A PRI staggered signal parameter estimation method based on folded time of arrival

Abstract

The application belongs to the technical field of radar reconnaissance, and is a PRI staggered signal parameter estimation method based on folded arrival time. According to the characteristics that the staggered signal alternately appears in multiple stable PRI periods, the pulse arrival time is folded by using the estimated pulse group repetition period, the first cluster center is determined first, all elements are clustered by setting a clustering threshold, the cluster center and the number of elements of all clusters are found, the real data judgment basis is set, the remaining clusters are sequentially judged by the real data judgment basis, and thus all real pulses are found. Then, the cluster results of adjacent real pulse arrival times are subtracted according to the real pulse arrival times, the main pulse arrival times in a single pulse group repetition period time are obtained, and the sequentially arranged PRI is estimated, so that the accurate estimation of the PRI parameters of the staggered signal is realized.

Description

Technical Field

[0001] This application belongs to the field of radar reconnaissance technology, and specifically relates to a method for estimating parameters of PRI staggered signals based on folded arrival time. Background Technology

[0002] Identification and parameter estimation of PRI modulation patterns in radar radiation source signals is a crucial branch of signal sorting and identification. Current research focuses primarily on PRI modulation pattern identification, with relatively simple parameter estimation methods and limited research. However, with the rapid increase in the number of radar radiation sources in the electromagnetic environment and the severe overlap of pulse signals detected by electronic reconnaissance equipment, pulse loss and interference in signal sorting are becoming increasingly serious, rendering traditional, simple parameter estimation methods inapplicable. Furthermore, the effective implementation of techniques such as smart jamming largely depends on the accuracy of PRI modulation type and parameters. The growing demand and declining performance create a sharp contradiction, hindering the advancement of electronic warfare countermeasures. Therefore, it is necessary to conduct research on PRI modulation parameter estimation techniques. Staggered PRI modulation is a typical PRI modulation pattern; research on staggered PRI parameter estimation techniques will contribute to a breakthrough in the overall PRI modulation parameter estimation technology. Summary of the Invention

[0003] The purpose of this application is to provide a method for estimating the parameters of staggered PRI signals based on folded arrival time, so as to solve the problem that it is difficult to accurately estimate the values ​​and order of staggered PRI signals under conditions of pulse loss and interference.

[0004] The technical solution of this application is: a method for estimating parameters of PRI staggered signals based on folded arrival time, including:

[0005] A set of staggered signals is obtained by sorting, and the arrival time sequence and pulse repetition period of the set of staggered signals are obtained.

[0006] The arrival time series is folded according to the pulse group repetition period to obtain the folded time series.

[0007] Define the first element of the folded time series as the first class center, and determine that the number of elements in the first class is 1;

[0008] Set a clustering threshold, and compare each element of the folded time series with the current cluster center. If the distance between the element and a current center is less than the threshold, the element is assigned to that cluster, and the cluster center is recalculated. If the distance between the current element and all current cluster centers is greater than the threshold, the element is determined to be a new cluster center. This process continues until all elements in all folded time series have been traversed, and the cluster centers and the number of elements in all clusters are obtained.

[0009] The number of clusters with a certain multiple of the number of elements in the largest cluster is used as the basis for judging the true data. The remaining clusters are judged according to the true data judgment criteria in turn. If the true data judgment criteria are met, the cluster is judged to be the true pulse arrival time; otherwise, it is judged to be other interference.

[0010] The difference between the clustering results of adjacent real pulse arrival times is calculated based on the actual pulse arrival times to obtain the pulse repetition interval of all adjacent clusters, and the PRI values ​​and order of the staggered signals are further obtained.

[0011] Preferably, after obtaining the time series and pulse group repetition period, the pulse loss rate and pulse spurious rate of the pulse can be further obtained, as follows:

[0012]

[0013]

[0014] Where N represents the length of the sorted pulse sequence, N L For a real pulse, N F This is an interference pulse.

[0015] Preferably, the specific formula for time folding is:

[0016] TOA Fold (n)=TOA(n)-TOA(1)-NUM(n)·T (3)

[0017]

[0018] Among them, TOA Fold (n) represents the folded time series, floor(·) represents rounding down, and NUM(n) represents the number of folds in the arrival time of the nth pulse.

[0019] Preferably, if |TOA main (1)+T-TOA main (N S If | < α, then the estimated pulse repetition interval is:

[0020] PRI i =TOA main (i+1)-TOA main (i), i = 1, ..., N S -1 (12)

[0021] If |TOA main (1)+T-TOA main (N S If |≥α, then the estimated pulse repetition interval is:

[0022]

[0023] Where α represents the decision threshold for when the first and last arrival times are the same, PRI i It refers to the pulse repetition interval of staggered signals arranged in sequence.

[0024] This application presents a method for estimating PRI staggered signal parameters based on folded arrival time. Taking advantage of the characteristic that staggered signals consist of multiple stable PRI cycles alternating, the method folds the pulse arrival time using the pulse group repetition period estimated by sorting. It then first determines the first cluster center and clusters all elements by setting a clustering threshold. After finding a new element in each cluster, a new cluster center is recalculated to ensure accuracy, until all cluster centers and element counts are found. Then, by setting a real data judgment criterion, the remaining clusters are sequentially judged using this criterion to find all the real pulses. Finally, the method subtracts the clustering results of adjacent real pulse arrival times based on the real pulse arrival time to obtain the arrival time of the main pulse within a single pulse group repetition period, thereby estimating the sequentially arranged PRI and achieving accurate estimation of the PRI parameters of the staggered signal. Attached Figure Description

[0025] To more clearly illustrate the technical solutions provided in this application, the accompanying drawings will be briefly described below. Obviously, the drawings described below are merely some embodiments of this application.

[0026] Figure 1 This is a schematic diagram of the overall process of this application;

[0027] Figure 2 This is a schematic diagram of the clustering statistics of folded arrival time in this application. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] A method for estimating parameters of PRI staggered signals based on folded arrival time, such as... Figure 1 As shown, it includes the following steps:

[0030] Step S100: A set of staggered signals is obtained through sorting, and the arrival time sequence TOA(n) and pulse repetition period T of the set of staggered signals are obtained; where n = 1, 2, ..., N, and N represents the length of the sorted pulse sequence. Compared with the pulse sequence transmitted by the radar, the sorted pulse sequence loses N... L One real pulse, increasing N F One interference pulse.

[0031] The pulse loss rate and pulse spurious rate are then:

[0032]

[0033]

[0034] For example, the obtained time series is A1, A2, A3, A4...A100, with a period of about 4 pulse intervals. For example, A1, A2, A3, and A4 form one period. When there are pulse loss, it is also possible that three pulses form one period, such as A5, A6, and A7.

[0035] Step S200: Perform time folding on the arriving time series TOA(n) according to the pulse group repetition period T to obtain the folded time series;

[0036] Preferably, the specific formula for time folding is as follows:

[0037] TOA Fold (n)=TOA(n)-TOA(1)-NUM(n)·T (3)

[0038]

[0039] Among them, TOA Fold (n) represents the folded time series, floor(·) represents rounding down, and NUM(n) represents the number of folds in the arrival time of the nth pulse.

[0040] Time folding is performed using formulas (3) and (4). Based on the above example, the folded time series is: A1, A2, A3, A4; A5, A6, A7; A8, A9, A10, A11...A100, where 1-100 are the folded time indices.

[0041] By utilizing the statistical distribution of folded arrival times, accurate estimation of the PRI value and sequence of staggered signals can be achieved even under conditions of pulse loss and severe interference.

[0042] Step S300: Perform cluster initialization, define the first element of the folded time series as the first cluster center, and determine that the number of elements in the first cluster is 1;

[0043] Right now:

[0044] TC(1)=TOA Fold (1),TN(1)=1,N C =1,m=1 (5)

[0045] Where TC(i) represents the center of the i-th class, TN(i) represents the number of elements in the i-th class, and i = 1, ..., N C N C This represents the number of clusters, and m represents the folding time index of the current cluster.

[0046] The above example illustrates that A1 is the first class center.

[0047] Step S400: Cluster the pulses

[0048] By performing clustering, pulses emitted at the same or nearly the same interval can be grouped into one unit. The values ​​of the pulses after clustering are basically the same, thus making it easier to identify the real pulses.

[0049] Specifically, a clustering threshold is set, and each element of the folded time series is compared with the current cluster center. That is, m = m + 1 is set. If m > N, it means that the clustering is complete and proceeds to the next step; otherwise, the remaining elements are compared in the order of the folded time index until all clustering is complete.

[0050] If the distance between an element and a current cluster center is less than a threshold, then the element is assigned to that cluster, and the cluster center is recalculated. If there exists a k such that |TC(k) - TOA Fold If (m)|<ΔT, it means that the new element belongs to the same cluster as the cluster center, and the clustering result of the kth cluster is updated:

[0051]

[0052]

[0053] Where k = 1, ..., N C ΔT represents the clustering threshold.

[0054] If the condition is not met, it means that the distance between the current element and all current cluster centers is greater than the threshold. In this case, the element is determined to be a new cluster center, and a new cluster category is added.

[0055] N C =N C +1 (8)

[0056] TC(N C ) = TFold (m)+1,TN(N C )=1 (9)

[0057] The process continues until all elements have been traversed within all folding time intervals, at which point the cluster centers and the number of elements for all clusters are obtained.

[0058] This step allows all elements to be clustered and all cluster centers to be found. Of course, the current clusters contain both real impulses and interference, so all elements need to be filtered to remove the interference.

[0059] Based on the examples above, we can see that the clustering of A1 is A1, A5, A8, A13...; and the clustering of A2 is A2, A6, A9, A14... etc.

[0060] Step S500: Filter real pulses

[0061] When interference exists, the number of clusters corresponding to interference will inevitably be much smaller than the number of clusters of real pulses. Therefore, by judging the size of each cluster, it is possible to determine which clusters are interferences and which clusters are real pulses. Since there is the possibility of pulse loss, when judging real pulses, other clusters can be judged by the multiple of the number of elements in the largest cluster being less than 1. This will allow for an accurate determination of whether they are real clusters.

[0062] Specifically, the number of elements in the largest cluster is used as a certain multiple of the number of elements in the true cluster as the criterion for determining the true data. The specific method is as follows:

[0063] First, find the class k corresponding to the largest cluster element. max :

[0064] k max ={k|TN(k)≥TN(l),l∈[1,N]} C ], k∈[1,N C ]} (10)

[0065] Preferably, the magnification factor is set to 0.5, but other magnification factors such as 0.6 and 0.7 are also within the range set in this application.

[0066] The remaining clusters are then judged according to the criteria for judging real data. If the criteria for judging real data are met, the cluster is judged to be the real pulse arrival time; otherwise, it is judged to be other interference.

[0067] Find classes with a number of elements greater than 0.5·TN(k) max The class ) is used to form a set S with its centers:

[0068] S={TC(k)|TN(k)≥0.5·TN(k max ),k∈[1,NC ]} (11)

[0069] Sort the elements of set S in ascending order to obtain the main pulse arrival time series (TOA). main (i), i = 1, ..., N S N S This represents the number of elements in set S.

[0070] If the number of remaining clusters is 3-5, then that cluster can be considered as interference.

[0071] Step S600: Subtract the clustering results of adjacent real pulse arrival times based on the real pulse arrival times to obtain the pulse repetition interval of all adjacent clusters, and further obtain the PRI values ​​and order of the staggered signals.

[0072] Preferably, when determining the pulse repetition interval, since there is a possibility of pulse loss, direct determination may lead to errors. Therefore, it is necessary to determine the cases of pulse loss and non-loss separately.

[0073] If |TOA main (1)+T-TOA main (N S If | < α, meaning there are no lost pulses, then the estimated pulse repetition interval is:

[0074] PRI i =TOA main (i+1)-TOA main (i), i = 1, ..., N S -1 (12)

[0075] If |TOA main (1)+T-TOA main (N S If |≥α, meaning there is pulse loss, then the estimated pulse repetition interval is:

[0076]

[0077] Where α represents the threshold for determining if the first and last arrival times are the same, PRI i It refers to the pulse repetition interval of staggered signals arranged in sequence.

[0078] This application leverages the characteristic of staggered signals, which consist of multiple stable PRI cycles alternating. It utilizes the pulse group repetition cycle estimated by sorting to fold the pulse arrival time. Then, it first determines the first cluster center and clusters all elements by setting a clustering threshold. After finding a new element in each cluster, a new cluster center is recalculated to ensure accuracy. This process continues until all cluster centers and element counts are found. Next, by setting a real data judgment criterion, the remaining clusters are sequentially judged using this criterion to find all the real pulses. Finally, the difference between the clustering results of adjacent real pulse arrival times is calculated based on the real pulse arrival time to obtain the main pulse arrival time within a single pulse group repetition cycle. This allows for the estimation of the sequentially arranged PRI, achieving accurate estimation of the PRI parameters for staggered signals.

[0079] The following is an example to illustrate this:

[0080] Suppose a staggered signal is composed of four PRI pulses at 37, 39, 33, and 41 μs in sequence, resulting in 400 pulses within a 16 ms timeframe. A measurement error of 0.2 μs exists, leading to 20% pulse loss and 5% spurious pulses after sorting. Folding arrival time clustering is then performed on the signal. Figure 2 The clustering results for a ΔT of 1 μs are presented. The figure shows that the main arrival times are 0.19, 39.15, 72.16, and 113.14 μs. The clustering threshold α is set to 0.5, resulting in PRI values ​​of 38.96, 33.02, 40.98, and 37.04 μs for the staggered signal. Since the PRI values ​​of the staggered signal are periodically repeated, the PRI parameter and order estimations can be considered accurate.

[0081] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change.

[0082] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0083] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for estimating parameters of PRI staggered signals based on folded arrival time, characterized in that, include: A set of staggered signals is obtained by sorting, and the arrival time sequence and pulse repetition period of the set of staggered signals are obtained. The arrival time series is folded according to the pulse group repetition period to obtain the folded time series. Define the first element of the folded time series as the first class center, and determine that the number of elements in the first class is 1; Set a clustering threshold, and compare each element of the folded time series with the current cluster center. If the distance between the element and a current center is less than the threshold, the element is assigned to that cluster, and the cluster center is recalculated. If the distance between the current element and all current cluster centers is greater than the threshold, the element is determined to be a new cluster center. This process continues until all elements in all folded time series have been traversed, and the cluster centers and the number of elements in all clusters are obtained. The number of clusters with a certain multiple of the number of elements in the largest cluster is used as the basis for judging the true data. The remaining clusters are judged according to the basis for judging the true data in turn. If the requirements of the basis for judging the true data are met, the cluster is judged to be the true pulse arrival time. Otherwise, it is judged as other interference; The difference between the clustering results of adjacent real pulse arrival times is calculated based on the real pulse arrival times to obtain the pulse repetition interval of all adjacent clusters, and the PRI values ​​and order of the staggered signals are further obtained.

2. The PRI staggered signal parameter estimation method based on folded arrival time as described in claim 1, characterized in that, After obtaining the time series and pulse group repetition period, the pulse loss rate and pulse spurious rate of the pulse can be further obtained as follows: ； ； in, This indicates the length of the pulse sequence obtained from sorting. For real pulses, This is an interference pulse.

3. The PRI staggered signal parameter estimation method based on folded arrival time as described in claim 1, characterized in that, The specific formula for time folding is as follows: ； ； in, Represents a folded time series. This indicates rounding down. This represents the number of folds in the arrival time of the nth pulse.

4. The PRI staggered signal parameter estimation method based on folded arrival time as described in claim 1, characterized in that, like The estimated pulse repetition interval is: ； like The estimated pulse repetition interval is: ； in, This indicates the threshold for determining if the first and last arrival times are the same. It refers to the pulse repetition interval of staggered signals arranged in sequence.

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