculation formula is as follows: 。 7. The MTD filter design method based on particle swarm algorithm according to claim 6, characterized in that: In step S3, designing a filter according to the fitness function further includes: The fitness function designs the filter by adjusting the sidelobe level and the null level. The calculation formula of the fitness function is as follows: in, is the main lobe level, V is the hard-coded threshold, is the sidelobe level, is the clutter suppression zone level, The side lobe is weakly attenuated as, Attenuation of clutter band.

8. The MTD filter design method based on particle swarm algorithm according to claim 7, characterized in that: In step S3, the optimal filter is obtained by iteratively using the main lobe width dichotomy method, further comprising: The main lobe width corresponding to the unwindowed FFT is used as the lower limit , with the clutter suppression area level added as the main lobe width corresponding to the Taylor window as the upper limit , and give the tolerance ζ, the main lobe width in the first optimization is =( + ) / 2; If the main lobe width meets the requirements in the first optimization, then ; If the main lobe width does not meet the requirements in the first optimization, , and iterate according to the main lobe width dichotomy until - <ζ; The optimal filter is obtained according to the obtained optimal main lobe width.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of claims 1 to 8.

10. A storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by one or more processors, the one or more processors are enabled to perform the steps of the MTD filter design method based on the particle swarm algorithm as described in any one of claims 1 to 8.

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