A PRI dithering radar signal sorting method based on a moss algorithm, a program, a device and a storage medium

Abstract

The present application belongs to the technical field of radar signal sorting, and particularly relates to a PRI jitter radar signal sorting method based on a vine moss algorithm, a program, an equipment and a storage medium. The present application reconstructs TOA difference distribution through overlapping PRI box structure, and combines dynamic time starting point calibration idea to improve the adaptability of the algorithm. The present application designs a vine moss algorithm, adopts Chebyshev mapping to construct population initialization position distribution, introduces a Levy flight strategy to change spore reproduction search mode, and integrates an environmental factor adaptive search mechanism to avoid falling into a local optimal solution, improve global search capability, and improve parameter space coverage. Therefore, the present method can realize PRI jitter radar signal sorting in an unknown environment. The present application adaptively obtains an overlap rate of PRI boxes in the sorting process, an observation time adjustable coefficient, a subharmonic elimination adjustable coefficient and a noise elimination adjustable coefficient, thereby improving the generalization and accuracy of sorting and realizing PRI jitter radar signal sorting.

Description

Technical Field

[0001] This invention belongs to the field of radar signal sorting technology, specifically relating to a PRI jitter radar signal sorting method, program, device and storage medium based on the moss algorithm. Background Technology

[0002] As radar technology evolves, the electromagnetic environment becomes increasingly complex, placing higher demands on radar signal sorting techniques. Existing methods suffer from low sorting accuracy and insufficient generalization capabilities. Pulse sorting of jittery radar signals is a particularly complex problem in radar signal sorting. Therefore, accurately sorting PRI jittery radar signals in unknown environments becomes crucial.

[0003] Existing PRI jitter radar signal sorting methods include dynamic extended correlation method, CDIF, SDIF, PRI transform method, etc. When pulses overlap, these methods are prone to detecting harmonic components and producing a large number of false radiation sources. Furthermore, the parameter settings rely on prior knowledge, resulting in low accuracy and insufficient generalization.

[0004] A search of existing technical literature revealed that Xu Minliang et al.'s invention (patent number: CN202011643542.2) proposed a "CDIF-based jitter signal sorting method" that achieves jitter radar signal sorting through region division and gradient histogram resolution. However, this method requires specifying the detection recurrence interval range and performing region division, resulting in poor sorting ability for pulse sequences with large PRI jitter ranges. Some parameter settings need to be obtained empirically, leading to insufficient generalization. Zhang Chunjie et al.'s invention (patent number: CN202011072664.0) proposed a "PRI jitter radar signal sorting method under pulse loss and aliasing conditions" that improves the PRI overlap box structure to determine the jitter rate of the jitter signal and utilizes correlation confidence to achieve the search and extraction of PRI jitter radar signals under pulse loss and aliasing conditions. However, some parameter settings in this method need to be obtained empirically, resulting in insufficient generalization and affecting the accuracy of the sorting results. In the case of pulse overlap, when sorting PRI jitter radar signals, combining intelligent algorithms to automatically adjust parameters based on the signal environment and sorting results can effectively improve the generalization and accuracy of sorting. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing sorting methods, when sorting PRI jitter radar signal pulses under pulse overlap conditions, suffer from insufficient generalization ability and poor sorting accuracy due to the inability to adaptively adjust parameters. This invention provides a PRI jitter radar signal sorting method, program, device, and storage medium based on the moss algorithm.

[0006] A method for sorting PRI jitter radar signals based on the moss-like algorithm includes the following steps:

[0007] Step 1: Receive radar signals within the sampling time and convert the radar signals into an ordered pulse sequence according to the order of pulse arrival times;

[0008] Step 2: Perform pre-sorting processing on the pulse sequence to obtain preliminary clustering results of the radar signal;

[0009] Step 3: For each pulse sequence in the preliminary clustering results of the radar signal, determine the minimum value, maximum value, and number of PRI bins; using the moss algorithm, take the overlap rate, observation time adjustable coefficient, subharmonic elimination adjustable coefficient, and noise elimination adjustable coefficient of the PRI overlap bins in the pulse main segmentation as optimization targets, construct the population initialization position distribution using Chebyshev mapping, introduce the Levy flight strategy to change the spore reproduction search method, and integrate the environmental factor adaptation search mechanism to enhance the global search capability and obtain the optimal parameter combination;

[0010] Step 4: Based on the optimal parameter combination corresponding to each pulse sequence in the preliminary clustering results of the radar signals, perform pulse main sorting on the preliminary clustering results of the radar signals, determine the PRI value retrieval range of each pulse sequence in the preliminary clustering results of the radar signals, remove the corresponding pulse descriptor from the preliminary clustering results of the radar signals, separate the pulse sequence of each radar, and complete the radar signal sorting.

[0011] Furthermore, in step 1, the radar signal is converted into an ordered pulse sequence pulse_list as follows:

[0012] pulse_list={PDW1,PDW2,...,PDW i ,...,PDW n}

[0013] Among them, PDW i This is the i-th pulse descriptor in the pulse sequence pulse_list, where i = 1, 2, ..., n, and n is the total number of pulses in the pulse sequence; PDW i ={TOA i PW i BW i ,CF i PA i TOA i Let PW be the pulse arrival time of the i-th pulse descriptor word. i BW is the pulse width of the i-th pulse descriptor word. i For the bandwidth of the i-th pulse descriptor, CF i PA is the carrier frequency of the i-th pulse descriptor word.i The pulse amplitude of the i-th pulse descriptor;

[0014] The pre-sorting process of the pulse sequence in step 2 specifically involves:

[0015] Step 2.1: Calculate the PW of all pulse descriptors i BW i and CF i The mean;

[0016]

[0017] Step 2.2: Calculate the PW of all pulse descriptors i BW i and CF i Standard deviation;

[0018]

[0019]

[0020] Step 2.3: Calculate the PW for each pulse descriptor word. i BW i and CF i Standard score;

[0021]

[0022] Step 2.4: Calculate the PW for each pulse descriptor word. i BW i and CF i Normalization results:

[0023]

[0024] Step 2.5: Cluster the normalized results PW_uni, BW_uni, and CF_uni using clustering. Based on the clustering results, further cluster the pulse descriptors PDW in the pulse sequence pulse_list. i Clustering yields preliminary clustering results for the radar signals, pre_res:

[0025] pre_res={pulse_list1,pulse_list2,...,pulse_list ii ,...,pulse_list f}

[0026] Among them, pulse_list iiLet f be the ii-th pulse sequence in the preliminary clustering result of the radar signal, where ii = 1, ..., f, and f is the total number of pulse sequences in the preliminary clustering result of the radar signal; the set of reception times for each pulse sequence in the preliminary clustering result of the radar signal is T, where T = {T1, T2, ..., T...} ii ,...,T f}, T ii is the reception time of the ii-th pulse sequence in the preliminary clustering results of the radar signal.

[0027] Furthermore, in step 3, the specific steps for constructing the population initialization location distribution using the Chebyshev mapping are as follows:

[0028]

[0029] Among them, X u (1) is the initial position of the u-th moss, u = 1, ..., NUM, where NUM is the number of moss in the population; the position of each moss represents a combination of parameters of a set of PRI overlap boxes, including the overlap rate e, the observation time adjustable coefficient α, the subharmonic elimination adjustable coefficient β, and the noise elimination adjustable coefficient γ; R1 is a random number in [0,1]; l_x is the lower bound of the range of moss population position parameters, u_x is the upper bound of the range of moss population position parameters, and h is a constant parameter.

[0030] Furthermore, step 3 introduces the Levy flight strategy to change the spore reproduction and search method, specifically as follows:

[0031] Mosses reproduce and search for spores by dispersing them in the wind. Due to variations in wind speed, the searching mechanisms of mosses differ.

[0032]

[0033] in, Let represent the location of the new *Mossula* after the *u*-th *Mossula* spreads via spores in the *Ite*-th iteration; K is a constant parameter; R2 is a random number in [0,1], R3 is a random number in [0,1]; WF is the wind intensity. MI is the maximum number of iterations; L is the spore dispersal distance coefficient under gentle wind conditions; B is the extension parameter of the Lévy distribution step size; z is a standard normally distributed random variable; μ is the parameter of the Lévy distribution; X best This represents the position of the globally optimal moss individual.

[0034] Furthermore, the specific steps in step 3 are as follows:

[0035] Step 3.1: Set the parameters of the moss vine algorithm, including the number of moss vines in the population (num) and the maximum number of iterations (MI); initialize the current iteration number (Ite) = 1, and construct the initial location distribution X of the population using a Chebyshev map.u (1); Based on the initial position X of each vine moss u (1) Calculate the fitness of each moss and select the position of the moss with the smallest fitness value as the global optimal position X. best ;

[0036] Step 3.2: From X best Randomly select the value X of the vth dimension best,v As a threshold, it is compared with the X locations of all moss vines. u The v-th dimension value X of (Ite) u,v (Ite) comparison, if X u,v (Ite)>X best,v (Ite), then X u (Ite) is added to the set area_m(Ite); if X u,v (Ite)≤X best,v (Ite), then X u (Ite) is added to the set area_l(Ite);

[0037] Step 3.3: Compare the amount of moss in area_m(Ite) and area_l(Ite), and select the set with more moss as area(Ite);

[0038] Step 3.4: Calculate the wind direction (WD);

[0039]

[0040] Where sum(area(Ite)) is the number of all mosses in the set area(Ite);

[0041] Step 3.5: Introduce the Levy flight strategy to change the spore reproduction and search method, and obtain the location of new mosses after each moss has spread through spores. The spore dispersal distance coefficient L under gentle breeze conditions is:

[0042]

[0043] Step 3.6: Integrate an environmental factor adaptation search mechanism to enhance global search capabilities. Determine if the environmental factor EV(Ite) meets the search conditions for sexual reproduction of mosses. If the conditions are met (EV(Ite) ≥ 0.5), perform a sexual reproduction search; otherwise, perform a vegetative reproduction search. Obtain the updated position X of each moss. u (Ite+1);

[0044]

[0045] Wherein, the environmental factor EV(Ite) is a random number between [0,1]; rand and N are both 1×4 random vectors, with each element in rand being a random number between [0,1], and each element in N being a random number distributed according to a standard Gaussian distribution; ⊙ is the operator for multiplying the elements of two vectors; R4 is a random number between [0,1]; Q is the propagation factor. H is a constant parameter;

[0046] Step 3.7: Based on the updated position X of each vine moss u (Ite+1) Calculate the fitness of each moss. If there is a moss at the updated position X... u The fitness of (Ite+1) is less than that of the global optimum position X. best If the fitness is determined, then the global optimal position X is updated. best ;

[0047] Step 3.8: If Ite < MI, then let Ite = Ite + 1 and return to step 3.2; otherwise, output the global optimal position X. best The corresponding parameter combinations.

[0048] Furthermore, in step 3, the pulse sequence pulse_list in the preliminary clustering result of the radar signal is... ii Determine the minimum value τ in the range of PRI values. min (ii) The maximum value τ of the PRI value range max (ii) and the number of PRI boxes F(ii), and τ min (ii) Not greater than pulse_list ii The time corresponding to the starting point, τ max (ii) Not less than pulse_list ii The time corresponding to the endpoint; then the width wid of the PRI box is: The center coordinate τ of the x-th PRI box x For: τ x = (x-0.5)·wid+τ min The width ov_wid of the overlapping portion of the x-th PRI box x For: ov_wid x =e·τ x ; The range of the xth PRI box [C 1x C 2x ]for:

[0049] When obtaining the optimal parameter combination using the moss-based algorithm, the position X of the moss is considered. u The specific method for calculating fitness is as follows:

[0050] Step 4.1: Based on the location X of the mossu ={e,α,β,γ}, to obtain the overlap rate e, observation time adjustable coefficient α, subharmonic elimination adjustable coefficient β, and noise elimination adjustable coefficient γ of the PRI overlap box;

[0051] Step 4.2: Initialize the cumulative spectral values ​​G = {G1, G2, ..., G} of all PRI boxes. x ,...,G F}={0,0,...,0,...,0}, and the time starting point O for all PRI boxes is O={O1,O2,...,O x ,...,O F} = {0,0,...,0,...,0}, and the total number of times each PRI box is used (flag = {flag1,flag2,...,flag}). x ,...,flag F} = {0,0,...,0,...,0}; Initialize j = 2, i = 1;

[0052] Step 4.3: Calculate the arrival time difference ε between the i-th pulse descriptor and the j-th pulse descriptor;

[0053] ε=TOA j -TOA i

[0054] Step 4.4: If ε≥τ max If ε ≤ τ, then let j = j + 1 and execute step 4.5; min If τ min <ε<τ max Then proceed to step 4.7;

[0055] Step 4.5: If j > num(pulse_list) ii If j ≤ num(pulse_list), then proceed to step 4.8; ii If i = j - 1, then return to step 4.3; num(pulse_list) ii ) represents the number of pulses in the ii-th pulse sequence in the pre-sorting results;

[0056] Step 4.6: If i > 1, return to step 4.3; if i ≤ 1, let j = j + 1 and return to step 4.5.

[0057] Step 4.7: Determine the range of the PRI box into which ε falls [C] 1x C 2x ], retrieve the corresponding PRI bin index x; since [C 1x C 2x [C] includes overlapping portions of other PRI boxes, for [C]1x C 2x Update cumulative spectral values ​​G for all PRI bins within the range. x Then, let i = i-1, and return to step 4.6;

[0058] Update cumulative spectral value G x The method is as follows:

[0059] Step 4.7.1: If [C 1x C 2x [Flag indicating the number of times a PRI box can be used within a certain range] x =0, then let flag x =flag x +1, O x =TOA j ;

[0060] Step 4.7.2: For [C] 1x C 2x For all PRI boxes within the range, calculate their initial phase η0, decomposition parameters U and W;

[0061] U = η0 + 0.5,

[0062] Step 4.7.3: If [C 1x C 2x Within a certain range, a PRI box satisfies U = 1 and O x =TOA i If U≥2 and |W|≤ξ, then update its time starting point to O. x =TOA j ξ is a constant coefficient that determines the starting point of time;

[0063] Step 4.7.4: Update [C] 1x C 2x The cumulative spectral value G of all PRI boxes within the range x ;

[0064] G x =G x +D x

[0065] in,

[0066] Step 4.8: Calculate the pulse current density ρ ii ;

[0067]

[0068] Step 4.9: For each PRI box, calculate the detection threshold A. x ; Draw Gx —τ x Discrete points are connected to form curve G. x (t); plot A x —τ x Discrete points are connected to form curve A. x (t); if curve G x The peak value of the hump in (t) G x (τ x )>A x (τ x If the x-coordinate of the hump peak value is obtained, then the hump peak value G is obtained. x (τ x ) corresponding to τ x ;

[0069]

[0070] Step 4.10: Calculate the location X of the moss vine. u The corresponding fitness is fit;

[0071]

[0072] Where s is the total number of x-coordinates of the peak values ​​of each hump; c q a is the x-coordinate value corresponding to the position of the q-th hump peak; q For the left side of the q-th hump, curve G x (t) and curve A x (t) The x-coordinate value corresponding to the intersection point; b q To the right of the q-th hump, curve G x (t) and curve A x (t) The x-coordinate value corresponding to the intersection point.

[0073] Furthermore, in step 4, each pulse sequence pulse_list in the preliminary clustering result pre_res of the radar signal is... ii The corresponding optimal parameter combination, according to steps 4.1 to 4.9, uses the horizontal axis coordinate of the detected hump peak as the PRI estimate, and sets the deviation range ψ above and below the PRI estimate as the PRI value retrieval range, from the pulse sequence pulse_list. ii Remove the corresponding pulse descriptor from the list; for each pulse sequence pulse_list in the preliminary clustering result pre_res of the radar signal,... ii By performing the above operations, the pulse sequence of each radar is separated, different radar signals are distinguished, and radar signal sorting is completed.

[0074] A computer device / equipment / system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the PRI jitter radar signal sorting method based on the moss algorithm described above.

[0075] A computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the PRI jitter radar signal sorting method based on the moss algorithm described above.

[0076] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps of the PRI jitter radar signal sorting method based on the moss algorithm described above.

[0077] The beneficial effects of this invention are as follows:

[0078] This invention reconstructs the TOA difference distribution through overlapping PRI bin structures and combines it with the concept of dynamic time start calibration to improve the algorithm's adaptability. This invention designs a moss-based algorithm, employing Chebyshev mapping to construct the population initialization position distribution, introducing a Lévy flight strategy to change the spore reproduction search method, and incorporating an environmental factor adaptation search mechanism to avoid getting trapped in local optima, thereby improving global search capability and parameter space coverage. This enables the method to achieve PRI jitter radar signal sorting even in unknown environments. This invention adaptively obtains the PRI bin overlap rate, observation time adjustable coefficient, subharmonic elimination adjustable coefficient, and noise elimination adjustable coefficient during the sorting process, thereby improving the generalization and accuracy of sorting and achieving PRI jitter radar signal sorting. Attached Figure Description

[0079] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0080] Figure 2 This is a schematic diagram of the moss algorithm in this invention. Detailed Implementation

[0081] The present invention will now be further described with reference to the accompanying drawings.

[0082] This invention designs a PRI jitter radar signal sorting method based on the *Mossia lataniae* algorithm. It primarily addresses the problem of insufficient generalization ability and poor sorting accuracy in existing sorting methods when sorting PRI jitter radar signal pulses under pulse overlap conditions, due to the inability to adaptively adjust parameters. This invention utilizes Chebyshev mapping to initialize the population's location distribution, combines it with the Levy flight strategy to modify the *Mossia lataniae* spore reproduction search method, incorporates an environmental factor adaptation search mechanism, enhances global search capability, and adaptively obtains the PRI bin overlap rate, observation time adjustable coefficient, subharmonic elimination adjustable coefficient, and noise elimination adjustable coefficient during the sorting process. This improves the generalization and accuracy of the sorting, achieving PRI jitter radar signal sorting.

[0083] As attached Figure 1 The diagram shown is a schematic flowchart of the PRI jitter radar signal sorting method based on the moss algorithm of this invention.

[0084] A method for sorting PRI jitter radar signals based on the moss-like algorithm includes the following steps:

[0085] Step 1: Receive radar signals within the sampling time and convert the radar signals into an ordered pulse sequence pulse_list according to the order of pulse arrival times;

[0086] pulse_list={PDW1,PDW2,...,PDW i ,...,PDW n}

[0087] Among them, PDW i This is the i-th pulse descriptor in the pulse sequence pulse_list, where i = 1, 2, ..., n, and n is the total number of pulses in the pulse sequence; PDW i ={TOA i PW i BW i ,CF i PA i TOA i Let PW be the pulse arrival time of the i-th pulse descriptor word. i BW is the pulse width of the i-th pulse descriptor word. i For the bandwidth of the i-th pulse descriptor, CF i PA is the carrier frequency of the i-th pulse descriptor word. i The pulse amplitude of the i-th pulse descriptor;

[0088] Step 2: Perform pre-sorting processing on the pulse sequence to obtain preliminary clustering results of the radar signal;

[0089] Step 2.1: Calculate the PW of all pulse descriptors i BWi and CF i The mean;

[0090]

[0091] Step 2.2: Calculate the PW of all pulse descriptors i BW i and CF i Standard deviation;

[0092]

[0093] Step 2.3: Calculate the PW for each pulse descriptor word. i BW i and CF i Standard score;

[0094]

[0095] Step 2.4: Calculate the PW for each pulse descriptor word. i BW i and CF i Normalization results:

[0096]

[0097] Step 2.5: Cluster the normalized results PW_uni, BW_uni, and CF_uni using clustering. Based on the clustering results, further cluster the pulse descriptors PDW in the pulse sequence pulse_list. i Clustering yields preliminary clustering results for the radar signals, pre_res:

[0098] pre_res={pulse_list1,pulse_list2,...,pulse_list ii ,...,pulse_list f}

[0099] Among them, pulse_list ii Let f be the ii-th pulse sequence in the preliminary clustering result of the radar signal, where ii = 1, ..., f, and f is the total number of pulse sequences in the preliminary clustering result of the radar signal; the set of reception times for each pulse sequence in the preliminary clustering result of the radar signal is T, where T = {T1, T2, ..., T...} ii ,...,T f}, T ii The reception time of the ii-th pulse sequence in the preliminary clustering results of the radar signal;

[0100] Step 3: For each pulse sequence in the preliminary clustering results of the radar signal, determine the minimum value, maximum value, and number of PRI bins; using the moss algorithm, take the overlap rate, observation time adjustable coefficient, subharmonic elimination adjustable coefficient, and noise elimination adjustable coefficient of the PRI overlap bins in the pulse main segmentation as optimization targets, construct the population initialization position distribution using Chebyshev mapping, introduce the Levy flight strategy to change the spore reproduction search method, and integrate the environmental factor adaptation search mechanism to enhance the global search capability and obtain the optimal parameter combination;

[0101] Step 3.1: Set the parameters of the moss vine algorithm, including the number of moss vines in the population (num) and the maximum number of iterations (MI); initialize the current iteration number (Ite) = 1, and construct the initial location distribution X of the population using a Chebyshev map. u (1); Based on the initial position X of each vine moss u (1) Calculate the fitness of each moss and select the position of the moss with the smallest fitness value as the global optimal position X. best ;

[0102]

[0103] Among them, X u (1) is the initial position of the u-th moss, u = 1, ..., NUM, where NUM is the number of moss in the population; the position of each moss represents a combination of parameters of a set of PRI overlap boxes, including the overlap rate e, the observation time adjustable coefficient α, the subharmonic elimination adjustable coefficient β, and the noise elimination adjustable coefficient γ; R1 is a random number in [0,1]; l_x is the lower bound of the range of moss population position parameters, u_x is the upper bound of the range of moss population position parameters, and h is a constant parameter;

[0104] In one embodiment of the present invention, h can be set to 4, the number of populations NUM can be set to 30, the population dimension dim can be set to 3, and the maximum number of iterations MI can be set to 500.

[0105] Step 3.2: From X best Randomly select the value X of the vth dimension best,v As a threshold, it is compared with the X locations of all moss vines. u The v-th dimension value X of (Ite) u,v (Ite) comparison, if X u,v (Ite)>X best,v (Ite), then X u (Ite) is added to the set area_m(Ite); if X u,v (Ite)≤X best,v (Ite), then X u (Ite) is added to the set area_l(Ite);

[0106] Step 3.3: Compare the amount of moss in area_m(Ite) and area_l(Ite), and select the set with more moss as area(Ite);

[0107] Step 3.4: Calculate the wind direction (WD);

[0108]

[0109] Where sum(area(Ite)) is the number of all mosses in the set area(Ite);

[0110] Step 3.5: Introduce the Levy flight strategy to change the spore reproduction and search method, and obtain the location of new mosses after each moss has spread through spores. The spore dispersal distance coefficient L under gentle breeze conditions is:

[0111] Mosses reproduce and search for spores by dispersing them in the wind. Due to variations in wind speed, the searching mechanisms of mosses differ.

[0112]

[0113] in, Let represent the location of the new *Mossula* after the *u*-th *Mossula* spreads via spores in the *Ite*-th iteration; K is a constant parameter; R2 is a random number in [0,1], R3 is a random number in [0,1]; WF is the wind intensity. MI is the maximum number of iterations; B is the extension parameter of the Lévy distribution step size; z is a standard normally distributed random variable; μ is the parameter of the Lévy distribution; X best The location of the globally optimal moss individual;

[0114] In one embodiment of the present invention, K may be set to 3.

[0115] L is the spore dispersal distance coefficient under gentle wind conditions:

[0116]

[0117] Step 3.6: Integrate an environmental factor adaptation search mechanism to enhance global search capabilities. Determine if the environmental factor EV(Ite) meets the search conditions for sexual reproduction of mosses. If the conditions are met (EV(Ite) ≥ 0.5), perform a sexual reproduction search; otherwise, perform a vegetative reproduction search. Obtain the updated position X of each moss. u (Ite+1);

[0118]

[0119] Wherein, the environmental factor EV(Ite) is a random number between [0,1]; rand and N are both 1×4 random vectors, with each element in rand being a random number between [0,1], and each element in N being a random number distributed according to a standard Gaussian distribution; ⊙ is the operator for multiplying the elements of two vectors; R4 is a random number between [0,1]; Q is the propagation factor. H is a constant parameter;

[0120] In one embodiment of the present invention, H may be set to 2.

[0121] Step 3.7: Based on the updated position X of each vine moss u (Ite+1) Calculate the fitness of each moss. If there is a moss at the updated position X... u The fitness of (Ite+1) is less than that of the global optimum position X. best If the fitness is determined, then the global optimal position X is updated. best ;

[0122] Step 3.8: If Ite < MI, then let Ite = Ite + 1 and return to step 3.2; otherwise, output the global optimal position X. best The corresponding parameter combinations;

[0123] Step 4: Based on the optimal parameter combination corresponding to each pulse sequence in the preliminary clustering results of the radar signals, perform pulse main sorting on the preliminary clustering results of the radar signals, determine the PRI value retrieval range of each pulse sequence in the preliminary clustering results of the radar signals, remove the corresponding pulse descriptor from the preliminary clustering results of the radar signals, separate the pulse sequence of each radar, and complete the radar signal sorting.

[0124] The pulse_list is the i-th pulse sequence in the preliminary clustering results of radar signals. ii Determine the minimum value τ in the range of PRI values. min (ii) The maximum value τ of the PRI value range max (ii) and the number of PRI boxes F(ii), and τ min (ii) Not greater than pulse_list ii The time corresponding to the starting point, τ max (ii) Not less than pulse_list ii The time corresponding to the endpoint; then the width wid of the PRI box is: The center coordinate τ of the x-th PRI box x For: τ x = (x-0.5)·wid+τ min The width ov_wid of the overlapping portion of the x-th PRI box x For: ov_wid x =e·τx ; The range of the xth PRI box [C 1x C 2x ]for:

[0125] Step 4.1: Based on the location X of the moss u ={e,α,β,γ}, to obtain the overlap rate e, observation time adjustable coefficient α, subharmonic elimination adjustable coefficient β, and noise elimination adjustable coefficient γ of the PRI overlap box;

[0126] Step 4.2: Initialize the cumulative spectral values ​​G = {G1, G2, ..., G} of all PRI boxes. x ,...,G F}={0,0,...,0,...,0}, and the time starting point O for all PRI boxes is O={O1,O2,...,O x ,...,O F} = {0,0,...,0,...,0}, and the total number of times each PRI box is used (flag = {flag1,flag2,...,flag}). x ,...,flag F} = {0,0,...,0,...,0}; Initialize j = 2, i = 1;

[0127] Step 4.3: Calculate the arrival time difference ε between the i-th pulse descriptor and the j-th pulse descriptor;

[0128] ε=TOA j -TOA i

[0129] Step 4.4: If ε≥τ max If ε ≤ τ, then let j = j + 1 and execute step 4.5; min If τ min <ε<τ max Then proceed to step 4.7;

[0130] Step 4.5: If j > num(pulse_list) ii If j ≤ num(pulse_list), then proceed to step 4.8; ii If i = j - 1, then return to step 4.3; num(pulse_list) ii ) represents the number of pulses in the ii-th pulse sequence in the pre-sorting results;

[0131] Step 4.6: If i > 1, return to step 4.3; if i ≤ 1, let j = j + 1 and return to step 4.5.

[0132] Step 4.7: Determine the range of the PRI box into which ε falls [C] 1x C 2x ], retrieve the corresponding PRI bin index x; since [C 1x C 2x [C] includes overlapping portions of other PRI boxes, for [C] 1x C 2x Update cumulative spectral values ​​G for all PRI bins within the range. x Then, let i = i-1, and return to step 4.6;

[0133] Update cumulative spectral value G x The method is as follows:

[0134] Step 4.7.1: If [C 1x C 2x [Flag indicating the number of times a PRI box can be used within a certain range] x =0, then let flag x =flag x +1, O x =TOA j ;

[0135] Step 4.7.2: For [C] 1x C 2x For all PRI boxes within the range, calculate their initial phase η0, decomposition parameters U and W;

[0136] U = η0 + 0.5,

[0137] Step 4.7.3: If [C 1x C 2x Within a certain range, a PRI box satisfies U = 1 and O x =TOA i If U≥2 and |W|≤ξ, then update its time starting point to O. x =TOA j ξ is a constant coefficient that determines the starting point of time;

[0138] In one embodiment of the present invention, ξ can be taken as 0.02.

[0139] Step 4.7.4: Update [C] 1x C 2x The cumulative spectral value G of all PRI boxes within the range x ;

[0140] G x =G x +D x

[0141] in,

[0142] Step 4.8: Calculate the pulse current density ρ ii ;

[0143]

[0144] Step 4.9: For each PRI box, calculate the detection threshold A. x ; Draw G x —τ x Discrete points are connected to form curve G. x (t); plot A x —τ x Discrete points are connected to form curve A. x (t); if curve G x The peak value of the hump in (t) G x (τ x )>A x (τ x If the x-coordinate of the hump peak value is obtained, then the hump peak value G is obtained. x (τ x ) corresponding to τ x ;

[0145]

[0146] Based on the preliminary clustering results of the radar signals, each pulse sequence pulse_list in pre_res ii The corresponding optimal parameter combination, according to steps 4.1 to 4.9, uses the horizontal axis coordinate of the detected hump peak as the PRI estimate, and sets the deviation range ψ above and below the PRI estimate as the PRI value retrieval range, from the pulse sequence pulse_list. ii Remove the corresponding pulse descriptor from the list; for each pulse sequence pulse_list in the preliminary clustering result pre_res of the radar signal,... ii By performing the above operations, the pulse sequence of each radar is separated, different radar signals are distinguished, and radar signal sorting is completed.

[0147] In one embodiment of the present invention, ψ may be set to 0.2.

[0148] In the moss algorithm, based on the position X of the moss... u The specific method for calculating fitness is as follows: First, execute steps 4.1 to 4.9, and then calculate as follows:

[0149]

[0150] Where s is the total number of x-coordinates of the peak values ​​of each hump; c qa is the x-coordinate value corresponding to the position of the q-th hump peak; q For the left side of the q-th hump, curve G x (t) and curve A x (t) The x-coordinate value corresponding to the intersection point; b q To the right of the q-th hump, curve G x (t) and curve A x (t) The x-coordinate value corresponding to the intersection point.

[0151] This invention reconstructs the TOA difference distribution through overlapping PRI box structures and combines it with the concept of dynamic time start calibration to improve the algorithm's adaptability. Simultaneously, a moss-based algorithm is designed, employing Chebyshev mapping to construct the population initialization position distribution, introducing the Lévy flight strategy to alter the spore reproduction search method, and incorporating an environmental factor adaptation search mechanism to avoid getting trapped in local optima, thereby improving global search capability and parameter space coverage. This enables the method to achieve PRI jitter radar signal sorting even in unknown environments.

[0152] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A PRI jitter radar signal sorting method based on the moss-like algorithm, characterized in that, Includes the following steps: Step 1: Receive radar signals within the sampling time and convert the radar signals into an ordered pulse sequence according to the order of pulse arrival times; Step 2: Perform pre-sorting processing on the pulse sequence to obtain preliminary clustering results of the radar signal; Step 3: For each pulse sequence in the preliminary clustering results of the radar signal, determine the minimum value of PRI, the maximum value of PRI, and the number of PRI boxes; The vine moss algorithm is adopted, and the overlap rate, observation time adjustable coefficient, subharmonic elimination adjustable coefficient, and noise elimination adjustable coefficient of the PRI overlap box in the pulse main sorting are used as optimization targets. Chebyshev mapping is used to construct the population initialization position distribution, Levy flight strategy is introduced to change the spore reproduction search method, and environmental factor adaptation search mechanism is integrated to enhance the global search capability and obtain the optimal parameter combination. Step 4: Based on the optimal parameter combination corresponding to each pulse sequence in the preliminary clustering results of the radar signals, perform pulse main sorting on the preliminary clustering results of the radar signals, determine the PRI value retrieval range of each pulse sequence in the preliminary clustering results of the radar signals, remove the corresponding pulse descriptor from the preliminary clustering results of the radar signals, separate the pulse sequence of each radar, and complete the radar signal sorting.

2. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 1, characterized in that: In step 1, the radar signal is converted into an ordered pulse sequence. for: in, pulse sequence The Each pulse description word , The total number of pulses in the pulse sequence; , For the first The pulse arrival time of each pulse description word. For the first The pulse width of each pulse description word. For the first The bandwidth of a pulse descriptor word For the first The carrier frequency of each pulse description word For the first The pulse amplitude of each pulse description word; The pre-sorting process of the pulse sequence in step 2 specifically involves: Step 2.1: Calculate the values ​​of all pulse descriptors. , and The mean; Step 2.2: Calculate the values ​​of all pulse descriptors. , and Standard deviation; Step 2.3: Calculate the value of each pulse descriptor word. , and Standard score; Step 2.4: Calculate the value of each pulse descriptor word. , and Normalization results: Step 2.5: Normalize the results , and Clustering is performed to separate the pulse sequences based on the clustering results. pulse description word in Clustering was performed to obtain preliminary clustering results for the radar signals. : in, The first in the preliminary clustering results of radar signals A pulse sequence, , To obtain the total number of pulse sequences in the preliminary clustering results of the radar signal; the set of reception times for each pulse sequence in the preliminary clustering results of the radar signal is as follows: , , The first in the preliminary clustering results of radar signals The reception time of each pulse sequence.

3. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 2, characterized in that: In step 3, the Chebyshev mapping is used to construct the population initialization location distribution, specifically as follows: in, For the first The initial location of the moss vine. , The number of mosses in the population; the position of each moss represents the overlap rate of a set of PRI overlap boxes. Observation time adjustable coefficient Elimination of subharmonic adjustable coefficient and noise cancellation adjustable coefficient Parameter combinations; for Random numbers; This represents the lower bound of the range of location parameters for the moss community. This represents the upper bound of the range of location parameters for the moss community. It is a constant parameter.

4. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 3, characterized in that: Step 3 introduces the Levy flight strategy to change the spore reproduction search method, specifically as follows: Mosses reproduce and search for spores by dispersing them in the wind. Due to variations in wind speed, the searching mechanisms of mosses differ. in, In the first In the nth iteration The location of new moss after the initial moss spreads through spores; It is a constant parameter; for random numbers, for Random numbers; As for the intensity of the wind, , This represents the maximum number of iterations. The coefficient for spore dispersal distance under gentle wind conditions; This is an extended parameter for the Lévy distribution step size; It is a standard normally distributed random variable; represents the parameters of the Lévy distribution; This represents the position of the globally optimal moss individual.

5. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 4, characterized in that: The specific steps in step 3 are as follows: Step 3.1: Set the parameters for the moss algorithm, including the number of moss plants in the population. and maximum number of iterations Initialize the current iteration count. The population initialization location distribution is constructed using Chebyshev mapping. Based on the initial position of each vine and moss Calculate the fitness of each moss, and select the position of the moss with the smallest fitness value as the global optimum. ; Step 3.2: From Randomly select the first Dimension As a threshold, it was compared with all moss locations. The Dimension In comparison, if Then Add to collection ;like Then Add to collection ; Step 3.3: Comparison and The number of *Moss fasciatus* was selected, and the set with the largest number of *Moss fasciatus* was chosen as the basis for further analysis. ; Step 3.4: Calculate wind direction ; in, For set The total number of all mosses in the plant; Step 3.5: Introduce the Levy flight strategy to change the spore reproduction and search method, and obtain the location of new mosses after each moss has spread through spores. ; Spore dispersal distance coefficient under gentle wind conditions for: Step 3.6: Integrate an environmental factor adaptation search mechanism to enhance global search capabilities and determine environmental factors. Does it meet the search criteria for sexual reproduction of moss? If it does meet the criteria... If sexual reproduction is used, a search will be performed; otherwise, a search will be performed for vegetative reproduction to obtain the updated locations of each vine and moss. ; Among them, environmental factors for Random numbers between; and All random vectors, The elements in the middle are random numbers, Each element in the set is a random number from a standard Gaussian distribution; The operator for calculating the element-wise product of two vectors; for Random numbers; As a propagation factor, ; It is a constant parameter; Step 3.7: Based on the updated positions of each vine and moss Calculate the fitness of each lichen; if there is a location where the lichen has been renewed... The fitness is less than the global optimum position. If the fitness is determined, then the global optimum position is updated. ; Step 3.8: If Then let If the condition is not met, return to step 3.2; otherwise, output the globally optimal position. The corresponding parameter combinations.

6. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 5, characterized in that: In step 3, the first step is to analyze the preliminary clustering results of the radar signals. pulse sequence Determine the minimum value in the range of PRI. PRI value range and maximum value and the number of PRI boxes ,and Not greater than The starting point corresponds to the time. Not less than The time corresponding to the endpoint; then the width of the PRI box. for: ;No. The center coordinates of each PRI box for: , No. Width of the overlapping portion of the PRI boxes for: ;No. The range of PRI boxes for: , ; When obtaining the optimal parameter combination using the moss-based algorithm, the location of the moss is considered. The specific method for calculating fitness is as follows: Step 4.1: Based on the location of the moss Obtain the overlap rate of the PRI overlap boxes. Observation time adjustable coefficient Elimination of subharmonic adjustable coefficient and noise cancellation adjustable coefficient ; Step 4.2: Initialize the cumulative spectral values ​​of all PRI boxes. The starting point of time for all PRI boxes Number of times all PRI boxes were used Initialization command , ; Step 4.3: Calculate the first... The pulse description word and the first Time difference of arrival of each pulse descriptor word ; Step 4.4: If Then let Proceed to step 4.5; if Then let Proceed to step 4.6; if Then proceed to step 4.7; Step 4.5: If If so, proceed to step 4.8; if Then let Return to step 4.3; The first in the pre-sorting results The number of pulses in a pulse sequence; Step 4.6: If If so, return to step 4.3; if Then let Return to step 4.5; Step 4.7: Determine The range of the PRI box that fell in Get the corresponding PRI box index. ;because This includes the overlapping portions of other PRI boxes, for Update cumulative spectral values ​​for all PRI boxes within the range Afterwards, order Return to step 4.6; Update cumulative spectral values The method is as follows: Step 4.7.1: If Number of times a PRI box is used within the range Then let , ; Step 4.7.2: For Calculate the initial phase of all PRI boxes within the range. Decomposition parameters and ; ， ， Step 4.7.3: If A certain PRI box within the range satisfies and or and Then update its starting time to ; The constant coefficient that determines the starting point of time; Step 4.7.4: Update Cumulative spectral values ​​of all PRI boxes within the range ; in, ; Step 4.8: Calculate the pulse current density ; Step 4.9: For each PRI box, calculate the detection threshold. ;draw — Discrete points are identified, and connecting these discrete points forms a curve. ;draw — Discrete points are identified, and connecting these discrete points forms a curve. If the curve peak value of camel hump Then obtain the x-coordinate of the hump peak value, that is, the hump peak value. corresponding ; Step 4.10: Calculate the location of the moss Corresponding fitness ; in, The total number of x-coordinates of the peak values ​​of each hump; For the first The horizontal coordinate value corresponding to the location of the peak value of each hump; For the first On the left side of the camel hump, the curve With curve The x-coordinate value corresponding to the intersection point; For the first On the right side of the camel hump, the curve With curve The x-coordinate value corresponding to the intersection point.

7. The PRI jitter radar signal sorting method based on the moss-like algorithm according to claim 6, characterized in that: In step 4, based on the preliminary clustering results of the radar signals Each pulse sequence The corresponding optimal parameter combination, according to steps 4.1 to 4.9, uses the horizontal axis coordinate of the detected hump peak value as the PRI estimate, and sets the upper and lower deviation range of the PRI estimate. As the PRI value retrieval range, from pulse sequences Remove the corresponding pulse descriptor from the data; for the preliminary clustering results of radar signals... Each pulse sequence By performing the above operations, the pulse sequence of each radar is separated, different radar signals are distinguished, and radar signal sorting is completed.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When executed by a processor, the computer instructions implement the steps of the method according to any one of claims 1 to 7.

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