High-fault-tolerance radar signal sorting method and device based on improved plane transformation and product

Abstract

The invention discloses a high fault tolerance radar signal sorting method, device and product based on improved plane transformation, and the method comprises the steps: processing a radar electromagnetic pulse signal, and obtaining a TOA sequence; based on a preset PRI dynamic range and a search step size, obtaining a preset PRI array, completing plane information entropy calculation on the TOA sequence, determining a local maximum value, and forming a preliminary PRI candidate set; establishing a jitter PRI supplementary recognition mechanism, and storing the PRI judged as jitter modulation in a preset PRI array into a compensation queue; performing two-dimensional plane mapping on the preliminary PRI candidate set, sequentially extracting a characteristic curve and a corresponding TOA sequence, and storing the PRI which is judged to be false into a false PRI queue; performing pairwise intersection operation on each extracted TOA sequence index to complete real PRI judgment to obtain a PRI set; and removing false PRI queues, supplementing the compensation queues, and realizing optimal integration of the PRI set.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing technology in electronic reconnaissance, specifically relating to a highly fault-tolerant radar signal sorting method, equipment, and products based on improved plane transformation. Background Technology

[0002] As a core sensing component of modern electronic reconnaissance systems, radar reconnaissance systems are deployed on multi-dimensional detection platforms, including airborne, shipborne, and spaceborne platforms, to continuously or periodically monitor and analyze the electromagnetic signals of radar radiation sources. By acquiring the radiation pulse characteristic parameters of target signals, they ultimately obtain intelligence information such as the technical parameters, spatial positioning information, radar type, and operating mode of the radar radiation source signal. This provides reliable information support and decision-making basis for subsequent electromagnetic situational awareness and radar electronic defense.

[0003] Radar signal sorting, as a core technology and crucial component of radar reconnaissance systems, plays a critical role in deinterleaving radiation sources from complex, overlapping signal streams. It is not only a core step in electromagnetic sensing but also a vital technological foundation for subsequent parameter analysis and extraction, and radar type identification in radar reconnaissance systems. With the rapid development of new radar technologies and the dramatic increase in the number of electromagnetic devices, traditional sorting algorithms inevitably exhibit limitations in today's complex electromagnetic environment, leading to issues such as over-scanning and under-scanning. This creates cascading interference on the accuracy of subsequent identification and positioning, posing a severe challenge to the information processing capabilities of traditional radar electronic reconnaissance systems and the overall performance of radar signal sorting algorithms.

[0004] In the sorting algorithm system, Pulse Repetition Interval (PRI) sorting is one of the earliest methods to originate and be applied, besides template matching. It boasts a simple principle, high computational efficiency, and high engineering practical value. In contrast, template matching has become increasingly inadequate for the complex radar signal sorting tasks of today, thanks to advancements in radar architecture. The core mechanism of PRI-based sorting lies in employing a reasonable method to mine the PRI values ​​of each radar pulse sequence hidden in the Time of Arrival (TOA) information, and then using these PRI values ​​to search and extract the radar pulse sequence. Compared to sorting methods based on intra-pulse modulation features or machine learning, this method has lower complexity and computational resource requirements. It meets real-time processing requirements while possessing good system compatibility, and can be combined with other sorting methods and features to achieve multi-domain joint sorting.

[0005] However, traditional PRI methods, such as Cumulative Difference Histogram (CDIF), Sequential Difference Histogram (SDIF), and PRI transform methods, often lack data fault tolerance (pulse loss, parameter estimation error) and interference fault tolerance (false alarms, noise interference), resulting in significantly reduced robustness in dynamic electromagnetic environments. Furthermore, when faced with complex scenarios involving multiple PRI modulation modes such as fixed PRI, staggered PRI, and jittery PRI, the algorithms lack effective adaptation mechanisms, leading to insufficient comprehensiveness and versatility, and a gradual decline in overall processing performance. This, to some extent, restricts their engineering applicability in complex electromagnetic environments. Therefore, there is an urgent need to develop highly fault-tolerant PRI sorting algorithms with stronger environmental adaptability and anti-interference capabilities to address the increasingly severe electromagnetic reconnaissance needs and technical challenges. Summary of the Invention

[0006] To overcome the limitations of radar signal sorting methods in complex environments, enhance the data and interference tolerance of sorting algorithms, and improve algorithm adaptability and processing performance to adapt to the increasingly complex practical application environment of radar reconnaissance systems, this invention proposes a highly fault-tolerant radar signal sorting method based on improved plane transformation, comprising:

[0007] Step 1: Process the received radar electromagnetic pulse signal to obtain the TOA sequence;

[0008] Step 2: Based on the preset dynamic range and search step size of PRI, obtain the preset PRI array, apply entropy theory to complete the planar information entropy calculation of the TOA sequence, determine the local maxima, and form a preliminary PRI candidate set by constructing the PRI values ​​corresponding to all maxima.

[0009] Step 3: Establish a jitter PRI supplementary identification mechanism. Perform jitter comprehensive judgment on each element value in the preset PRI array obtained in Step 2 in turn, and store the PRI value that is judged as jitter modulation into the compensation queue as jitter modulation.

[0010] Step 4: Perform two-dimensional plane mapping on the preliminary PRI candidate set obtained in Step 2, extract the feature curves and their corresponding TOA sequences in sequence, and store the false PRIs into the false PRI queue through PRI verification.

[0011] Step 5: Perform pairwise intersection operations on the TOA sequence indices extracted in Step 4, and complete the true PRI determination to obtain the PRI set;

[0012] Step 6: Based on the PRI set obtained in Step 5, integrate the sequence data, remove the false PRI queue from Step 4, and supplement the compensation queue from Step 3 to achieve optimized integration of the PRI set.

[0013] Preferably, the TOA sequence obtained in step 1 contains the arrival times of n pulse signals {toa1, toa2, ..., toa...} n}, where toa n This indicates the arrival time of the nth pulse signal.

[0014] Preferably, step 2 includes:

[0015] Step 2-1: Preset the dynamic range of PRI to [PRI min ,PRI max ], of which PRI min Set a minimum value for PRI. max Set a preset maximum value for PRI, set the search step size to step, and obtain the preset PRI array PRI. preset ;

[0016] Step 2-2: Using PRI preset The number of elements contained in the histogram is M, which is the number of bins in the histogram.

[0017] Steps 2-3: Using PRI respectively preset Each element in the matrix is ​​used as the width W of the two-dimensional plane mapping transformation; the number of pulses in the i-th sub-bin under the current W is counted as n. i The probability density p of the i-th bin is obtained. i for:

[0018]

[0019] In the formula, n is the total number of received pulses. The current W-plane information entropy H(W) is obtained according to the following formula:

[0020]

[0021] Steps 2-4: After traversing each element in the PRI array, perform a negative operation on the information entropy data to transform the optimization problem of detecting local minima of information entropy into the problem of detecting local maxima in the negative entropy function;

[0022] Steps 2-5: Based on the changing trend of the planar information entropy value, an adaptive strategy is used to determine potential local maxima. The observation window width corresponding to the local maxima constitutes the preliminary PRI candidate set. cand .

[0023] Preferably, step 3 includes:

[0024] Step 3-1: Using PRI respectively preset Each element value in the matrix is ​​used as the width W of the two-dimensional plane mapping transformation and the number of histogram bins MM;

[0025] Step 3-2: Let the bin counting result N = {n1, n2, ..., n} i ,...,n M}, n i This represents the number of pulses in the i-th sub-bin, when the following formula is satisfied:

[0026] n i >mean(N)+std(N)

[0027] Preliminary determination indicates that this binning corresponds to one of the characteristic curves; where mean(N) and std(N) represent the mean and standard deviation, respectively;

[0028] Step 3-3: For all characteristic curves obtained in Step 3-2, perform jitter comprehensive judgment on the data corresponding to the bin index. If the judgment condition is met, determine that the PRI value corresponding to W and M is jitter modulation and store it in the compensation queue.

[0029] Preferably, the jitter comprehensive determination in step 3-3 includes: simultaneously satisfying the criteria of continuity, range, and chi-square test, wherein,

[0030] The continuity determination includes: first, using a set relatively low threshold to preliminarily determine whether there is a continuous array in the set index; if a continuous array exists, it is corrected for continuity and the binning index is completed; then, using a set relatively high threshold to determine the continuous array, if both thresholds meet the continuity condition, the index is determined to meet the continuity condition.

[0031] The range is determined as follows: the range of N is greater than the mean of N;

[0032] The chi-square test determines that: if the chi-square test is performed on N, the corresponding value rejects the uniform distribution hypothesis.

[0033] Preferably, step 4 includes:

[0034] Step 4-1: Preliminary PRI candidate set obtained from Step 2 cand Extract the histogram binning index set and the corresponding binning count result N = {n1, n2, ..., n}. M}, characteristic curves and corresponding TOA sequences; perform continuity correction on the histogram binning index set. If the set before and after correction is inconsistent and the corrected set does not have continuity characteristics, it indicates that the PRI is a false value and is stored in the false PRI queue; if the set before and after correction is consistent, proceed to step 4-2:

[0035] Step 4-2: Iterate through the count results of each sub-box and make a judgment based on the following criteria:

[0036] min{n i :n i >μ+σ}-max{n i :n i <μ+σ}>max{μ,σ}

[0037] Where μ is the mean of N and σ is the standard deviation of N.

[0038] If the condition is not met, the corresponding PRI value is a false PRI value and is stored in the false PRI queue.

[0039] Preferably, step 5 includes:

[0040] Perform pairwise intersection operations on the TOA sequence indices extracted in step 4 to merge PRI values ​​with similar TOA sequences into a unified subset, that is, group the real PRI values ​​and their harmonics into one set;

[0041] Each subset is identified as having one and only one true PRI value. The PRI value with the fewest feature curves and the strongest continuity of the ordinate in the subset is selected as the true PRI value, thus obtaining the current PRI set.

[0042] Preferably, step 6 includes:

[0043] Calculate the intersection of the false PRI queues identified in step 4 and the current PRI set, and delete the PRIs and their corresponding TOA sequences from the intersection;

[0044] The compensation queue from step 3 is extracted and added to the true PRI value set and its corresponding TOA sequence to obtain the true PRI value set and its corresponding TOA sequence set.

[0045] In a second aspect, the present invention provides a computer device, including 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 above-described method.

[0046] On the other hand, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0047] Compared with the prior art, the advantages of the present invention are:

[0048] 1. To address the limitations of traditional PRI sorting algorithms in mixed modulation scenarios, this invention maps the radar pulse arrival time (TOA) to a specific two-dimensional feature plane. It fully leverages the separability of planar features for different modulation types, such as fixed PRI, staggered PRI, and jittery PRI, as well as the independence of the two-dimensional mapping parameters for each radar source. This invention can effectively distinguish TOA sequences from multiple radar sources with overlapping modulation types, accurately determining the PRI value while simultaneously identifying the PRI modulation type.

[0049] 2. This invention introduces a two-dimensional planar information entropy quantization model, which provides a quantitative judgment basis for candidate PRI values. It does not rely on additional pulse search algorithms. It only needs to extract the constituent points of the characteristic curves corresponding to PRI to achieve effective deinterlacing of radar pulse signals. While reducing the computational complexity of the algorithm, it avoids the system sorting error that may be introduced by the sequence search algorithm, and improves the accuracy and reliability of the sorting results.

[0050] 3. This invention possesses strong data and interference tolerance capabilities; the random distribution of stray pulses will not substantially affect the overall representation of the two-dimensional plane. Even in situations with high pulse loss and false alarm rates, this invention can still achieve high-quality signal sorting. Attached Figure Description

[0051] Figure 1 It is a two-dimensional planar information entropy curve and extreme point distribution diagram;

[0052] Figure 2 This is a schematic diagram of the plane transformation of dithered PRI modulation;

[0053] Figure 3 It is a histogram of PRI modulation frequency counts by bin;

[0054] Figure 4 This is a schematic diagram of a fixed PRI modulation plane transform;

[0055] Figure 5 It is a fixed PRI modulation histogram with bin frequency statistics;

[0056] Figure 6 This is a schematic diagram of the plane transformation of staggered PRI modulation;

[0057] Figure 7 It is a histogram of the frequency of PRI modulation in different bins;

[0058] Figure 8 This is a flowchart of a high-fault-tolerant radar signal sorting algorithm based on improved plane transformation. Detailed Implementation

[0059] The radar radiation source reconnaissance system intercepts and reconnoiters the radar radiation source pulse signals within a designated reconnaissance area, and processes the received radar electromagnetic pulse signals to obtain TOA data.

[0060] The method includes:

[0061] Step 1) Based on the preset dynamic range and search step size of PRI, a preset PRI array is obtained. The values ​​of each element in the PRI array are used as the width W of the two-dimensional plane mapping, and the number of elements in the PRI array is used as the number of histogram bins. Histogram statistics are performed on the elements of the PRI array, and the entropy theory is applied to complete the planar information entropy calculation. The obtained information entropy data is negatively processed, and its numerical trend is analyzed to determine local maxima. The PRI values ​​corresponding to the obtained maxima constitute the initial candidate PRI set.

[0062] Step 2) To address the issue of insignificant information entropy characteristics in the pulse plane of jittered PRI modulation, a jittered PRI supplementary identification mechanism is established. Each element in the PRI array is sequentially used as the two-dimensional plane mapping width W and the number of histogram bins M. A comprehensive judgment criterion based on histogram statistical sequence continuity, range, and chi-square test is constructed. When the jitter condition is met, the PRI value corresponding to W is determined to be jittered modulation and stored in the compensation queue.

[0063] Step 3) Perform a two-dimensional planar mapping on the candidate PRI set from Step 1). Define the values ​​in the histogram statistics that exceed the sum of the mean and standard deviation as the corresponding PRI feature curves. Extract the ordinate sequence of all histogram bins that meet the conditions and their corresponding TOA sequence indices. Perform continuity correction on the histogram bin sequences that meet the threshold conditions. Based on the continuity processing results and the corresponding histogram range values, determine whether the PRI is a jitter modulation type or a fake PRI. If it is a fake PRI, store it in the fake PRI queue.

[0064] Step 4) To eliminate the influence of harmonic interference, perform pairwise intersection operations on the extracted TOA sequence indices to merge PRI values ​​with similar TOA sequences into a unified subset. At this point, it can be determined that there is one and only one true PRI value in each subset. The true PRI value is determined by the degree of continuity of the ordinate sequence extracted in Step 3) and the number of corresponding sequence representations.

[0065] Step 5) Based on the PRI set determined in Step 4), integrate the sequence data, remove the spurious PRI identified in Step 3) and delete the corresponding sequences, supplement the compensated jitter PRI queue selected in Step 2) and complete the corresponding sequence extraction, and determine the PRI modulation type using the corresponding sequence representation number and histogram binning continuity. Finally, a complete output is formed containing the set of true PRI values, the set of modulation types, and the corresponding TOA sequence set.

[0066] Step 6) Using the accuracy rate (the degree of matching between the sorted radar radiation source and the real signal), false detection rate (the proportion of pulses misidentified as a certain cluster), and false detection rate (the proportion of pulses not sorted out) as the core indicators, the data fault tolerance and interference fault tolerance performance of the present invention is evaluated, and the actual sorting performance of the present invention under different datasets and different pulse false alarm rates and pulse loss rates is verified.

[0067] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0068] Example 1

[0069] like Figure 8 As shown, Embodiment 1 of the present invention provides a highly fault-tolerant radar signal sorting method based on improved plane transformation, comprising the following steps:

[0070] 1. Initial variable setting and determination of candidate PRI set

[0071] Let the TOA sequence contain the arrival times of n pulse signals {toa1, toa2, ..., toa...} n}, The dynamic range of PRI is [PRI min ,PRI max ], of which PRI min Set a minimum value for PRI. max If a maximum value is preset for PRI and the search step size is set to step, then the preset PRI array can be represented as:

[0072]

[0073] in For the set of non-negative integers, PRI preset The element iteration in the transformation is used as the width W of the two-dimensional plane mapping transformation, and the transformation formula can be expressed as:

[0074]

[0075] In the formula, mod is the modulo function. It is the floor function, i.e., x i for toa i The remainder when divided by W, y i for toa i The quotient divided by W.

[0076] Taking fixed PRI modulation as an example, let toa q with toa p Let be the pulse arrival time from the same radar, with an interval of k·PRI, where k is any positive integer and PRI is the pulse repetition interval of that radar. This applies when the transform width W = PRI.

[0077]

[0078] From the above formula, it can be concluded that when the correct PRI value is selected as the observation window width W, pulse signals from the same radar radiation source exhibit significant geometric characteristics on the two-dimensional plane. Specifically, the horizontal coordinates tend to be equal, while the vertical coordinates differ due to the pulse arrival time. Under ideal conditions, the difference in the vertical coordinates exhibits a regular characteristic of integer continuity.

[0079] Therefore, for radar signals using fixed PRI modulation, their two-dimensional feature mapping is represented by a single line segment approximately perpendicular to the x-axis; while the two-dimensional feature mapping of staggered PRI modulation is represented by multiple parallel approximately perpendicular line segments, with the number of feature segments corresponding to the number of staggered sub-cycles of the radar; and for jittery PRI modulation, the radar signal forms a series of continuously distributed vertical line segments on the two-dimensional plane, which can also be regarded as a strip-shaped region that is continuous and has a large span relative to the x-axis.

[0080] When the true PRI is selected as the observation window width, compared to other candidate values, the corresponding two-dimensional planar feature point distribution exhibits higher density and effectiveness. This distribution characteristic can be quantified using planar information entropy. The planar information entropy corresponding to the true PRI shows a local minimum on the entire entropy value change curve. This is because the correct PRI parameters optimally represent the inherent periodicity of the pulse time series, achieving maximum regularity and minimum randomness in the distribution of the two-dimensional mapping points. Based on the above theoretical analysis, this invention applies planar information entropy to initially determine the PRI candidate set by finding the local minimum of the entropy value. cand .

[0081] Using the number of elements in the PRI array as the number of histogram bins M, count the number of pulses in the i-th bin under the current W as n. i The probability density of each bin is:

[0082]

[0083] In the formula, n is the total number of received pulses. The information entropy of the current W plane is calculated using the probability density of each sub-box under the current W:

[0084]

[0085] This formula avoids the occurrence of log(0). When the planar feature points approach the random distribution under ideal conditions, the planar entropy approaches log(n). The overall calculation of H(W) is completed by taking the element values ​​in the PRI array as W in turn. In order to facilitate the application of mature peak detection algorithms, the original information entropy data is negatively processed, transforming the optimization problem of detecting local minima of information entropy into the problem of detecting local maxima in the negative entropy function.

[0086] W * =argmax W (-H) (7)

[0087] Based on the changing trend of planar information entropy values, an adaptive strategy is adopted to determine potential local maxima. The observation window widths corresponding to the local maxima identified by the algorithm constitute the initial candidate set of PRIs. cand .like Figure 1 The figure shows the relationship between planar information entropy and observation window width, where the triangle markers represent local minimum points of information entropy.

[0088] 2. Jitter PRI Supplement Mechanism

[0089] The jittered PRI modulation pulse is represented by a two-dimensional plane mapping as multiple continuous line segments. When the jitter range is too large, its density and effectiveness are not significant under the information entropy feature, thus affecting the detection results. To solve this problem, this invention designs a jittered PRI detection mechanism, using PRI... preset The element iteration in the algorithm is used as the width W of the two-dimensional plane mapping transformation and the number of histogram bins M. Unlike the information entropy calculation strategy, this setting ensures that the histogram bins correspond to the PRI unit time. Let N = {n1, n2, ..., n} M}, n i This represents the number of pulses in the i-th sub-box. The i-th sub-box satisfies the following condition:

[0090] n i >mean(N)+std(N) (8)

[0091] At that time, it was initially determined that the sub-box corresponded to one of the characteristic curves.

[0092] The continuity of the histogram binning index set that meets the threshold condition can be used as a criterion for the significance of jitter PRI. First, a preliminary determination is made as to whether a continuous array exists in the set of indices; in this case, a relatively low threshold is applied. If a continuous array exists, continuity correction is performed. This is because the distribution of pulse numbers within the jitter PRI is not uniform, and a small number of binning indices may be within the PRI jitter range but do not meet the threshold condition. Continuity correction will complete the determination of these binning indices. Then, a continuous array determination is performed, this time with a relatively high threshold. If both thresholds meet the continuity condition, the index is determined to satisfy the continuity condition.

[0093] Since this threshold is the sum of the mean and one standard deviation, almost all bins in N will contain bins that exceed this threshold. Therefore, relying solely on continuity judgment may not be feasible in N. i When the distribution is uniform, it is mistakenly identified as jitter. Therefore, if the continuity condition is met, the following conditions need to be further verified:

[0094] (1) Range verification: The range of N should be greater than the mean of N.

[0095] (2) Chi-square test: Perform a chi-square test on N. The corresponding value should be able to reject the uniform distribution hypothesis.

[0096] If the data corresponding to the binning index meets the judgment criteria in terms of histogram statistical sequence continuity, range, and chi-square test, then the PRI value corresponding to W and M is determined to be jitter modulation, and it is stored in the candidate queue. Figure 2 The diagram shown is a schematic of the plane transformation of jittered PRI modulation. Figure 3 This is a histogram of frequency distributions of the jittered PRI modulation.

[0097] 3. Candidate PRI Validation and Feature Curve Extraction

[0098] This step requires processing the candidate PRI set determined in step 1). cand A two-dimensional plane mapping analysis is performed, and since step 2) already covers the corresponding two-dimensional plane mapping calculation, PRI can be directly extracted from the calculation results of step 2). cand Histogram binning index set and corresponding binning count results N = {n1, n2, ..., n} M It is important to note that although both involve two-dimensional mapping, their target orientation and the objects processed by PRI differ, and the subsequent judgment criteria and specific operations are also different.

[0099] The core objective of this step is to verify the authenticity of candidate PRIs and extract the TOA indexes corresponding to the feature curve points. To this end, this invention first performs continuity correction on the histogram binning index set, making a preliminary judgment by comparing the consistency of the set before and after processing. If the set before and after correction is inconsistent, it indicates that the PRI may be a spurious value or a jittered PRI modulation. Further, the continuity of the set after continuity correction is evaluated: if it possesses continuity characteristics, the PRI is determined to be a jittered signal; if it lacks continuity, it is identified as a spurious PRI.

[0100] Considering the characteristics of threshold settings, there is still a possibility that simply judging based on continuity might lead to problems in n... i To address the problem of misjudgment that can occur when the distribution is relatively uniform, this invention introduces the following judgment criteria:

[0101] min{n i :n i >μ+σ}-max{n i :n i <μ+σ}>max{μ,σ} (9)

[0102] Where μ is the mean of the current N, and σ is the standard deviation of the current N. This criterion requires that the difference between the minimum count of the histogram bin index set that satisfies the threshold condition and the maximum count of the remaining histogram bin indices should be greater than the mean and standard deviation of the current N.

[0103] If the above conditions are not met, the PRI value will also be considered a false PRI value. The false PRI will be stored in the corresponding false queue, and if... Figures 4-7 This presents a two-dimensional mapping diagram of the verified real PRI (including fixed PRI and staggered PRI modulation types). This invention extracts the histogram bin ordinate sequences of all satisfying conditions as the basis for subsequent harmonic analysis, and simultaneously obtains their corresponding TOA sequence indices to form a candidate radar pulse set.

[0104] 4. Harmonic interference suppression

[0105] The generation of harmonic phenomena essentially depends on the multiple relationship between the two-dimensional mapping width W and the actual pulse repetition interval. Here, we will continue with a detailed analysis using a fixed PRI modulation type as an example:

[0106] (1) If the width of the two-dimensional plane mapping is equal to the real PRI, i.e. W = PRI, according to the description in step 1), the two-dimensional mapping plane can effectively characterize the characteristic curve of the corresponding PRI, thus providing a reliable basis for subsequent pulse sorting, PRI value determination and modulation type identification.

[0107] (2) If the width of the two-dimensional plane mapping is an integer multiple of the real PRI, i.e., W = m·PRI, where m is a positive integer, let toa q with toa p Let toa be the arrival time of pulses from the same radar, with an interval of k·PRI, where k is any positive integer. q =q·PRI+r, According to the definition of PRI, r is a constant and 0 ≤ r < PRI, so the mapped coordinates are:

[0108]

[0109] It should be noted that since r is a constant, while y p x p Since it is obtained from the modulo or integer operation, the existence of the extra constant r only affects y. p x p The absolute numerical offset does not affect its relative distribution relationship.

[0110] From the above derivation, it can be seen that the horizontal coordinates tend to appear at m equally spaced positions, with the position distribution exhibiting an equidistant PRI interval pattern. The vertical coordinates, however, show continuity at their respective horizontal coordinate positions. Therefore, the two-dimensional mapping characteristics under this condition are: m characteristic curves appear within the observation window, and the vertical coordinates of each curve exhibit good continuity.

[0111] (3) If the actual PRI is an integer multiple of the width W of the two-dimensional plane mapping, i.e., PRI = m·W. Similarly, let toa q with toa p Given the arrival times of pulses from the same radar, the mapped coordinates can be obtained as follows:

[0112]

[0113] The derivation shows that, in this case, the abscissa remains nearly equal, while the ordinate values ​​no longer exhibit continuity, with the ordinate interval between adjacent pulses from the same radar being m. Therefore, the two-dimensional mapping characteristic under this condition is: a single characteristic curve appears within the observation window, but the ordinate of the curve loses its continuity.

[0114] Extending the above analysis to other PRI modulation schemes, their characteristic features follow the same pattern: when W = m·PRI, the number of characteristic curves becomes m times the mapping width of the true PRI, while the continuity of the ordinate remains unchanged; while when PRI = m·W, the number of characteristic curves remains unchanged, but the continuity of the ordinate decreases, and the interval between adjacent ordinates increases to m times that of W = PRI. Therefore, under a given PRI, the mapping condition of W = PRI has fewer characteristic curves and stronger continuity of the ordinate compared to W = m·PRI and PRI = m·W.

[0115] When W is a harmonic value, the correct pulse sequence can still be obtained and pulse sorting can be completed by histogram binning extraction. That is, ideally, the pulse extracted under PRI harmonics is consistent with the pulse extracted under true PRI, but errors may occur in determining the true PRI value and identifying the modulation type.

[0116] Therefore, to ensure the completeness and accuracy of the method, this invention first performs pairwise intersection operations on the extracted TOA sequence indices based on the characteristic that the pulse sequences extracted from the harmonics and their corresponding true PRI values ​​are identical. This merges PRI values ​​with similar TOA sequences into a unified subset, essentially grouping the true PRI values ​​and their harmonics into one set. Each subset is defined as containing one and only one true PRI value. The PRI value with the fewest characteristic curves and the strongest continuity on the ordinate is selected as the true PRI value, effectively suppressing harmonic interference and ensuring accurate identification of the PRI value and modulation type.

[0117] 5. PRI set optimization and integration

[0118] Based on the PRI set determined in step 4), the sequence data integration process is performed. First, the intersection of the false PRI set identified in step 3) and the current PRI set is calculated, and the PRIs and their corresponding sequences in the intersection are deleted. Simultaneously, the compensated jitter PRI queues and their corresponding sequences selected in step 2) are extracted and added to the true PRI value set and its corresponding TOA sequence. For each jitter PRI, only the corresponding TOA sequence within the maximum continuity range of the histogram binning is extracted. Through the above processing, the true PRI value set and its corresponding TOA sequence set are obtained.

[0119] For PRI modulation type identification, if the number of feature curves is 1, it is determined to be fixed PRI modulation; if the number of feature curves is greater than 1 and does not have histogram binning continuity, it is determined to be staggered PRI modulation; if the number of feature curves is much greater than 1 and clearly has histogram binning continuity, it is determined to be jittery PRI modulation; if the PRI originally belongs to the compensated jittery PRI queue, no discrimination is needed, and it is directly identified as jittery PRI modulation. This results in a complete output containing the set of true PRI values, the set of PRI modulation types, and the corresponding set of TOA sequences.

[0120] 6. Algorithm sorting performance evaluation and verification

[0121] The TOA sequence extracted by the algorithm is compared with the corresponding real radar TOA sequence. The data fault tolerance and interference fault tolerance capabilities of the method of the present invention are evaluated by the accuracy rate (ACC), false positive rate (FPR), and false negative rate (LPR) of radar radiation source signal pulse sorting, and the actual sorting effect of the method is verified. The formula is as follows:

[0122]

[0123] Example 2

[0124] Embodiment 2 of the present invention provides a computer device comprising: at least one processor, a memory, at least one network interface, and a user interface. The various components of the device are coupled together via a bus system. It is understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0125] The user interface may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0126] It is understood that the memory in the embodiments disclosed in this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0127] In some implementations, the memory stores elements such as executable modules or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0128] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. Programs implementing the methods of the embodiments of this disclosure can be included in the application programs.

[0129] In the above embodiments, the processor can also invoke programs or instructions stored in memory, specifically programs or instructions stored in an application program, for the following purposes:

[0130] The steps of performing the method of Example 1.

[0131] The method of Embodiment 1 can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in Embodiment 1. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in Embodiment 1 can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0132] It is understood that the embodiments described in this invention can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in this application, or combinations thereof.

[0133] For software implementation, the technology of this invention can be achieved by executing the functional modules (e.g., procedures, functions, etc.) of this invention. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally.

[0134] Example 3

[0135] Embodiment 3 of the present invention may also provide a computer program product, including a computer program / instructions. When the computer program / instructions are executed by a processor, the various steps in the above method embodiments can be implemented.

[0136] Simulation example:

[0137] The effectiveness of this invention can be verified using the following simulation data:

[0138] Seven radar radiation sources were set up, and their parameters are shown in Table 1. To simulate a real-world scenario, the radar clusters were similar in certain dimensions and the number of pulses was uneven. This invention considers the coexistence of multiple PRI modulation types, including staggered, fixed, and jitter modulation methods. Based on the parameter information provided in Table 1, radar pulse descriptor word (PDW) data received by the receiving station was generated. Taking a 1000ms pulse sequence as an example, 5-40% of the pulses in this sequence were randomly lost, and a 10% false alarm rate was also set. The number of pulses for each radar radiation source in Table 1 represents the original sequence without pulse loss or false alarms. The actual number of pulses detected by each radar is not constant under different false alarm and loss rates.

[0139] Table 1 Radar radiation source parameters

[0140]

[0141] Using the accuracy (ACC), false positive rate (FPR), and false negative rate (LPR) evaluation indicators from step 6), the specific sorting results of the example are shown in Table 2.

[0142] Table 2 Sorting Results of Simulation Examples

[0143]

[0144]

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A highly fault-tolerant radar signal sorting method based on improved plane transformation, comprising: Step 1: Process the received radar electromagnetic pulse signal to obtain the TOA sequence; Step 2: Based on the preset dynamic range and search step size of PRI, obtain the preset PRI array, apply entropy theory to complete the planar information entropy calculation of the TOA sequence, determine the local maxima, and form a preliminary PRI candidate set by constructing the PRI values ​​corresponding to all maxima. Step 3: Establish a jitter PRI supplementary identification mechanism. Perform jitter comprehensive judgment on each element value in the preset PRI array obtained in Step 2 in turn, and store the PRI value that is judged as jitter modulation into the compensation queue as jitter modulation. Step 4: Perform two-dimensional plane mapping on the preliminary PRI candidate set obtained in Step 2, extract the feature curves and their corresponding TOA sequences in sequence, and store the false PRI values ​​into the false PRI queue through PRI verification. Step 5: Perform pairwise intersection operations on the TOA sequence indices extracted in Step 4, and complete the true PRI determination to obtain the PRI set; Step 6: Based on the PRI set obtained in Step 5, integrate the sequence data, remove the false PRI queue from Step 4, and supplement the compensation queue from Step 3 to achieve optimized integration of the PRI set.

2. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 1, characterized in that, The TOA sequence obtained in step 1 contains the arrival times of n pulse signals {toa1, toa2, ..., toa...} n }, where toa n This indicates the arrival time of the nth pulse signal.

3. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 1, characterized in that, Step 2 includes: Step 2-1: Preset the dynamic range of PRI to [PRI min ,PRI max ], of which PRI min Set a minimum value for PRI. max Set a preset maximum value for PRI, set the search step size to step, and obtain the preset PRI array PRI. preset ; Step 2-2: Using PRI preset The number of elements contained is the number of bins M in the histogram; Steps 2-3: Using PRI respectively preset Each element in the matrix is ​​used as the width W of the two-dimensional plane mapping transformation; the number of pulses in the i-th sub-bin under the current W is counted as n. i The probability density p of the i-th bin is obtained. i for: In the formula, n is the total number of received pulses. The current W-plane information entropy H(W) is obtained according to the following formula: Steps 2-4: After traversing each element in the PRI array, perform a negative operation on the planar information entropy data to transform the optimization problem of detecting local minima of information entropy into the problem of detecting local maxima in the negative entropy function; Steps 2-5: Based on the changing trend of the planar information entropy value, an adaptive strategy is used to determine potential local maxima. The observation window width corresponding to the local maxima constitutes the preliminary PRI candidate set. cand .

4. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 3, characterized in that, Step 3 includes: Step 3-1: Using PRI respectively preset Each element value in the matrix serves as the width W of the two-dimensional plane mapping transformation and the number of histogram bins M; Step 3-2: Let the bin counting result N = {n1, n2, ..., n} i ,...,n M }, n i This represents the number of pulses in the i-th sub-bin, when the following formula is satisfied: n i >mean(N)+std(N) Preliminary determination indicates that this binning corresponds to one of the characteristic curves; where mean(N) and std(N) represent the mean and standard deviation, respectively; Step 3-3: For all characteristic curves obtained in Step 3-2, perform jitter comprehensive judgment on the data corresponding to the bin index. If the judgment condition is met, determine that the PRI value corresponding to W and M is jitter modulation and store it in the compensation queue.

5. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 4, characterized in that, The jitter comprehensive judgment in step 3-3 includes: simultaneously satisfying the continuity, range, and chi-square test criteria, wherein... The continuity determination includes: first, using a set relatively low threshold to preliminarily determine whether there is a continuous array in the set index; if a continuous array exists, it is corrected for continuity and the binning index is completed; then, using a set relatively high threshold to determine the continuous array, if both thresholds meet the continuity condition, the index is determined to meet the continuity condition. The range is determined as follows: the range of N is greater than the mean of N; The chi-square test determines that: if the chi-square test is performed on N, the corresponding value rejects the uniform distribution hypothesis.

6. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 1, characterized in that, Step 4 includes: Step 4-1: Preliminary PRI candidate set obtained from Step 2 cand Extract the histogram binning index set and the corresponding binning count result N = {n1, n2, ..., n}. M }, characteristic curves and corresponding TOA sequences; perform continuity correction on the histogram binning index set. If the set before and after correction is inconsistent and the corrected set does not have continuity characteristics, it indicates that the PRI is a false value and is stored in the false PRI queue; if the set before and after correction is consistent, proceed to step 4-2: Step 4-2: Iterate through the count results of each sub-box and make a judgment based on the following criteria: min{n i :n i >μ+σ}-max{n i :n i <μ+σ}>max{μ,σ} Where μ is the mean of N and σ is the standard deviation of N; If the condition is not met, the corresponding PRI value is considered a false PRI value and is stored in the false PRI queue.

7. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 1, characterized in that, Step 5 includes: Perform pairwise intersection operations on the TOA sequence indices extracted in step 4 to merge PRI values ​​with similar TOA sequences into a unified subset, that is, group the real PRI values ​​and their harmonics into one set; Each subset is identified as having one and only one true PRI value. The PRI value with the fewest feature curves and the strongest continuity of the ordinate in the subset is selected as the true PRI value, thus obtaining the current PRI set.

8. The high-fault-tolerant radar signal sorting method based on improved plane transformation according to claim 7, characterized in that, Step 6 includes: Calculate the intersection of the false PRI queues identified in step 4 and the current PRI set, and delete the PRIs and their corresponding TOA sequences from the intersection; The compensation queue from step 3 is extracted and added to the true PRI value set and its corresponding TOA sequence to obtain the true PRI value set and its corresponding TOA sequence set.

9. 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 of claim 1.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 1.

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