CN118131168A - Complex environment signal sorting system and method - Google Patents
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Abstract
The invention belongs to the field of digital communication, and particularly relates to a complex environment signal sorting system and method. The specific technical scheme is as follows: s1, pre-selecting: de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method; s2, constant repetition frequency judgment: finding out a pulse sequence belonging to a heavy frequency constant type from a pre-sorting result, and processing by adopting an improved SDIF algorithm; s3, performing spread verification: carrying out judgment of repetition frequency spread on the basis of repetition frequency constant judgment; s4, jitter and sliding judgment: and (3) identifying jitter/sliding signals, and sorting the signals by adopting a corrected PRI conversion method to sort out constant repetition frequency, dispersion of repetition frequency, jitter of repetition frequency and sliding of repetition frequency. The method solves the defect of low recognition rate of the conventional SDIF algorithm on the repeated frequency spread signal and solves the contradiction given by poor threshold.
Description
Technical Field
The invention belongs to the field of digital communication, and particularly relates to a complex environment signal sorting system and method.
Background
The radar signal sorting is a process of separating each single radar signal from irregularly overlapped signal streams, and the principle of the radar signal sorting is that the single pulse signals of each radar are separated from the pulse signals which are interweaved and mixed together by utilizing various received parameters (pulse arrival angle DOA, carrier frequency Fc, pulse width PW, pulse amplitude PA, pulse arrival time TOA and the like).
Currently, the signal sorting algorithm mainly comprises a cumulative difference histogram method, a sequence difference histogram method, a traditional PRI conversion method and an improved PRI conversion method. The cumulative difference histogram method and the sequence difference histogram method are mainly applied to separation of constant repetition frequency and staggered repetition frequency signals, and have the defect that setting of a staggered signal threshold is difficult if the staggered signal threshold is met; the traditional PRI conversion method and the improved PRI conversion method are suitable for sorting heavy frequency sliding and heavy frequency dithering signals, and have the defect of large operand. Therefore, no sorting method capable of sorting repetition frequency constant, repetition frequency spread, repetition frequency jitter and repetition frequency sliding simultaneously exists at present.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a complex environment signal sorting system and a complex environment signal sorting method.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a complex environment signal sorting method comprises the following steps:
S1, pre-selecting: de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method;
S2, constant repetition frequency judgment: finding out a pulse sequence belonging to a heavy frequency constant type from a pre-sorting result, and processing by adopting an improved SDIF algorithm;
s3, performing spread verification: carrying out judgment of repetition frequency spread on the basis of repetition frequency constant judgment;
S4, jitter and sliding judgment: and (3) identifying jitter/sliding signals, and sorting the signals by adopting a corrected PRI conversion method to sort out constant repetition frequency, dispersion of repetition frequency, jitter of repetition frequency and sliding of repetition frequency.
Preferably: the S1 comprises the following steps:
S11, taking a first pulse parameter of the current batch of PDWs, and marking the first pulse parameter as a first type; PDW is a pulse descriptor;
S12, sequentially taking down a pulse spread, calculating the minimum distance between DOA, PW, fc and the central value of all the current categories, classifying the minimum distance into the current category if the minimum distance is less than 3, otherwise, creating a new category; DOA is the angle; PW is pulse width; fc is the carrier frequency;
and S13, traversing all the categories every 1ms in the clustering process, and deleting the categories if the number of the current category clustering pulses is less than 5.
Preferably: the step S2 comprises the following steps:
S21, setting an order N=1, taking out one type of the pre-selection results in the S1, carrying out N-order differential PRI values on all pulse TOA of the pre-selection results, carrying out PRI clustering, and recording the pulse sequence number of each type of PRI; TOA is pulse arrival time; PRI is pulse repetition period;
S22, arranging PRI clustering results from large to small according to the pulse number, and taking the maximum first N category pulse numbers for summation;
s23, calculating the pulse quantity and the proportion of the total pulse quantity in S22, if the proportion exceeds 80%, and the PRI center values of the multiple types are close, considering the multiple types as the same constant type of repetition frequency, and deleting each type of pulse according to the pulse sequence number in all the pulses; otherwise, n=n+1, repeating S21 to S23;
S24, when N is more than 10, ending.
Preferably: the step S3 comprises the following steps:
s31, clustering the obtained constant repetition frequency sequences, and clustering according to whether pulse widths, angles and pulse repetition periods are close to each other or not;
s32, combining all sequences of each group, and simultaneously arranging the sequences from small to large according to the arrival time of the pulse, and performing first-order difference to obtain a first-order difference PRI;
S33, assuming the number of the sequences of the group is M, clustering the first-order differential PRI when M is more than 1, finding out the first M PRIs with the largest clustering number, if M is more than 8, considering the group as a sliding signal, otherwise, as a staggered signal;
S34, deleting all PDW dispersion of the group where the selected sliding signals/dispersion signals are located in the original PDW.
Preferably: in the step S4, identifying the jitter/slide signal includes:
s411, sequentially taking out the pulses of the pre-selected rest groups, and performing first-order difference on the pulse repetition period of the current group of pulses PDW to obtain a first-order difference PRI sequence;
s412, calculating the average value of the first-order differential PRI sequence, traversing the first-order differential PRI sequence, and if the current value is greater than 1.2 times of the average value, using the difference value of the first two adjacent PRI sequences as a slope, and recalculating the current value;
s413, carrying out first-order difference again and finding a position d smaller than 0 in the first-order difference sequence;
S414, respectively calculating sequences between adjacent d to perform straight line fitting, and calculating errors between the sequences between the adjacent d and the fitting, wherein if the errors are smaller than 0.1, the sliding type is considered, and otherwise, the sliding type is considered as the jitter type.
Preferably: in S4, sorting the signal, improving the existing modified PRI transformation method, includes:
s421, determining the maximum detection PRI range and determining the number of boxes;
s422, traversing all pulse TOAs, respectively calculating PRI difference values of the largest adjacent 3 steps each time, and calculating the range that the current PRI difference value possibly falls into a box;
S423, PRI conversion calculation is carried out when the range of the box possibly falls into, and the corresponding box is updated;
s424, finding out box numbers larger than a threshold value for all boxes;
S425, sliding window expansion corrosion is carried out on the result in S424;
s426, averaging boxes with the same signal range in the result of S425 to obtain a possible PRI value;
S427, setting the jitter/slide range to be 5% -30%, searching possible PRI values in all TOAs respectively, taking the jitter/slide range value with the largest number of searched pulses, and if the number of pulses is more than 80% of the total number of pulses at the moment, considering that the search is successful and deleting in all TOAs.
Corresponding to: a complex environment signal sorting system comprises a pre-sorting module, a repetition frequency constant judging module, a spread checking module and a jitter and sliding judging module;
The pre-selection module is used for de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method;
The repeated frequency constant judging module is used for finding out a pulse sequence belonging to a repeated frequency constant type from a pre-selection result and adopting an improved SDIF algorithm for processing;
The spread checking module is used for judging the spread of the repeated frequency on the basis of constant judgment of the repeated frequency;
The jitter and sliding judgment module is used for identifying jitter/sliding signals and sorting signals by adopting a corrected PRI conversion method to sort out repetition frequency constant, repetition frequency spread, repetition frequency jitter and repetition frequency sliding.
The invention has the following beneficial effects:
1. the sorting method solves the defects of the traditional SDIF algorithm on low recognition rate of the repeated frequency spread signals and the contradiction given by poor threshold, regards the repeated frequency spread as a plurality of groups of repeated frequency constancy, sorts the repeated frequency constancy by using the improved SDIF algorithm, and then checks the characteristics among the spread signal groups.
2. The sorting method solves the speed disadvantage of the traditional modified convertible PRI method, and greatly reduces the running time by adopting a mode of combining the calculation times of the reduction order and the range search. Is suitable for a complex electromagnetic environment with 10% of lost pulse and 12% of disordered false pulse.
Drawings
FIG. 1 is a block flow diagram of a signal sorting method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The technical means used in the examples are conventional means well known to those skilled in the art unless otherwise indicated.
As shown in fig. 1, the invention discloses a complex environment signal sorting method, which comprises the following steps:
S1, pre-selecting: the method aims at de-interlacing pulse streams and processing data by combining a dynamic clustering method. The specific implementation process of the S1 is as follows:
S11, taking a first pulse parameter of the current batch of PDWs, and marking the first pulse parameter as a first type; PDW is the pulse descriptor. It should be noted that the current batch of PDWs is composed of a large number of pulse PDWs, so the first pulse parameter is the first PDW parameter in all PDWs.
S12, sequentially taking down a pulse spread, calculating the minimum distance between DOA, PW, fc and the central value of all the current categories, classifying the minimum distance into the current category if the minimum distance is less than 3, otherwise, creating a new category; DOA is the angle; PW is pulse width; fc is the carrier frequency. It should be noted that, the central value herein refers to three values of DOA, PW, fc in the current category, that is, each time the next pulse is calculated and clustered, the distance between DOA, PW, fc of the next pulse and DOA, PW, fc in the current category is calculated, and if the distance is less than 3, the two parameters (DOA, PW, fc of the next pulse and DOA, PW, fc of the current category) are summed and averaged, respectively, to update the central value.
And S13, traversing all the categories every 1ms in the clustering process, and deleting the categories if the number of the current category clustering pulses is less than 5, so as to prevent excessive categories and cause memory explosion.
S2, constant repetition frequency judgment: and (3) finding out a pulse sequence belonging to the heavy frequency constant type from the pre-sorting result, and processing by adopting an improved SDIF algorithm. The specific implementation process of the S2 is as follows:
S21, setting an order N=1, taking out one type of the pre-selection results in the S1, carrying out N-order differential PRI values on all pulse TOA of the pre-selection results, carrying out PRI clustering, and recording the pulse sequence number of each type of PRI; TOA is pulse arrival time; PRI is the pulse repetition period.
S22, arranging PRI clustering results from large to small according to the pulse number, and taking the maximum first N category pulse numbers for summation.
S23, calculating the pulse quantity and the proportion of the total pulse quantity in S22, if the proportion exceeds 80%, and the PRI center values of the multiple types are close, the multiple types are considered to be the same heavy frequency constant type, and each type of pulse is deleted according to the pulse sequence number in all the pulses; otherwise, n=n+1, repeating S21 to S23. Here, "close to" means that the error is less than 1%.
S24, when N is more than 10, ending.
S3, performing spread verification: and carrying out the judgment of the repetition frequency spread on the basis of the repetition frequency constant judgment. The specific implementation process of the S3 is as follows:
S31, clustering the repetition frequency constant sequences obtained in the step S2, and performing grouping clustering according to the pulse width, the angle and the pulse repetition period of the repetition frequency constant sequences. Here, "close" means that the error is less than 1%.
S32, combining all sequences of each group, and simultaneously, arranging the sequences from small to large according to the arrival time of the pulses, and performing first-order difference to obtain a first-order difference PRI.
S33, assuming the number of the sequences of the group is M, clustering the first-order differential PRIs when M is more than 1, finding out the first M PRIs with the largest clustering number, if M is more than 8, considering the group as a sliding signal, otherwise, considering the group as a staggered signal.
S34, deleting all PDW dispersion of the group where the selected sliding signals/dispersion signals are located in the original PDW.
S4, jitter and sliding judgment: the decision on jitter and jitter is divided into two parts, one part is to identify jitter/jitter signals and the other part is to sort signals by using a modified PRI conversion method.
Further, in the step S4, the implementation process of identifying the jitter/slide signal is as follows:
s411, sequentially taking out the pulses of the pre-selected rest groups, and performing first-order difference on the pulse repetition period of the current group of pulses PDW to obtain a first-order difference PRI sequence;
S412, calculating the average value of the first-order differential PRI sequence, traversing the first-order differential PRI sequence, and if the current value is greater than 1.2 times of the average value, using the difference value of the first two adjacent PRI sequences as a slope, and recalculating the current value; the method comprises the following steps:
Wherein, Representing the number of PRI sequences,/>Represent the mean value/>Indicating the ith PRI sequence value.
S413, carrying out first-order difference on the calculated value in the S412 again and finding a position d smaller than 0 in the first-order difference sequence; the method comprises the following steps:
step one: re-doing the first order difference
Step two: finding a position less than 0 in the first order differential sequence
Wherein,Representing the number of PRI sequences,/>To redo the first order differential result,/>Representing the number of redo first order differential results,/>Indicating a position number less than 0 in the first order difference.
S414, respectively calculating sequences between adjacent d to perform straight line fitting, and calculating errors between the sequences between the adjacent d and the fitting, wherein if the errors are smaller than 0.1, the type is considered as a sliding type, otherwise, the type is considered as a shaking type. The method comprises the following steps:
Wherein, Representation/>Number,/>Representing intermediate variables,/>Represents the i < th > neighbor >Slope of fitted line between,/>Representing the variance.
Further, in the step S4, the existing modified PRI conversion method is improved by sorting signals, and the specific implementation process is as follows:
S421, determining the maximum detection PRI range and determining the number of boxes; empirically, the range is typically defined as 1ms.
S422, traversing all pulse TOAs, calculating PRI difference values of maximum adjacent 3 steps each time, and calculating that the current PRI difference value possibly falls into the range of the box.
S423, PRI conversion calculation is carried out when the range possibly falls into the range of the box, and the corresponding box is updated.
S424, finding bin numbers larger than a threshold value for all bins.
S425, sliding window expansion corrosion is carried out on the result in S424, so that the purpose of keeping sampling is achieved.
S426, the bins for the same signal range in the result of S425 will be averaged as possible PRI values.
S427, setting the jitter/slide range to be 5% -30%, searching possible PRI values in all TOAs respectively, taking the jitter/slide range value with the largest number of searched pulses, and if the number of pulses is more than 80% of the total number of pulses at the moment, considering that the search is successful and deleting in all TOAs.
So far, the repetition frequency is constant, the repetition frequency is staggered, and the repetition frequency jitter and the repetition frequency sliding are all sorted out.
The invention also discloses a complex environment signal sorting system which comprises a pre-sorting module, a repetition frequency constant judging module, a spread checking module and a shaking and sliding judging module; the pre-selection module is used for de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method; the repeated frequency constant judging module is used for finding out a pulse sequence belonging to a repeated frequency constant type from a pre-selection result and adopting an improved SDIF algorithm for processing; the spread checking module is used for judging the spread of the repeated frequency on the basis of constant judgment of the repeated frequency; the jitter and sliding judgment module is used for identifying jitter/sliding signals and sorting signals by adopting a corrected PRI conversion method to sort out repetition frequency constant, repetition frequency spread, repetition frequency jitter and repetition frequency sliding. The implementation process of each module is referred to the above sorting method, and will not be described herein.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications, variations, alterations, substitutions made by those skilled in the art to the technical solution of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the design of the present invention.
Claims (7)
1. A complex environment signal sorting method is characterized in that: the method comprises the following steps:
S1, pre-selecting: de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method;
S2, constant repetition frequency judgment: finding out a pulse sequence belonging to a heavy frequency constant type from a pre-sorting result, and processing by adopting an improved SDIF algorithm;
s3, performing spread verification: carrying out judgment of repetition frequency spread on the basis of repetition frequency constant judgment;
S4, jitter and sliding judgment: and (3) identifying jitter/sliding signals, and sorting the signals by adopting a corrected PRI conversion method to sort out constant repetition frequency, dispersion of repetition frequency, jitter of repetition frequency and sliding of repetition frequency.
2. The complex environmental signal sorting method according to claim 1, characterized in that: the S1 comprises the following steps:
S11, taking a first pulse parameter of the current batch of PDWs, and marking the first pulse parameter as a first type; PDW is a pulse descriptor;
S12, sequentially taking down a pulse spread, calculating the minimum distance between DOA, PW, fc and the central value of all the current categories, classifying the minimum distance into the current category if the minimum distance is less than 3, otherwise, creating a new category; DOA is the angle; PW is pulse width; fc is the carrier frequency;
and S13, traversing all the categories every 1ms in the clustering process, and deleting the categories if the number of the current category clustering pulses is less than 5.
3. The complex environmental signal sorting method according to claim 2, characterized in that: the step S2 comprises the following steps:
S21, setting an order N=1, taking out one type of the pre-selection results in the S1, carrying out N-order differential PRI values on all pulse TOA of the pre-selection results, carrying out PRI clustering, and recording the pulse sequence number of each type of PRI; TOA is pulse arrival time; PRI is pulse repetition period;
S22, arranging PRI clustering results from large to small according to the pulse number, and taking the maximum first N category pulse numbers for summation;
s23, calculating the pulse quantity and the proportion of the total pulse quantity in S22, if the proportion exceeds 80%, and the PRI center values of the multiple types are close, considering the multiple types as the same constant type of repetition frequency, and deleting each type of pulse according to the pulse sequence number in all the pulses; otherwise, n=n+1, repeating S21 to S23;
S24, when N is more than 10, ending.
4. A complex environmental signal sorting method according to claim 3, characterized in that: the step S3 comprises the following steps:
s31, clustering the obtained constant repetition frequency sequences, and clustering according to whether pulse widths, angles and pulse repetition periods are close to each other or not;
s32, combining all sequences of each group, and simultaneously arranging the sequences from small to large according to the arrival time of the pulse, and performing first-order difference to obtain a first-order difference PRI;
S33, assuming the number of the sequences of the group is M, clustering the first-order differential PRI when M is more than 1, finding out the first M PRIs with the largest clustering number, if M is more than 8, considering the group as a sliding signal, otherwise, as a staggered signal;
S34, deleting all PDW dispersion of the group where the selected sliding signals/dispersion signals are located in the original PDW.
5. The complex environmental signal sorting method according to claim 4, wherein: in the step S4, identifying the jitter/slide signal includes:
s411, sequentially taking out the pulses of the pre-selected rest groups, and performing first-order difference on the pulse repetition period of the current group of pulses PDW to obtain a first-order difference PRI sequence;
s412, calculating the average value of the first-order differential PRI sequence, traversing the first-order differential PRI sequence, and if the current value is greater than 1.2 times of the average value, using the difference value of the first two adjacent PRI sequences as a slope, and recalculating the current value;
s413, carrying out first-order difference again and finding a position d smaller than 0 in the first-order difference sequence;
S414, respectively calculating sequences between adjacent d to perform straight line fitting, and calculating errors between the sequences between the adjacent d and the fitting, wherein if the errors are smaller than 0.1, the sliding type is considered, and otherwise, the sliding type is considered as the jitter type.
6. The complex environmental signal sorting method according to claim 5, wherein: in S4, sorting the signal, improving the existing modified PRI transformation method, includes:
s421, determining the maximum detection PRI range and determining the number of boxes;
s422, traversing all pulse TOAs, respectively calculating PRI difference values of the largest adjacent 3 steps each time, and calculating the range that the current PRI difference value possibly falls into a box;
S423, PRI conversion calculation is carried out when the range of the box possibly falls into, and the corresponding box is updated;
s424, finding out box numbers larger than a threshold value for all boxes;
S425, sliding window expansion corrosion is carried out on the result in S424;
s426, averaging boxes with the same signal range in the result of S425 to obtain a possible PRI value;
S427, setting the jitter/slide range to be 5% -30%, searching possible PRI values in all TOAs respectively, taking the jitter/slide range value with the largest number of searched pulses, and if the number of pulses is more than 80% of the total number of pulses at the moment, considering that the search is successful and deleting in all TOAs.
7. A sorting system according to the method of any one of claims 1-6, characterized in that: the device comprises a pre-sorting module, a repetition frequency constant judging module, a spread checking module and a shaking and sliding judging module;
The pre-selection module is used for de-interlacing the pulse stream and carrying out data processing by combining a dynamic clustering method;
The repeated frequency constant judging module is used for finding out a pulse sequence belonging to a repeated frequency constant type from a pre-selection result and adopting an improved SDIF algorithm for processing;
The spread checking module is used for judging the spread of the repeated frequency on the basis of constant judgment of the repeated frequency;
The jitter and sliding judgment module is used for identifying jitter/sliding signals and sorting signals by adopting a corrected PRI conversion method to sort out repetition frequency constant, repetition frequency spread, repetition frequency jitter and repetition frequency sliding.
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