A method, device and medium for selecting ultrashort wave signals based on space-time-frequency characteristics

Abstract

The invention relates to a method for sorting ultrashort wave signals based on space-time-frequency characteristics, including the following steps: S1, obtaining spectrum and azimuth data; S2, time slot segmentation; S3, parameter setting; S4, data preprocessing; S5, single frequency point batch processing; S6, multi-frequency point batch processing; S7, completing signal sorting and outputting a signal list. Also disclosed is a device and a medium for implementing a method for sorting ultrashort wave signals based on space-time-frequency characteristics. The beneficial effects achieved by the present invention are: being able to accurately distinguish target signals incident from different directions with the same frequency, reducing the influence of noise on the signal parameter estimation results; and improving the accuracy of data processing.

Description

Technical Field

[0001] The invention relates to the field of electronic reconnaissance, and in particular to an ultrashort wave signal sorting method, device and medium based on space-time-frequency characteristics. Background Art

[0002] At present, in the process of detecting ultra-short wave signals in China, most of them use the time-frequency distribution characteristics of the signals in the monitoring frequency band (30MHz-3000MHz) to set the threshold, thereby completing the signal sorting work and finally generating the target signal table.

[0003] However, the existing ultrasonic signal sorting methods only use the time-frequency characteristics of the signal and cannot accurately distinguish target signals with the same frequency but incident from different directions. In addition, there are large errors in the estimation of the center frequency and bandwidth of broadband signals such as FM signals, which may also generate false target signals. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an ultrashort wave signal sorting method, device and medium based on space-time-frequency characteristics, which can automatically extract the main parameters (center frequency, bandwidth, directionality, appearance and cutoff time, duration) of the target signal in the monitoring frequency band, can distinguish the target signals incident from different directions at the same frequency, and reduce the signal parameter estimation error.

[0005] It should be noted that the existing ultrashort wave signal sorting method only uses the time-frequency characteristics of the signal, but does not use the space-time characteristics of the signal within the reconnaissance bandwidth, and cannot accurately distinguish target signals with the same frequency but incident from different directions. In addition, there are large errors in the estimation of the center frequency and bandwidth of broadband signals such as FM signals, and false target signals may also be generated.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for sorting ultrashort wave signals based on space-time-frequency characteristics, the method comprising:

[0008] S1. Obtain spectrum and directionality data

[0009] Obtain a data packet within a fixed observation time, the data packet containing spectrum data within the monitoring bandwidth and K frequency points in the spectrum as input data;

[0010] S2, time slot division

[0011] The fixed observation time is divided into N time slots, each of which contains M input data packets;

[0012] S3. Parameter setting

[0013] Calculate the time corresponding to a single time slot after segmentation;

[0014] According to the test accuracy index, set the single-frequency combined direction determination threshold and multi-frequency direction determination threshold;

[0015] Pre-set confidence decision threshold, multi-frequency point batch frequency decision threshold, and time continuity decision threshold;

[0016] S4. Data preprocessing to extract the spatial, temporal and frequency characteristics of ultrashort wave signals

[0017] S41, respectively calculating the mean values ​​of the azimuths of K frequency points in N time slots as the representative values ​​of the azimuths of the time slots;

[0018] S42, counting the number of m azimuth data of each frequency point in N time slots whose difference from the azimuth mean of the corresponding frequency point is less than the set angle value;

[0019] S43, calculating the confidence of the representative values ​​of the K frequency points in the N time slots;

[0020] S44, calculating the time slots and frequencies corresponding to the time when the confidence of all the representative values ​​of the azimuth is greater than the confidence decision threshold;

[0021] S5, single frequency point batch processing

[0022] That is, all the frequency points that meet the confidence judgment conditions are traversed, and according to the confidence and directionality mean parameters of these frequency points in different time slots, the signals appearing in different time periods of a single frequency point are found, and a single frequency point signal table is generated;

[0023] S6, multi-frequency batch processing

[0024] That is, according to the start and end time, direction, and frequency parameters of each signal in the single frequency signal table, multiple single frequency signals in the single frequency signal table are merged and processed;

[0025] S7, complete signal sorting and output signal list

[0026] When the fine merging in step S6 is completed, the fine merging signal parameters are calculated based on the single frequency signal parameters contained in each type of fine merging signal, and a signal table is generated and output. The signal table includes the signal center frequency, bandwidth, representative value of the directionality, maximum energy, average energy, duration, start and end time.

[0027] Further, for step S4:

[0028] 1) In S41, the average values ​​of the azimuths of the K frequency points in the N time slots are calculated respectively as the representative values ​​of the azimuths of the time slots:

[0029]

[0030] Among them, dir(k，n) It represents the representative value of the azimuth at the kth frequency point in time slot n. represents the mean azimuth of the kth frequency point in time slot n, dir (k，n，m) Indicates the directionality of the kth frequency point in the mth data packet in time slot n;

[0031] 2) In S42, when the number of m azimuth data of each frequency point in N time slots whose difference from the azimuth mean of the corresponding frequency point is less than the set angle value is counted:

[0032]

[0033] At this time, set the angle value to 3°.

[0034] Among them, rel_num (k，n) It indicates the number of m azimuths of the kth frequency point in time slot n whose difference from the mean azimuth of the kth frequency point in time slot n is less than 3°. It means counting the number of items that meet the condition [·] within the range of 1≤m≤M;

[0035] 3) In S43, when the confidence of the representative values ​​of the K frequency points in the N time slots is calculated:

[0036]

[0037] Among them, rel (k，n) Indicates the confidence level of the representative value of the k-th frequency in time slot n.

[0038] Furthermore, step S5 specifically includes:

[0039] S51, judging the state of the frequency point at different times according to the distribution of the confidence and the directivity representative value of the frequency point that meets the confidence judgment condition in each time slot;

[0040] The states include: signal appearance, signal termination, signal continuation, the previous signal termination and the next signal start, and no signal;

[0041] S52, after the signal status of the frequency point in step S51 is determined in all time slots, the parameters of the appearing signal are recorded;

[0042] The parameters include: frequency, start time, end time, duration, direction finding at the end point, mean direction finding during the duration, variance of direction finding during the duration, confidence of direction finding results during the duration, maximum signal energy during the duration, and mean signal energy during the duration.

[0043] S53, repeat steps S51, S52, until all frequencies in all time slots after the signal status is determined to be complete, based on the recorded data to generate a single frequency signal table.

[0044] In step S51, the basis for status determination is:

[0045] A. Signal appears

[0046] If the confidence of the kth frequency point in time slot n is greater than the confidence decision threshold, and the confidence in time slot n-1 is less than the confidence decision threshold, then the signal is considered to appear in time slot n, and the start time of time slot n is recorded as the start time of the signal, that is,

[0047] rel (k，n) ≥Confidence decision threshold&&rel (k，n-1) ≤Confidence decision threshold;

[0048] B. Signal cutoff

[0049] If the confidence of the kth frequency point in time slot n is less than the confidence decision threshold, and the confidence in time slot n-1 is greater than the confidence decision threshold, then the signal is considered to be terminated in time slot n, and the start time of time slot n is recorded as the signal termination time, that is,

[0050] rel (k，n) ≤Confidence decision threshold&&rel (k，n-1) ≥Confidence decision threshold;

[0051] C. Signal persistence

[0052] If the confidence of the kth frequency point in time slot n and time slot n-1 are both greater than the confidence decision threshold, and the difference between the representative values ​​of the azimuth in time slot n and time slot n-1 is less than the single frequency point combined azimuth decision threshold, then it is considered that the signal continues in time slot n, that is,

[0053] rel (k，n) ≥Confidence decision threshold&&rel (k，n-1) ≥Confidence decision threshold&&

[0054] |dir (k，n) -dir (k，n-1) |≤ single frequency point combined direction decision threshold;

[0055] D. The previous signal ends and the next signal starts

[0056] If the confidence of the kth frequency point in time slot n and time slot n-1 is greater than the confidence decision threshold, and the difference between the representative values ​​of the azimuth in time slot n and time slot n-1 is greater than the single frequency point combined azimuth decision threshold, then it is considered that the new signal starts at the start time of time slot n, and the previous signal ends at the start time of time slot n, that is,

[0057] rel (k，n) ≥Confidence decision threshold&&rel (k，n-1)≥Confidence decision threshold&&

[0058] |dir (k，n) -dir (k，n-1) |≥ single frequency combined direction decision threshold;

[0059] E. No signal

[0060] If the confidence of the kth frequency point in time slot n and time slot n-1 is less than the confidence decision threshold, it is considered that there is no signal at the kth frequency point in time slot n and time slot n-1, that is,

[0061] rel (k，n) ≤Confidence decision threshold&&rel (k，n-1) ≤Confidence decision threshold.

[0062] Further, step S6 specifically includes:

[0063] S61, rough merging process

[0064] That is, according to the directionality and frequency of each signal in the single frequency signal table, multiple single frequency signals are merged into a type of coarse merged signal until all signals in the single frequency signal table are coarsely merged;

[0065] S62, arranging the start and end times of the single frequency signals in each type of rough combined signal in ascending order according to the start time of the single frequency signal;

[0066] S63, fine merging and processing

[0067] That is, according to the relationship between the start and end time of the single-frequency signals after the arrangement of the various types of roughly combined signals, the states of the various types of signals after the rough combination are determined, and the signals appearing at different times in the various types of roughly combined signals are distinguished.

[0068] In step S61, the rough merging determination is based on:

[0069] A. If the minimum frequency difference between single-frequency signals is less than the frequency decision threshold for combining multiple frequencies, and the directionality difference is less than the directionality decision threshold for combining multiple frequencies, then these single-frequency signals are considered to be a type of coarse combined signal;

[0070] B. If the minimum frequency difference between single-frequency signals is greater than the frequency decision threshold for combining multiple frequencies, or the directionality difference is greater than the directionality decision threshold for combining multiple frequencies, then these single-frequency signals are not considered to be a type of coarse combined signal.

[0071] In step S63, the judgment basis for fine merging is:

[0072] A. For single-frequency point signals arranged sequentially in the same type of coarse combined signals, if the end time of the previous single-frequency point signal is greater than or equal to the start time of the next single-frequency point signal, all single-frequency point signals that meet the judgment condition are a type of fine combined signals;

[0073] B. For single-frequency point signals arranged sequentially in the same type of coarse combined signal, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is less than the time continuity judgment threshold, then the previous and next signals are considered to be the same type of fine combined signal;

[0074] C. For single-frequency point signals arranged sequentially in the same type of coarse combined signal, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is greater than the time continuity judgment threshold, then the previous and next signals are considered not to be the same type of fine combined signals.

[0075] In a second aspect, the present invention provides a device, comprising:

[0076] The processor is used to execute the steps of an ultrashort wave signal sorting method based on space-time-frequency characteristics.

[0077] In a third aspect, the present invention provides a computer-readable storage medium, including a program, which can be executed by a processor to support a method for sorting ultrashort wave signals based on space-time-frequency characteristics.

[0078] It should be noted that the traditional method only uses the decision threshold to implement data analysis and processing. The optimal threshold analytical formula is derived through the three error rate formulas of OOK, PPM, and DPIM; however, it is impossible to distinguish target signals with the same frequency but incident from different directions at different times (this is the technical problem to be solved by the present invention). For example, in a continuous period of time, two targets with different directions communicate with each other at the same frequency, and the communication interval is very short. At this time, the traditional method will judge the two signals as the same signal because there is no spatial domain information; after the present invention introduces the space-time-frequency feature, even if the cutoff time of signal 1 and the start time of signal 2 are very short, they can be distinguished as two signals with the same frequency from different directions.

[0079] It should be noted that, in the data packet in step S1 of this solution, K frequency point directivity in the spectrum is introduced, and the directivity is the space-time-frequency characteristic, so that target signals incident from different directions with the same frequency can be accurately distinguished.

[0080] In addition, the observation time is divided into multiple time slots (each time slot includes multiple input data packets); after data preprocessing, the azimuth representative value of the time slot is obtained by the oscillometric mean value of the corresponding frequency point in the time slot, and then the confidence is calculated, and only the time slots and frequencies corresponding to the confidence greater than the azimuth representative value and greater than the confidence decision threshold are counted (equivalent to screening out data with low confidence, preliminarily improving the accuracy); then according to the confidence decision condition, the state of the frequency point at different times (signal appearance, signal cutoff, signal continuation, previous signal cutoff and next signal start, signal) is found, the parameters of the signal that appears are recorded, and then Generate a single-frequency signal table (according to the state found by the confidence level, record the parameters that meet the confidence level, which is equivalent to removing the parameters that do not meet the confidence level, further improving the accuracy); then, merge multiple single-frequency signals into a type of coarse merged signal (during coarse merging, conditions must be met, and those that do not meet the conditions are removed, which further improves the accuracy); then, in each type of coarse merged signal, the starting time of the corresponding single-frequency signals is arranged in ascending order, and then fine merging is performed based on the single-frequency signals arranged in sequence in the same type of coarse merged signal that meets certain conditions (equivalent to removing the signals that cannot be finely merged, further improving the accuracy); finally, generate an output signal table.

[0081] In short, after dividing the observation time into multiple time slots, data preprocessing, coarse merging, and fine merging are used to screen out signals that meet the conditions and remove signals that do not meet the conditions; after step-by-step processing, the accuracy of the final output signal table is improved.

[0082] It should be noted that this solution can automatically extract the main parameters of the target signal in the monitoring frequency band (center frequency, bandwidth, directionality, appearance and cutoff time, and duration).

[0083] The present invention has the following advantages:

[0084] (1) By introducing the space-time characteristics of the signal in the monitoring frequency band, it is combined with the signal time-frequency characteristics to form a space-time-frequency feature, thereby increasing the feature space dimension; thus, it is possible to accurately distinguish target signals with the same frequency but different directions incident at different times;

[0085] After introducing directional features (with space-time-frequency features), the influence of noise on the signal parameter estimation results can be reduced, that is, the influence of other signals except the main reconnaissance target can be reduced;

[0086] (For example, two signals A and B appear at different times and from different directions at a certain frequency point. If signal A is the main reconnaissance target, signal B is the noise of signal A. If the directional characteristics are not used to distinguish, the signal appearance time, cut-off time, duration, and directionality in the sorting results may be wrong);

[0087] (2) Through data preprocessing, rough merging, and fine merging, data that does not meet the requirements is removed, so that the accuracy of the final output signal table is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a schematic diagram of the process of the present invention;

[0089] Figure 2 It is a schematic diagram of time slot division in a specific implementation manner. DETAILED DESCRIPTION

[0090] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0091] In the field of electronic reconnaissance, the current ultrasonic signal sorting method only utilizes the time-frequency characteristics of the signal and cannot reflect the spatial angular position, so it is impossible to accurately distinguish target signals incident from different directions with the same frequency. In addition, since there are large errors in the center frequency and bandwidth estimation of broadband signals such as FM signals, false target signals may also be generated. The traditional method cannot improve the accuracy very well through simple threshold processing. The present invention introduces the azimuth, so that the space-time-frequency characteristics are contained in the data processing, so that the target signals incident from different directions with the same frequency can be accurately distinguished. The present invention also improves the final accuracy through data preprocessing, coarse merging, and fine merging, thereby eliminating false target signals.

[0092] Embodiment 1

[0093] The flow chart of the present invention refers to Figure 1 , a method for sorting ultrashort wave signals based on space-time-frequency characteristics, comprising the following steps:

[0094] S1. Obtain spectrum and azimuth data, that is, obtain a data packet within a fixed observation time, the data packet containing spectrum data within the monitoring bandwidth and azimuths of K frequency points in the spectrum as input data.

[0095] In this embodiment, the direction finding device sends a data packet every 0.5 ms, and obtains 9600 data packets output by the direction finding device within 4.8 s. The data packets contain spectrum data within the monitoring bandwidth and 6400 frequency point direction indicators in the spectrum, which serve as input data for this technical solution.

[0096] S2, time slot segmentation, that is, dividing the fixed observation time into N time slots, each time slot contains M input data packets.

[0097] In this embodiment, Figure 2 As shown, the fixed observation time for acquiring input data is divided into 320 time slots, each of which contains 30 input data packets.

[0098] S3. Parameter setting.

[0099] Calculate the time corresponding to a single time slot after segmentation; set the single-frequency point combined direction decision threshold and the multi-frequency point direction decision threshold based on the tested accuracy index; pre-set the confidence decision threshold, the multi-frequency point combined frequency decision threshold, and the time continuity decision threshold.

[0100] In this embodiment, the time corresponding to a single time slot is calculated to be 15ms according to the time slot segmentation. The single-frequency point combined direction determination threshold is set to 4° and the multi-frequency point direction determination threshold is set to 4° according to the direction finding accuracy index of the direction finding equipment. Generally, the confidence determination threshold is set to 96%, the multi-frequency point combined frequency determination threshold is set to 60KHz, and the time continuity determination threshold is set to 0.3s for subsequent processing.

[0101] S4. Data preprocessing to extract the space-time-frequency characteristics of ultrashort wave signals.

[0102] The specific steps are described in the following embodiments:

[0103] S41, respectively calculate the average of the azimuth of 6400 frequency points in 320 time slots as the representative value of the azimuth of the time slot,

[0104]

[0105] Among them, dir (k，n) Indicates the representative value of the directivity of the kth frequency point in time slot n; represents the mean azimuth of the kth frequency point in time slot n, dir (k，n，m) It indicates the directionality of the kth frequency point in the mth data packet in time slot n.

[0106] S42, count the number of m azimuth data of each frequency point in 320 time slots that differ from the azimuth mean of the corresponding frequency point by less than 3°,

[0107]

[0108] Among them, rel_num (k，n) It indicates the number of m azimuths of the kth frequency point in time slot n whose difference from the mean azimuth of the kth frequency point in time slot n is less than 3°. It means counting the number of items that satisfy the condition [·] within the range of 1≤m≤M.

[0109] S43, calculate the confidence of the representative value of the azimuth of 6400 frequency points in 320 time slots,

[0110]

[0111] Among them, rel (k，n)Indicates the confidence level of the representative value of the k-th frequency in time slot n.

[0112] S44. Count the time slots and frequencies corresponding to the representative values ​​of all the azimuths when the confidence level is greater than 96%.

[0113] S5, single frequency point batch processing, that is, traversing all frequency points that meet the confidence judgment conditions, finding the signals appearing in different time periods in the single frequency point according to the confidence, directionality mean and other parameters of these frequency points in different time slots, and generating a single frequency point signal table.

[0114] The specific steps are described in the following implementation method:

[0115] S51, judging the state of the frequency point at different times according to the distribution of the confidence and the directivity representative value of the frequency point that meets the confidence judgment condition in each time slot;

[0116] The states include: signal appearance, signal end, signal continuation, the previous signal ends and the next signal starts, and no signal.

[0117] Furthermore, the specific implementation methods of the signal status determination are as follows:

[0118] A. Signal appears

[0119] If the confidence of the kth frequency point in time slot n is greater than 96%, and the confidence in time slot n-1 is less than 96%, then the signal is considered to appear in time slot n, and the start time of time slot n is recorded as the start time of the signal, that is,

[0120] rel (k，n) ≥96%&&rel (k，n-1) ≤96%;

[0121] B. Signal cutoff

[0122] If the confidence of the kth frequency point in time slot n is less than 96%, and the confidence in time slot n-1 is greater than 96%, then the signal is considered to be terminated in time slot n, and the start time of time slot n is recorded as the termination time of the signal, that is,

[0123] rel (k，n) ≤96%&&rel (k，n-1) ≥96%;

[0124] C. Signal persistence

[0125] If the confidence of the kth frequency point in time slot n and time slot n-1 is greater than 96%, and the difference between the representative values ​​of the directivity in time slot n and time slot n-1 is less than 4°, then the signal is considered to be continuous in time slot n, that is,

[0126] rel (k，n) ≥96%&&rel(k，n-1) ≥96%&&|dir (k，n) -dir (k，n-1) |≤4°;

[0127] D. The previous signal ends and the next signal starts

[0128] If the confidence of the kth frequency point in time slot n and time slot n-1 is greater than 96%, and the difference between the representative values ​​of the directivity in time slot n and time slot n-1 is greater than 4°, then it is considered that the new signal starts at the start time of time slot n and the previous signal ends at the start time of time slot n, that is,

[0129] rel (k，n) ≥96%&&rel (k，n-1) ≥96%&&|dir (k，n) -dir (k，n-1) |≥4°;

[0130] E. No signal

[0131] If the confidence of the kth frequency point in time slot n and time slot n-1 is less than the confidence decision threshold, it is considered that there is no signal at the kth frequency point in time slot n and time slot n-1, that is,

[0132] rel (k，n) ≤96%&&rel (k，n-1) ≤96%.

[0133] S52. After completing the signal status determination of the frequency point in all time slots, record the parameters of the appearing signal, including: frequency, start time, end time, duration, direction finding direction at the end point, mean direction finding direction within the duration, variance of direction finding direction within the duration, confidence level of the direction finding result within the duration, maximum signal energy within the duration, and mean signal energy within the duration.

[0134] S53, repeat 5.1 and 5.2 until the signal status of all frequency points in all time slots is determined, and then generate a single frequency point signal table based on the recorded data.

[0135] S6, multi-frequency point batch processing, that is, combining multiple single-frequency signals in the single-frequency point signal table according to the start and end time, direction, frequency and other parameters of each signal in the single-frequency point signal table.

[0136] S61, coarse merging processing, that is, merging multiple single-frequency point signals into a type of coarse combined signal according to the directionality and frequency of each signal in the single-frequency point signal table, until all signals in the single-frequency point signal table are coarsely combined.

[0137] Furthermore, the rough merging determination basis is described in the following implementation manner:

[0138] A. If the minimum frequency difference between single-frequency signals is less than 60KHz and the direction difference is less than 4°, these single-frequency signals are considered to be a type of coarse combined signal;

[0139] B. If the minimum frequency difference between single-frequency signals is greater than 60KHz, or the direction difference is greater than 4°, then these single-frequency signals are considered not to be a type of coarse combined signal.

[0140] S62, arranging the start and end times of the single frequency signals in each type of rough combined signal in ascending order according to the start time of the single frequency signal.

[0141] S63, fine merging processing, that is, judging the states of various types of signals after coarse merging according to the relationship between the start and end times of the single-frequency signals after the arrangement of various types of coarse merging signals, distinguishing the signals appearing at different times in various types of coarse merging signals, until all the coarse merging signals are finely merged.

[0142] Further, the determination basis of the fine merging process is described in the following embodiments:

[0143] A. For single-frequency point signals arranged sequentially in the same type of coarse combined signals, if the end time of the previous single-frequency point signal is greater than or equal to the start time of the next single-frequency point signal, all single-frequency point signals that meet the judgment condition are a type of fine combined signals;

[0144] B. For single-frequency point signals arranged sequentially in the same type of coarse combined signal, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is less than 0.3s, then the previous and next signals are considered to be the same type of fine combined signal;

[0145] C. For single-frequency point signals arranged sequentially in the same type of coarse combined signals, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is greater than 0.3s, then the previous and next signals are considered not to be the same type of fine combined signals.

[0146] S7, complete signal sorting and output signal list. After fine merging is completed, fine merging signal parameters are calculated based on the single frequency signal parameters contained in various fine merging signals, and a signal table is generated and output, which includes the signal center frequency, bandwidth, representative value of the azimuth, maximum energy, average energy, duration, start and end time.

[0147] Embodiment 2

[0148] Provided is a device or system, which includes a processor, the number of which can be multiple, and the multiple processors work together to execute the steps of an ultrashort wave signal sorting method based on space-time-frequency characteristics.

[0149] The device or system provided in this embodiment is applicable to the ultrashort wave signal sorting method based on space-time-frequency characteristics provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0150] Embodiment 3

[0151] A computer-readable storage medium is provided, on which a computer program is stored according to different connection measures. When the program is executed by a processor, the ultrashort wave signal sorting method based on space-time-frequency characteristics in any of the above embodiments can be implemented.

[0152] The above embodiments only express preferred implementation modes, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention.

Claims

1. A method for sorting ultrashort wave signals based on space-time-frequency characteristics, characterized in that: S1. Obtain spectrum and azimuth data Obtain a data packet within a fixed observation time, the data packet containing spectrum data within the monitoring bandwidth and K frequency points in the spectrum as input data; S2, time slot division The fixed observation time is divided into N time slots, each of which contains M input data packets; S3. Parameter setting Calculate the time corresponding to a single time slot after segmentation; According to the test accuracy index, set the single-frequency combined direction determination threshold and multi-frequency direction determination threshold; Pre-set confidence decision threshold, multi-frequency point batch frequency decision threshold, and time continuity decision threshold; S4. Data preprocessing to extract the spatial, temporal and frequency characteristics of ultrashort wave signals That is, the confidence of the representative values ​​of the azimuth of K frequency points in N time slots is calculated, and the corresponding time slots and frequencies when the confidence of all the representative values ​​of the azimuth is greater than the confidence decision threshold are counted; S5, single frequency point batch processing That is, all the frequency points that meet the confidence judgment conditions are traversed, and according to the confidence and directionality mean parameters of these frequency points in different time slots, the signals appearing in different time periods of a single frequency point are found, and a single frequency point signal table is generated; S6, multi-frequency batch processing That is, according to the start and end time, direction, and frequency parameters of each signal in the single frequency signal table, multiple single frequency signals in the single frequency signal table are merged and processed; S7, complete signal sorting and output signal list When the fine merging in step S6 is completed, the fine merging signal parameters are calculated based on the single frequency signal parameters contained in each type of fine merging signal, and a signal table is generated and output. The signal table includes the signal center frequency, bandwidth, representative value of the directionality, maximum energy, average energy, duration, start and end time.

2. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to claim 1, characterized in that: In the step S4, the specific steps include: S41, respectively calculating the mean values ​​of the azimuths of K frequency points in N time slots as the representative values ​​of the azimuths of the time slots; S42, counting the number of m azimuth data of each frequency point in N time slots whose difference from the azimuth mean of the corresponding frequency point is less than the set angle value; S43, calculating the confidence of the representative values ​​of the K frequency points in the N time slots; S44, calculating the time slots and frequencies corresponding to the time when the confidence of all the representative values ​​of the azimuth is greater than the confidence decision threshold.

3. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to claim 2, characterized in that: In the step S4: 1) In S41, the average values ​​of the azimuths of the K frequency points in the N time slots are calculated respectively as the representative values ​​of the azimuths of the time slots: Among them, dir (k,n) It represents the representative value of the azimuth at the kth frequency point in time slot n. represents the mean azimuth of the kth frequency point in time slot n, dir (k,n,m) Indicates the directionality of the kth frequency point in the mth data packet in time slot n; 2) In S42, when the number of m azimuth data of each frequency point in N time slots whose difference from the azimuth mean of the corresponding frequency point is less than the set angle value is counted: At this time, set the angle value to 3°. Among them, rel_num (k,n) It indicates the number of m azimuths of the kth frequency point in time slot n whose difference from the mean azimuth of the kth frequency point in time slot n is less than 3°. It means counting the number of items that meet the condition [·] within the range of 1≤m≤M; 3) In S43, when the confidence of the representative values ​​of the K frequency points in the N time slots is calculated: Among them, rel (k,n) Indicates the confidence level of the representative value of the k-th frequency in time slot n.

4. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to any one of claims 1 to 3, characterized in that: In the step S5, all the frequency points that meet the confidence judgment conditions are traversed, and the signals appearing in different time periods in a single frequency point are found according to the confidence and directionality mean parameters of these frequency points in different time slots, and a single frequency point signal table is generated, which specifically includes: S51, judging the state of the frequency point at different times according to the distribution of the confidence and the directivity representative value of the frequency point that meets the confidence judgment condition in each time slot; The states include: signal appearance, signal termination, signal continuation, the previous signal termination and the next signal start, and no signal; S52, after the signal status of the frequency point in step S51 is determined in all time slots, the parameters of the appearing signal are recorded; The parameters include: frequency, start time, end time, duration, direction finding at the end point, mean direction finding during the duration, variance of direction finding during the duration, confidence of direction finding results during the duration, maximum signal energy during the duration, and mean signal energy during the duration. S53, repeating steps S51 and S52 until the signal status of all frequency points in all time slots is determined, and generating a single frequency point signal table based on the recorded data.

5. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to claim 4, characterized in that: In the above S51, the state of the frequency point at different times is determined, and the state determination is based on the following: A. Signal appears If the confidence of the kth frequency point in time slot n is greater than the confidence decision threshold, and the confidence in time slot n-1 is less than the confidence decision threshold, then the signal is considered to appear in time slot n, and the start time of time slot n is recorded as the start time of the signal, that is, rel (k,n) ≥Confidence decision threshold&&rel (k,n-1) ≤Confidence decision threshold; B. Signal cutoff If the confidence of the kth frequency point in time slot n is less than the confidence decision threshold, and the confidence in time slot n-1 is greater than the confidence decision threshold, then the signal is considered to be terminated in time slot n, and the start time of time slot n is recorded as the signal termination time, that is, rel (k,n) ≤Confidence decision threshold&&rel (k,n-1) ≥Confidence decision threshold; C. Signal persistence If the confidence of the kth frequency point in time slot n and time slot n-1 are both greater than the confidence decision threshold, and the difference between the representative values ​​of the azimuth in time slot n and time slot n-1 is less than the single frequency point combined azimuth decision threshold, then it is considered that the signal continues in time slot n, that is, rel (k,n) ≥Confidence decision threshold&&rel (k,n-1) ≥Confidence decision threshold&& |dir (k,n) -dir (k,n-1) |≤ single frequency point combined direction decision threshold; D. The previous signal ends and the next signal starts If the confidence of the kth frequency point in time slot n and time slot n-1 is greater than the confidence decision threshold, and the difference between the representative values ​​of the azimuth in time slot n and time slot n-1 is greater than the single frequency point combined azimuth decision threshold, then it is considered that the new signal starts at the start time of time slot n, and the previous signal ends at the start time of time slot n, that is, rel (k,n) ≥Confidence decision threshold&&rel (k,n-1) ≥Confidence decision threshold&& |dir (k,n) -dir (k,n-1) |≥ single frequency combined direction decision threshold; E. No signal If the confidence of the kth frequency point in time slot n and time slot n-1 is less than the confidence decision threshold, it is considered that there is no signal at the kth frequency point in time slot n and time slot n-1, that is, rel (k,N) ≤Confidence decision threshold&&rel (k,n-1) ≤Confidence decision threshold.

6. A method for separating ultrashort wave signals based on space-time-frequency characteristics according to any one of claims 1, 2, 3 or 5, characterized in that: The step S6, according to the start and end time, the azimuth, and the frequency parameters of each signal in the single frequency signal table, combines multiple single frequency signals in the single frequency signal table, specifically including: S61, rough merging process That is, according to the directionality and frequency of each signal in the single frequency signal table, multiple single frequency signals are merged into a type of coarse merged signal until all signals in the single frequency signal table are coarsely merged; S62, arranging the start and end times of the single frequency signals in each type of rough combined signal in ascending order according to the start time of the single frequency signal; S63, fine merging and processing That is, according to the relationship between the start and end time of the single-frequency signals after the arrangement of the various types of roughly combined signals, the states of the various types of signals after the rough combination are determined, and the signals appearing at different times in the various types of roughly combined signals are distinguished.

7. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to claim 6, characterized in that: In the above S61, the rough merging determination basis is: A. If the minimum frequency difference between single-frequency signals is less than the frequency decision threshold for combining multiple frequencies, and the directionality difference is less than the directionality decision threshold for combining multiple frequencies, then these single-frequency signals are considered to be a type of coarse combined signal; B. If the minimum frequency difference between single-frequency signals is greater than the frequency decision threshold for combining multiple frequencies, or the directionality difference is greater than the directionality decision threshold for combining multiple frequencies, then these single-frequency signals are not considered to be a type of coarse combined signal.

8. The method for separating ultrashort wave signals based on space-time-frequency characteristics according to claim 7, characterized in that: In the above-mentioned S63, the judgment basis of fine merging is: A. For single-frequency point signals arranged sequentially in the same type of coarse combined signals, if the end time of the previous single-frequency point signal is greater than or equal to the start time of the next single-frequency point signal, all single-frequency point signals that meet the judgment condition are a type of fine combined signals; B. For single-frequency point signals arranged sequentially in the same type of coarse combined signal, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is less than the time continuity judgment threshold, then the previous and next signals are considered to be the same type of fine combined signal; C. For single-frequency point signals arranged sequentially in the same type of coarse combined signal, if the end time of the previous single-frequency signal is less than the start time of the next single-frequency signal, and the difference between the end time of the previous single-frequency signal and the start time of the next single-frequency signal is greater than the time continuity judgment threshold, then the previous and next signals are considered not to be the same type of fine combined signals.

9. A device for ultrashort wave signal sorting method based on space-time-frequency characteristics, characterized in that: include: A processor, used to execute the steps of an ultrashort wave signal sorting method based on space-time-frequency characteristics as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: It includes a program, which can be executed by a processor to support the completion of an ultrashort wave signal sorting method based on space-time-frequency characteristics as described in any one of claims 1 to 8.

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