A multi-level histogram sorting method and device of a signal, and an electronic device

Abstract

The application discloses a multi-stage histogram sorting scheme of a signal and belongs to the field of counter reconnaissance, and comprises the following steps: acquiring vectors of radar signal arrival time constitutions to be sorted, and taking each vector as a sample point; recording the serial number of each sample point as a first vector in the order of arrival time from small arrival, and performing initialization; performing cyclic processing on the first vector, when the histogram stage number corresponding to the first vector after updating, and whether the number of times of meeting the significance meet the first preset condition, extracting the elements of the repeated interval vector after updating in the order from high to low in the element of the counting vector after updating to form a PRI characteristic value vector, taking the PRI characteristic value vector as a target repeated interval characteristic value vector; sequentially searching the pulse sequence corresponding to each PRI characteristic value in the target repeated interval characteristic value vector and filtering out the PRI characteristic value with the total number less than a second threshold; and determining a sorted pulse sequence based on the remaining PRI characteristic value, so that the process of judging the rationality of the PRI through pulse sequence searching can be simplified.

Description

Technical Field

[0001] This invention relates to the field of counter-reconnaissance technology, and in particular to a multi-level histogram sorting method and apparatus for signals, and electronic equipment. Background Technology

[0002] Radar signal sorting is the process of separating pulses belonging to different radar radiation sources from a dense stream of intercepted radar pulses (i.e., full pulses, each pulse represented by a pulse descriptor word PDW). It is the foundation of radar electronic countermeasures intelligence analysis. Only by sorting the randomly overlapping pulse stream into individual pulse sequences of each radar can the characteristic parameters of the radar be accurately measured and analyzed in detail, thereby determining the functional purpose, platform type, threat level, and other attributes of these radars, and accurately jamming enemy threat radiation sources.

[0003] Currently, most radar signal sorting methods revolve around five key parameters: Time of Arrival (TOA), Carrier Frequency (RF), Pulse Width (PW), Pulse Amplitude (PA), and Direction of Arrival (DOA). Pre-sorting methods primarily utilize the carrier frequency, azimuth, and pulse width dimensions of the signal. They divide the space spanned by the signal's characteristic parameters into multiple sorting subspaces, projecting the pulse sets to be sorted onto these subspaces to dilute the pulse stream. Main sorting mainly employs algorithms based on pulse repetition interval deinterleaving, such as the Cumulative Difference Histogram (CDIF) method, the Sequential Difference Histogram (SDIF) method, and the PRI transform method. The CDIF algorithm estimates the potential PRI value by comparing the accumulated results of each level of difference histogram with the detection threshold, and then confirms the PRI value and extracts the pulse sequence of a single radar through sequence retrieval. SDIF improves upon the CDIF algorithm, also including PRI determination and sequence retrieval. Its basic principle is to compare the statistical results of the difference histograms at each level with the detection threshold to estimate potential PRI values. Then, sequence retrieval confirms the PRI value and extracts the pulse sequence of a single radar. SDIF increases its adaptability to random jitter within a certain PRI error range. If the PRI jitter is less than the tolerance, sequence retrieval is performed at the SDIF group center value exceeding the threshold. The PRI transform method, in handling integer multiples of PRI harmonics, almost completely suppresses subharmonics appearing in the autocorrelation function by introducing the periodic relationship of trigonometric functions. However, compared to CDIF and SDIF algorithms, PRI transform is only suitable for handling fixed PRI types with small jitter, because the phase error caused by PRI jitter increases with distance from the time starting point of the transform, making the PRI spectrum flatter. All three methods require close integration with the sequence search method, continuously judging the possible values ​​of PRIs that show peaks in the spectrum, and using the number of searched pulse sequences to help determine the reasonableness of the found PRI, making the process cumbersome. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-level histogram sorting method, apparatus, and electronic device for signals, which can solve the problem that existing methods such as CDIF, SDIF, and PRI transform rely too much on searching pulse sequences to help determine the rationality of PRI feature values.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] This invention provides a multi-level histogram sorting method for signals, wherein the method includes:

[0007] Obtain the vector composed of the arrival times of the radar signals to be sorted, and use each vector as a sample point;

[0008] The sequence number of each sample point is recorded as the first vector according to the order of arrival time from smallest to largest, and initialization is performed. The initialization sets the histogram level to 1, satisfies the saliency product of times being 0, and records the repetition interval vector and the counting vector as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repetition interval vector.

[0009] The first vector is processed cyclically according to a preset rule to obtain the updated first vector, the repetition interval vector, and the counting vector.

[0010] Determine whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition.

[0011] If the first preset condition is met, determine whether the number of times the significance is satisfied is 2;

[0012] If so, extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is then used as the target repeating interval feature vector.

[0013] Using a sequence search method, the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector is sequentially searched;

[0014] PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold are filtered out.

[0015] The sorting pulse sequence is determined based on the remaining PRI feature values.

[0016] The first preset condition includes at least one of the following: the histogram level is greater than a first threshold, and the number of times the significance is satisfied is greater than 1.

[0017] Optionally, after the step of determining whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition, the method further includes:

[0018] If the first preset condition is not met, the first vector is differentially divided by an interval step size of t to obtain possible values ​​of the repetition interval.

[0019] Filter out possible values ​​of the repetition interval that are less than the third threshold;

[0020] Within the tolerance range, the histograms are plotted. Histograms with a repetition interval occurrence greater than 2 are arranged in ascending order of occurrence, forming an updated repetition interval vector and an updated count vector.

[0021] Based on the length of the updated repetition interval vector, a processing strategy for the updated counting vector and the repetition interval vector is determined.

[0022] Optionally, the step of determining the processing strategy for the updated counting vector and the repeating interval vector based on the length of the updated repeating interval vector includes:

[0023] If the length of the updated repeating interval vector meets the second preset condition, the first processing strategy is executed;

[0024] If the length of the updated repeating interval vector meets the third preset condition, the second processing strategy is executed;

[0025] If the length of the updated repeating interval vector meets the fourth preset condition, the third processing strategy is executed.

[0026] Optionally, if the length of the updated repetition interval vector satisfies the fourth preset condition, the steps for executing the third processing strategy include:

[0027] If the length of the updated repeating interval vector meets the fourth preset condition, the elements in the updated repeating interval vector that have a count less than the median of the counting sequence are deleted in a loop to update the repeating interval vector and the counting vector. The loop stops when the length of the latest repeating interval vector meets the fourth preset condition, and the process jumps to the execution flow corresponding to the second processing strategy.

[0028] Optionally, the step of determining the sorting pulse sequence based on the remaining PRI feature values ​​includes:

[0029] Determine whether the intervals between the remaining PRI feature values ​​are all greater than or equal to the search tolerance;

[0030] If not, the average value of the remaining PRI feature value intervals is used as the updated search tolerance to search for the corresponding sorting pulse sequence.

[0031] This invention also provides a multi-level histogram sorting device for signals, wherein the device includes:

[0032] The acquisition module is used to acquire a vector composed of the arrival times of the radar signals to be sorted, and to use each vector as a sample point.

[0033] An initialization module is used to record the sequence number of each sample point as a first vector according to the order of arrival time from smallest to largest, and to perform initialization. The initialization sets the histogram level to 1, satisfies the saliency product of times being 0, and records the repetition interval vector and the counting vector as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repetition interval vector.

[0034] The update module is used to process the first vector cyclically according to a preset rule to obtain the updated first vector, the repetition interval vector, and the counting vector.

[0035] The first judgment module is used to determine whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition.

[0036] The second judgment module is used to determine whether the number of times the significance is satisfied is 2, provided that the first preset condition is met.

[0037] The execution module is used to extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is then used as the target repeating interval feature vector.

[0038] The search module is used to sequentially search for the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector using a sequence search method;

[0039] The first filtering module is used to filter out PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold.

[0040] The sequence determination module is used to determine the sorting pulse sequence based on the remaining PRI feature values;

[0041] The first preset condition includes at least one of the following: the histogram level is greater than a first threshold, and the number of times the significance is satisfied is greater than 1.

[0042] Optionally, the device further includes:

[0043] The difference module is used to perform a difference of the first vector with an interval step size of t after the first judgment module judges whether the histogram level and the number of times the significance is satisfied meet the first preset condition. If the first preset condition is not satisfied, the module obtains the possible value of the repetition interval by performing a difference of the first vector with an interval step size of t.

[0044] The second filtering module is used to filter out possible values ​​of the repetition interval that are less than the third threshold.

[0045] The statistics module is used to generate histograms within the tolerance range. Histograms with a repetition interval occurrence greater than 2 are arranged in ascending order of occurrence, forming an updated repetition interval vector and an updated count vector.

[0046] The strategy determination module is used to determine the processing strategy for the updated counting vector and the repeating interval vector based on the length of the updated repeating interval vector.

[0047] Optionally, the strategy determination module includes:

[0048] The first submodule is used to execute the first processing strategy when the length of the updated repeating interval vector meets the second preset condition.

[0049] The second submodule is used to execute the second processing strategy if the length of the updated repeating interval vector meets the third preset condition.

[0050] The third submodule is used to execute the third processing strategy if the length of the updated repeating interval vector meets the fourth preset condition.

[0051] Optionally, the third submodule is specifically used for:

[0052] If the length of the updated repeating interval vector meets the fourth preset condition, the elements in the updated repeating interval vector that have a count less than the median of the counting sequence are deleted in a loop to update the repeating interval vector and the counting vector. The loop stops when the length of the latest repeating interval vector meets the fourth preset condition, and the process jumps to the execution flow corresponding to the second processing strategy.

[0053] Optionally, the sequence determination module includes:

[0054] The fourth submodule is used to determine whether the spacing between the remaining PRI feature values ​​is greater than or equal to the search tolerance.

[0055] The fifth submodule is used to search for the corresponding sorting pulse sequence if, no, the average value of the remaining PRI feature value spacing is used as the updated search tolerance.

[0056] This invention provides an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the multi-level histogram sorting method for any of the above-described signals.

[0057] This invention provides a readable storage medium storing a program or instructions, which, when executed by a processor, implement the steps of the multi-level histogram sorting method for any of the above-described signals.

[0058] The multi-level histogram sorting scheme for signals provided in this embodiment of the invention obtains a vector composed of the arrival times of the radar signals to be sorted, and uses each vector as a sample point; the sequence number of each sample point is recorded as a first vector according to the order of arrival time from smallest to largest, and initialization is performed; the first vector is processed cyclically according to a preset rule to obtain an updated first vector, a repetition interval vector, and a counting vector; it is determined whether the histogram level and the number of times the significance is satisfied by the updated first vector meet a first preset condition; if the first preset condition is met, it is determined whether the number of times the significance is satisfied is 2; if so, the elements of the updated repetition interval vector are extracted in descending order of the elements in the updated counting vector to form a PRI feature value vector, which is used as the target repetition interval feature value vector; the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector is searched sequentially using a sequence search method; PRI feature values ​​whose total number of corresponding pulse sequences is less than a second threshold are filtered out; and the sorting pulse sequence is determined based on the remaining PRI feature values. The multi-level histogram sorting scheme for signals provided in this invention first extracts potential PRI feature values ​​from each level of histogram using methods such as tolerance and frequency doubling. Then, it performs multi-level counting accumulation and simplification. Finally, it determines the significance condition of the PRI feature values ​​in the multi-level counting graph. If the significance condition is met twice, the process of increasing the number of levels is terminated. If the significance condition is not met in any consecutive specific levels, the process of increasing the number of levels is also terminated. This method, through statistical analysis of multi-level histograms, enriches the extraction of significant features of PRI feature values ​​and simplifies the process of judging the rationality of PRI through complex pulse sequence searches. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of a multi-level histogram sorting method for signals according to an embodiment of this application;

[0060] Figure 2 This is a structural block diagram illustrating a multi-level histogram sorting device for signals according to an embodiment of this application;

[0061] Figure 3This is a structural block diagram illustrating an embodiment of an electronic device according to this application. Detailed Implementation

[0062] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0063] The multi-level histogram sorting method for signals provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0064] As attached Figure 1 As shown, the multi-level histogram sorting method for signals according to an embodiment of this application includes the following steps:

[0065] Step 101: Obtain the vector formed by the arrival times of the radar signals to be sorted, and use each vector as a sample point.

[0066] The multi-level histogram sorting method for signals provided in this application is applied to an electronic device, which is an intelligent hardware device with analysis and calculation functions, such as a computer or server. The electronic device stores a multi-level histogram sorting program or instruction for signals. When this program or instruction is executed by the processor of the electronic device, it implements the multi-level histogram sorting process for signals shown in this application embodiment.

[0067] Step 102: Record the sequence number of each sample point as the first vector according to the order of arrival time from smallest to largest, and perform initialization.

[0068] In practical implementation, a vector composed of the arrival times (TOA) of all radar signals can be obtained as sample points. The index (id) of these sample points is recorded as vector z, i.e., the first vector, in ascending order of arrival time. The repetition interval vector pri_ac and the vector hist_ac composed of the histogram counts corresponding to each element of pri_ac are set as empty vectors, with "number of times the significance is satisfied" num set to 0 and "histogram level" t set to 1.

[0069] In the initialization, the histogram level is set to 1, the saliency product is 0, and the repeating interval vector and the counting vector are denoted as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repeating interval vector.

[0070] Step 103: Process the first vector cyclically according to the preset rules to obtain the updated first vector, the repetition interval vector, and the counting vector.

[0071] Step 104: Determine whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition.

[0072] The first preset condition includes at least one of the following: the histogram level is greater than the first threshold, and the significance count is greater than 1.

[0073] Step 105: If the first preset condition is met, determine whether the number of times the significance is satisfied is 2.

[0074] If the histogram level and the number of times the significance is satisfied do not meet the first preset condition after updating the first vector, the first vector is differentially analyzed with an interval step size of t to obtain possible values ​​of the repeating interval; possible values ​​of the repeating interval less than the third threshold are filtered out; the histogram is statistically analyzed within the tolerance range, and the histograms with the number of times the repeating interval occurs greater than 2 are arranged in ascending order of the number of occurrences to form the updated repeating interval vector and the updated counting vector; based on the length of the updated repeating interval vector, a processing strategy for the updated counting vector and the repeating interval vector is determined. The specific processing strategy can be flexibly set by those skilled in the art, and a feasible processing strategy setting method is detailed in the following related preferred embodiments.

[0075] Step 106: If yes, extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is used as the target repeating interval feature vector.

[0076] If the number of times the significance is satisfied is 2, it means that the pri_ac feature of the multi-level histogram is significant when exiting the loop. The elements of pri_ac are extracted in descending order of the elements in hist_ac to form the PRI feature vector, which serves as the feature value of all repeated intervals provided by the multi-level histogram method.

[0077] Step 107: Using a sequence search method, sequentially search for the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector.

[0078] The pulse sequence is found sequentially using a sequence search method based on the obtained PRI feature value vector.

[0079] Step 108: Filter out PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold.

[0080] The specific value of the second threshold can be flexibly set by those skilled in the art, and no specific restrictions are imposed on it in the embodiments of this application.

[0081] Step 109: Determine the sorting pulse sequence based on the remaining PRI feature values.

[0082] In practical implementation, the total number of pulses corresponding to each feature value obtained through the search can be analyzed, and PRI feature values ​​with too few pulse sequences (less than the second threshold) can be removed. For the remaining feature values, if the interval between feature values ​​is less than the search tolerance, their average value is taken as a more accurate PRI conventional signal, and the corresponding sorting pulse sequence is searched again.

[0083] An alternative method for determining the sorting pulse sequence based on the remaining PRI feature values ​​is as follows:

[0084] Determine whether the spacing between the remaining PRI feature values ​​is greater than or equal to the search tolerance; if not, use the average spacing between the remaining PRI feature values ​​as the updated search tolerance, and search for the corresponding sorting pulse sequence.

[0085] This optional method of determining the sorting pulse sequence yields accurate and reliable results.

[0086] In one alternative embodiment, the processing strategy for the updated count vector and the repeating interval vector can be determined based on the length of the updated repeating interval vector as follows:

[0087] If the length of the updated repeating interval vector meets the second preset condition, the first processing strategy is executed;

[0088] If the length of the updated repeating interval vector meets the third preset condition, the second processing strategy is executed;

[0089] If the length of the updated repeating interval vector meets the fourth preset condition, the third processing strategy is executed.

[0090] Within the tolerance range, a histogram is plotted. Histograms showing repetition intervals greater than 2 are arranged in ascending order of frequency, forming an updated repetition interval vector denoted as pri_new and an updated count vector denoted as hist_new. A second preset condition can be set as the length of pri_new being less than 1 / 100*N; a third preset condition can be set as the length of pri_new being greater than 1 / 100*N but less than 1 / 10*N; and a fourth preset condition can be set as the length of pri_new being greater than or equal to 1 / 10*N. Specifically, the first vector is first differentially analyzed with an interval step size of t to obtain all possible repetition interval values, the total number of which is denoted as N.

[0091] If the length of the updated repetition interval vector satisfies the fourth preset condition, the steps for executing the third processing strategy include:

[0092] If the length of the updated repeating interval vector meets the fourth preset condition, the loop will delete elements in the updated repeating interval vector whose counts are less than the median of the counting sequence, in order to update the repeating interval vector and the counting vector. The loop will stop when the length of the latest repeating interval vector meets the fourth preset condition, and then jump to the execution flow corresponding to the second processing strategy.

[0093] The first processing strategy can be set to execute steps 106 to 109.

[0094] The second processing strategy can be configured to perform the following steps:

[0095] Step 1: Let the length of the current pri_new be n. Initialize the vectors beishu and mark as zero vectors of length n. Analyze i iteratively from 1 to n. For each pri_new(i), calculate the highest possible multiple of the value in the pri_new sequence. Let this multiple be maxtime. Traverse the pri_new vector and find the position where a multiple of pri_new(i) exists. Mark the element at that position in the beishu vector as 1. When each multiple of pri_new(i) can be found in the pri_new sequence during the traversal of the maximum maxtime multiples, mark the element of mark(i) as 1.

[0096] Step 2: Based on the elements marked as 1 in mark and beishu, find the index of the first element in the current t-level histogram where a multiple relationship first appears among the elements of pri_new.

[0097] Step 3: If the index of the first element in pri_new that first shows a multiple relationship is within the first half of all elements in mark that are 1, go to step 3.1; otherwise, go to step 4.

[0098] Step 3.1: Based on the position, append the elements at the positions marked as 1 in the vectors pri_new and hist_new obtained in Step 4 to the end of vectors pri_ac and hist_ac respectively; call the method hist_accu to update the histogram after adding a new level to obtain the new pri_ac and hist_ac;

[0099] Step 3.2: When the ratio of the maximum value of hist_ac to the median is greater than a certain threshold, execute step 3.3; otherwise, set num = 0, and set pri_ac and hist_ac to null, then proceed to step four.

[0100] Step 3.3: When the "number of times the significance is satisfied" is 0 or 1, let num = num + 1 and go to step four; otherwise, go to step five.

[0101] Step 4: Increase the histogram series t by 1, then proceed to step 105.

[0102] Step 5: If the number of times the significance is satisfied is 2, it means that the pri_ac feature of the multi-level histogram is significant when exiting the loop, and proceed to step 6. Otherwise, it means that the counts corresponding to each repeating interval element of pri_ac are relatively uniform and the significance is not strong. At this time, the histogram update method hist_accu is executed again to obtain new pri_ac and hist_ac, and then proceed to step 6.

[0103] Step 6: Extract the elements of pri_ac from the highest to the lowest order in hist_ac to form the PRI feature vector, which serves as the feature value of all repeating intervals provided by the multi-level histogram method.

[0104] Step 7: Find the pulse sequence by sequentially searching the PRI feature value vector obtained in Step 6.

[0105] Step 8: Analyze the total number of pulses corresponding to each feature value obtained through the search, and remove PRI feature values ​​with too few pulse sequences. For the remaining feature values, if the interval between feature values ​​is less than the search tolerance, take their average value as the more accurate PRI conventional signal, and search again to obtain the corresponding sorting pulse sequence.

[0106] Steps five through eight are equivalent to steps 106 through 109.

[0107] The multi-level histogram sorting method for signals provided in this application embodiment obtains a vector composed of the arrival times of the radar signals to be sorted, and uses each vector as a sample point; the sequence number of each sample point is recorded as a first vector according to the order of arrival time from smallest to largest, and initialization is performed; the first vector is processed cyclically according to a preset rule to obtain an updated first vector, a repetition interval vector, and a counting vector; it is determined whether the histogram level and the number of times the significance is satisfied by the updated first vector meet a first preset condition; if the first preset condition is met, it is determined whether the number of times the significance is satisfied is 2; if so, the elements of the updated repetition interval vector are extracted in descending order of the elements in the updated counting vector to form a PRI feature value vector, which is used as the target repetition interval feature value vector; the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector is searched sequentially using a sequence search method; PRI feature values ​​whose total number of corresponding pulse sequences is less than a second threshold are filtered out; and the sorting pulse sequence is determined based on the remaining PRI feature values. The multi-level histogram sorting method for signals provided in this invention first extracts potential PRI feature values ​​from each level of histogram using methods such as tolerance and frequency doubling. Then, it performs multi-level counting accumulation and simplification. Finally, it determines the significance condition of the PRI feature values ​​in the multi-level counting graph. If the significance condition is met twice, the process of increasing the number of levels is terminated. If the significance condition is not met in any consecutive specific levels, the process of increasing the number of levels is also terminated. This method, through statistical analysis of multi-level histograms, enriches the extraction of significant features of PRI feature values ​​and simplifies the process of judging the rationality of PRI through complex pulse sequence searches.

[0108] The following is a specific example illustrating the multi-level histogram sorting method for signals according to an embodiment of this application.

[0109] This specific embodiment provides a multi-level histogram sorting method for signals, which includes the following steps:

[0110] Step 1: Obtain the vector composed of the arrival times (TOA) of all radar signals, and use it as sample points. Record the index (id) of these sample points as vector z in ascending order of arrival time. Perform initialization.

[0111] In this embodiment, the "histogram series" is denoted as t, the "number of times the significance is satisfied" is denoted as num, the "repetition interval vector" that is continuously updated during the sorting process is denoted as pri_ac, and the vector formed by the counts corresponding to each element of pri_ac is denoted as hist_ac. The initialization is t=1, num=0, and pri_ac and hist_ac are empty vectors.

[0112] Step 2: When the "histogram level" t is greater than a certain threshold or the "number of times the significance is satisfied" num exceeds 1, proceed to step 9; otherwise, repeat steps 3 to 8.

[0113] In this embodiment, the histogram level threshold is set to 30.

[0114] Step 3: First, perform a difference operation on the sequence with an interval step size of t to obtain all possible values ​​of the repeating interval, denoted as N. First, remove the excessively small values, and then calculate the histogram within the tolerance range. Keep the repeating intervals with a count greater than 2, and arrange them in ascending order to form a new vector, denoted as pri_new. The vector formed by the corresponding count values ​​is denoted as hist_new. If the length of pri_new is less than 1 / 100*N, then go to step 9; if it is greater than 1 / 100*N but less than 1 / 10*N, then go to step 5; otherwise, execute step 4.

[0115] In this specific embodiment, the threshold for the excessively small repetition interval is selected as 10. -7 Microseconds.

[0116] Step 4: Repeat the process of decreasing the number of intervals in a loop: Each time, delete the part of pri_new that corresponds to the part of hist_new whose count is less than the median of the counting sequence, update pri_new and hist_new, until the length of pri_new is less than 1 / 10*N, then go to step 5.

[0117] Step 5: Let the current length of pri_new be n. Since the elements of pri_new still maintain the ascending order, pri_new(n) is the maximum repetition interval value. Initialize i = 1, and initialize vectors beishu and mark as zero vectors of length n; repeat steps 5.1 to 5.3 until i equals n, then go to step 6.

[0118] Step five of this specific embodiment includes the following steps:

[0119] Step 5.1: For pri_new(i), calculate the highest possible multiple of frequency in the pri_new sequence, denoted as maxtime. Initialize time = 1, and initialize the vector beishu_temp as a zero vector of length n. From position i to n, search for the existence of a position j where pri_new(j) is (time+1) times pri_new(i) within the tolerance range. If not, set i = i+1 and repeat step 5.1; if it exists, set the element beishu_temp(j) corresponding to this position to 1 and repeat step 5.2.

[0120] Step 5.2: Set time = time + 1, then check whether the (time+1) multiple of pri_new(i) exists in the sequence of pri_new. If yes, set beishu_temp(j) corresponding to this position to 1, and go back to step 5.2 for cyclic execution until the (time+1) multiple of pri_new(i) does not exist in the sequence of pri_new, or time increments to maxtime, then go to step 5.3;

[0121] Step 5.3: If time < maxtime at this time, set time = 1 and i = i+1 at the same time, return to step 5.1; if time equals maxtime at this time, it indicates that the judgment on pri_new(i) is completed, mark the element of mark(i) as 1, perform a "bitwise OR" operation on the vector beishu_temp obtained in the process from time=1 to time=maxtime and the vector beishu, reassign the result to the vector beishu, set i = i+1, and go back to step 5.1 for cyclic execution.

[0122] Step 6: Form a vector from the position indexes of elements marked as 1 in mark, denoted as A; form a vector from the position indexes of elements marked as 1 in beishu, denoted as B, calculate the intersection of A and B, denoted as C. It can be known that the first element C(1) in C represents the index position of the first element where the multiple relationship first occurs among various elements of pri_new on the current t-level histogram.

[0123] Step 7: If C is not empty, and the position of value C(1) in vector A is within the first half of A, go to step 7.1; otherwise, go to step 8.

[0124] In this embodiment, the specific process after C meets the condition includes the following steps 7.1 to 7.3:

[0125] Step 7.1: Append the pri_new element corresponding to the element marked as 1 in mark to the end of the pri_ac vector, and append the corresponding hist_new element to the end of the hist_ac vector; call the method hist_accu of "updating the histogram after adding a new level" to obtain new pri_ac and hist_ac;

[0126] Step 7.2: When the ratio of the maximum value to the median of hist_ac is greater than a certain threshold, perform step 7.3; otherwise, set num = 0, empty pri_ac and hist_ac, and go to step 8;

[0127] In this embodiment, the threshold of the ratio of the maximum value to the median of hist_ac is set to 25.

[0128] Step 7.3: When num < 2, let num = num + 1 and go to step 8; otherwise, go to step 9.

[0129] Step 8: t = t + 1, then proceed to step 3.

[0130] Step 9: If the number of times the significance is satisfied is 2, it means that the pri_ac feature of the multi-level histogram is significant when exiting the loop, and proceed to step 10. Otherwise, it means that the counts corresponding to each repeating interval element of pri_ac are relatively uniform and the significance is not strong. At this time, the histogram update method hist_accu is executed again to obtain new pri_ac and hist_ac, and then proceed to step 10.

[0131] Furthermore, the specific process of updating the histogram after adding a new level, hist_accu, includes the steps from 7.1.1 to 7.1.3 mentioned above.

[0132] Step 10: Extract the elements of pri_ac from the highest to the lowest order in hist_ac to form the PRI feature vector, which serves as the feature value of all repeating intervals provided by the multi-level histogram method.

[0133] Step 11: Using the PRI feature value vector obtained in Step 10, find the pulse sequence sequentially through a sequence search method. During the search process, appropriately relax the tolerance setting, for example, choose 2.0 microseconds.

[0134] Step 12: Analyze the total number of pulses corresponding to each feature value obtained through the search, and remove PRI feature values ​​with too few pulse sequences. For the remaining feature values, if the interval between feature values ​​is less than the search tolerance, take their average value as the more accurate PRI conventional signal, and search again to obtain the corresponding sorting pulse sequence.

[0135] In this embodiment, each class that satisfies the condition that the total number of pulse sequences is greater than valid_pulse_num_hb (default value 40) is considered as a sorted pulse sequence.

[0136] Furthermore, the method of calling "update histogram after adding a new level" as described in steps 7.1 and 9 includes the following three steps:

[0137] Step 1: Sort the input pri_ac and hist_ac vectors in ascending order of the elements of pri_ac. Then, according to the repetition interval tolerance (0.5 to 1.5 microseconds), merge the elements within the tolerance range into one element and add the corresponding counts to form a new vector, denoted as pri and hist. Denote the length of the pri vector as n.

[0138] Step 2: Initialize i=1, select valid pri through iterative statistical analysis until i equals n, and then go to Step 3; the specific process of selecting valid pri through iterative statistical analysis includes the following steps 2.1 to 2.3;

[0139] Step 2.1: For the element pri(i), calculate the highest possible frequency multiple that can occur among the elements of the pri vector, that is, how many times the maximum element in pri is greater than or equal to pri(i), and this multiple is recorded as maxtime; initialize the vector beishu_temp as a zero vector of length n; initialize time=1;

[0140] Step 2.2: Under the condition that time < maxtime is satisfied, check whether there is a position j after position i that satisfies, within the tolerance range, that pri(j) is (time+1) times of pri(i). If such j exists, set beishu_temp(j)=hist(j), continue to circularly search for j until all hist(j) values corresponding to pri(j) that are (time+1) times of pri(i) are recorded in beishu_temp(j), then set time=time+1, and cyclically execute step 2.2; if no such j exists, it is determined that pri(i) is invalid, stop searching whether higher frequency multiple relationships exist, directly jump out of step 2.2, and execute step 2.3;

[0141] Step 2.3: If time < maxtime or time equals 1, set i=i+1 and return to step 2.1; if time equals maxtime, accumulate all counting hist(j) corresponding to pri(j) that are multiples of pri(i) recorded in beishu_temp to hist(i), reset all hist(j) to zero, then set i=i+1 and return to step 2.1.

[0142] Step 3: extract pri and hist at positions where the value is greater than zero in the hist sequence, as updated pri_ac and hist_ac.

[0143] The multi-level histogram sorting method for signals provided by this specific embodiment has the following beneficial effects:

[0144] First, potential PRI characteristic values are extracted at each level of the histogram by means of tolerance and frequency multiplication, then accumulation and simplification of multi-level counting are performed, and finally the significance condition of the multi-level counting graph is judged. When the significance condition is met twice, the process of increasing the number of levels is exited. The foregoing method enriches the selection methods for PRI characteristic values, simplifies the calculation time for determining characteristic values through search, and greatly improves the success rate of subsequently obtaining sorting results through a sequence search method.

[0145] Secondly, the classification process is completed at two levels: a multi-level histogram algorithm and a histogram update algorithm under the condition of adding a new level. The two complement each other, and by analyzing the statistical characteristics of the multi-level histogram counts, the significance of the selected PRI feature values ​​is improved.

[0146] Third, it allows for sample overlap and missed pulses during the acquisition process, making it particularly suitable for radar detection data with multiple source signals (which may be multiple radar transmitters, or a single source signal that has been reflected and refracted multiple times before being detected) and densely intersecting in time, enabling accurate and rapid acquisition of sorting patterns.

[0147] Fourth, it eliminates the need for complex parameter selection, greatly reducing the workload and professional requirements for operators.

[0148] Figure 2 The structural block diagram of a multi-level histogram sorting device for signals according to an embodiment of this application is shown.

[0149] The multi-level histogram sorting device for signals provided in this application includes the following functional modules:

[0150] The acquisition module 201 is used to acquire a vector composed of the arrival times of the radar signals to be sorted, and to use each vector as a sample point.

[0151] The initialization module 202 is used to record the sequence number of each sample point as a first vector according to the order of arrival time from smallest to largest, and to perform initialization. The initialization sets the histogram level to 1, satisfies the saliency product of times being 0, and records the repetition interval vector and the counting vector as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repetition interval vector.

[0152] The update module 203 is used to process the first vector cyclically according to a preset rule to obtain the updated first vector, the repetition interval vector, and the counting vector.

[0153] The first judgment module 204 is used to judge whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition.

[0154] The second judgment module 205 is used to determine whether the number of times the significance is satisfied is 2 when the first preset condition is met.

[0155] Execution module 206 is used to extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is used as the target repeating interval feature vector.

[0156] The search module 207 is used to sequentially search for the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector using a sequence search method;

[0157] The first filtering module 208 is used to filter out PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold.

[0158] Sequence determination module 209 is used to determine the sorting pulse sequence based on the remaining PRI feature values;

[0159] The first preset condition includes at least one of the following: the histogram level is greater than a first threshold, and the number of times the significance is satisfied is greater than 1.

[0160] Optionally, the device further includes:

[0161] The difference module is used to perform a difference of the first vector with an interval step size of t after the first judgment module judges whether the histogram level and the number of times the significance is satisfied meet the preset conditions. If the first preset conditions are not met, the module performs a difference of the first vector with an interval step size of t to obtain the possible value of the repetition interval.

[0162] The second filtering module is used to filter out possible values ​​of the repetition interval that are less than the third threshold.

[0163] The statistics module is used to generate histograms within the tolerance range. Histograms with a repetition interval occurrence greater than 2 are arranged in ascending order of occurrence, forming an updated repetition interval vector and an updated count vector.

[0164] The strategy determination module is used to determine the processing strategy for the updated counting vector and the repeating interval vector based on the length of the updated repeating interval vector.

[0165] Optionally, the strategy determination module includes:

[0166] The first submodule is used to execute the first processing strategy when the length of the updated repeating interval vector meets the second preset condition.

[0167] The second submodule is used to execute the second processing strategy if the length of the updated repeating interval vector meets the third preset condition.

[0168] The third submodule is used to execute the third processing strategy if the length of the updated repeating interval vector meets the fourth preset condition.

[0169] Optionally, the third submodule is specifically used for:

[0170] If the length of the updated repeating interval vector meets the fourth preset condition, the elements in the updated repeating interval vector that have a count less than the median of the counting sequence are deleted in a loop to update the repeating interval vector and the counting vector. The loop stops when the length of the latest repeating interval vector meets the fourth preset condition, and the process jumps to the execution flow corresponding to the second processing strategy.

[0171] Optionally, the sequence determination module includes:

[0172] The fourth submodule is used to determine whether the spacing between the remaining PRI feature values ​​is greater than or equal to the search tolerance.

[0173] The fifth submodule is used to search for the corresponding sorting pulse sequence if, no, the average value of the remaining PRI feature value spacing is used as the updated search tolerance.

[0174] The multi-level histogram sorting device for signals provided in this application first extracts potential PRI feature values ​​from each level of histogram using methods such as tolerance and frequency doubling. Then, it performs multi-level counting accumulation and simplification. Finally, it determines the significance condition of the PRI feature values ​​in the multi-level counting graph. If the significance condition is met twice, the process of increasing the number of levels is terminated. If the significance condition is not met in any consecutive specific levels, the process of increasing the number of levels is also terminated. This method, through statistical analysis of multi-level histograms, enriches the extraction of significant features of PRI feature values ​​and simplifies the process of judging the rationality of PRI through complex pulse sequence searches.

[0175] In the embodiments of this application Figure 2 The multi-level histogram sorting device for the signals shown can be installed in a mobile device or a server. The mobile device or server equipped with this device can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application does not specifically limit the specific operating system used.

[0176] The embodiments provided in this application Figure 2 The multi-level histogram sorting device for the signal shown can achieve Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0177] Optionally, refer to Figure 3 The present application also provides an electronic device 300, including a processor 301, a memory 302, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the various processes performed by the multi-level histogram sorting device for the above-mentioned signals and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0178] Optionally, embodiments of the present invention provide a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes performed by the multi-level histogram sorting device for the aforementioned signals.

[0179] It should be noted that the electronic device in this application embodiment includes the server described above.

[0180] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0182] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-level histogram sorting method for signals, characterized in that, include: Obtain the vector composed of the arrival times of the radar signals to be sorted, and use each vector as a sample point; The sequence number of each sample point is recorded as the first vector according to the order of arrival time from smallest to largest, and initialization is performed. The initialization sets the histogram level to 1, satisfies the saliency product of times being 0, and records the repetition interval vector and the counting vector as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repetition interval vector. The first vector is processed cyclically according to a preset rule to obtain the updated first vector, the repetition interval vector, and the counting vector. Determine whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition. If the first preset condition is met, determine whether the number of times the significance is satisfied is 2; If so, extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is then used as the target repeating interval feature vector. Using a sequence search method, the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector is sequentially searched; PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold are filtered out. The sorting pulse sequence is determined based on the remaining PRI feature values; The first preset condition includes at least one of the following: the histogram level is greater than a first threshold, and the number of times the significance is satisfied is greater than 1.

2. The method according to claim 1, characterized in that, After determining whether the histogram level and the number of times the significance is satisfied by the updated first vector meet preset conditions, the method further includes: If the first preset condition is not met, the first vector is differentially divided by an interval step size of t to obtain possible values ​​of the repetition interval. Filter out possible values ​​of the repetition interval that are less than the third threshold; Within the tolerance range, the histogram is calculated. The histograms with the number of occurrences of the repetition interval greater than 2 are arranged in ascending order of the number of occurrences to form the updated repetition interval vector and the updated count vector. Based on the length of the updated repetition interval vector, a processing strategy for the updated counting vector and the repetition interval vector is determined.

3. The method according to claim 1, characterized in that, The step of determining the processing strategy for the updated counting vector and the repeating interval vector based on the length of the updated repeating interval vector includes: If the length of the updated repeating interval vector meets the second preset condition, the first processing strategy is executed; If the length of the updated repeating interval vector meets the third preset condition, the second processing strategy is executed; If the length of the updated repeating interval vector meets the fourth preset condition, the third processing strategy is executed.

4. The method according to claim 3, characterized in that, If the length of the updated repetition interval vector satisfies the fourth preset condition, the steps for executing the third processing strategy include: If the length of the updated repeating interval vector meets the fourth preset condition, the elements in the updated repeating interval vector that have a count less than the median of the counting sequence are deleted in a loop to update the repeating interval vector and the counting vector. The loop stops when the length of the latest repeating interval vector meets the fourth preset condition, and the process jumps to the execution flow corresponding to the second processing strategy.

5. The method according to claim 1, characterized in that, The steps for determining the sorting pulse sequence based on the remaining PRI feature values ​​include: Determine whether the intervals between the remaining PRI feature values ​​are all greater than or equal to the search tolerance; If not, the average value of the remaining PRI feature value intervals is used as the updated search tolerance to search for the corresponding sorting pulse sequence.

6. A multi-level histogram sorting device for signals, characterized in that, include: The acquisition module is used to acquire a vector composed of the arrival times of the radar signals to be sorted, and to use each vector as a sample point. An initialization module is used to record the sequence number of each sample point as a first vector according to the order of arrival time from smallest to largest, and to perform initialization. The initialization sets the histogram level to 1, satisfies the saliency product of times being 0, and records the repetition interval vector and the counting vector as empty vectors. The counting vector is a vector composed of the counts corresponding to each element of the repetition interval vector. The update module is used to process the first vector cyclically according to a preset rule to obtain the updated first vector, the repetition interval vector, and the counting vector. The first judgment module is used to determine whether the histogram level and the number of times the significance is satisfied by the updated first vector meet the first preset condition. The second judgment module is used to determine whether the number of times the significance is satisfied is 2, provided that the first preset condition is met. The execution module is used to extract the elements of the updated repeating interval vector in descending order of the elements in the updated counting vector to form the PRI feature vector, which is then used as the target repeating interval feature vector. The search module is used to sequentially search for the pulse sequence corresponding to each PRI feature value in the target repetition interval feature value vector using a sequence search method; The first filtering module is used to filter out PRI feature values ​​whose total number of corresponding pulse sequences is less than the second threshold. The sequence determination module is used to determine the sorting pulse sequence based on the remaining PRI feature values; The first preset condition includes at least one of the following: the histogram level is greater than a first threshold, and the number of times the significance is satisfied is greater than 1.

7. The apparatus according to claim 6, characterized in that, The device further includes: The difference module is used to perform a difference of the first vector with an interval step size of t after the first judgment module judges whether the histogram level and the number of times the significance is satisfied meet the preset conditions. If the first preset conditions are not met, the module obtains the possible value of the repetition interval by performing a difference of the first vector with an interval step size of t. The second filtering module is used to filter out possible values ​​of the repetition interval that are less than the third threshold. The statistics module is used to generate histograms within the tolerance range. Histograms with a repetition interval occurrence greater than 2 are arranged in ascending order of occurrence, forming an updated repetition interval vector and an updated count vector. The strategy determination module is used to determine the processing strategy for the updated counting vector and the repeating interval vector based on the length of the updated repeating interval vector.

8. The apparatus according to claim 7, characterized in that, The strategy determination module includes: The first submodule is used to execute the first processing strategy when the length of the updated repeating interval vector meets the second preset condition. The second submodule is used to execute the second processing strategy if the length of the updated repeating interval vector meets the third preset condition. The third submodule is used to execute the third processing strategy if the length of the updated repeating interval vector meets the fourth preset condition.

9. The apparatus according to claim 6, characterized in that, The sequence determination module includes: The fourth submodule is used to determine whether the spacing between the remaining PRI feature values ​​is greater than or equal to the search tolerance. The fifth submodule is used to search for the corresponding sorting pulse sequence if, no, the average value of the remaining PRI feature value spacing is used as the updated search tolerance.

10. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions are executed by the processor to perform the steps of the multi-level histogram sorting method for any one of the signals in claims 1-5.

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