CN115755042A - Improved radar multi-target two-dimensional fuzzy solution method - Google Patents
Improved radar multi-target two-dimensional fuzzy solution method Download PDFInfo
- Publication number
- CN115755042A CN115755042A CN202211605586.5A CN202211605586A CN115755042A CN 115755042 A CN115755042 A CN 115755042A CN 202211605586 A CN202211605586 A CN 202211605586A CN 115755042 A CN115755042 A CN 115755042A
- Authority
- CN
- China
- Prior art keywords
- target
- distance
- speed
- solving
- ambiguity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses an improved radar multi-target two-dimensional fuzzy solution method, which comprises the following steps: arranging the trace point data after radar signal processing from small to large according to the pulse repetition period; constructing a reference lookup table for solving the distance ambiguity based on the arranged data; constructing a speed ambiguity solving reference lookup table based on the arranged data; traversing all repetition frequency combinations transmitted by the radar, and selecting N groups from the M groups of repetition frequencies to carry out multi-target pairing; obtaining a distance matching table based on a reference lookup table for solving the distance ambiguity, calculating a standard deviation between each row of the distance matching table, and solving a target real distance; obtaining a speed matching table based on a reference lookup table for solving the speed ambiguity, calculating a standard deviation between each row of the speed matching table, and solving a target real speed; and merging the output target data. The method can solve the problem that the traditional one-dimensional set method is difficult to meet the real-time requirement when the target quantity is large and the ambiguity is large.
Description
Technical Field
The invention relates to the technical field of radar ambiguity resolution, in particular to an improved radar multi-target two-dimensional ambiguity resolution method.
Background
The traditional radar ambiguity resolution method comprises a residual error table look-up method, a residual theorem method, a one-dimensional set method and the like.
The residual error lookup method has the main advantage that when the range of the distance measurement is not large, the real distance of the target can be quickly matched by looking up the table according to the difference between the apparent distances. However, as the range of the target is increased, the storage space is large, the query time is slow, and the real-time signal processing requirement is difficult to achieve.
The residual theorem (also called Chinese remainder method) requires that the data number/distance gate number in several groups of PRI spaces are relatively prime in pairs, i.e. after several groups of PRI are multiplied by the signal bandwidth, the relation of mutual prime in pairs is satisfied. The remaining rational method can obtain correct results when there is no distance measurement error. But when there is an error, the solution is erroneous.
The one-dimensional set method (also called least square method) can solve two-dimensional ambiguity of distance and speed at the same time, and has good fault tolerance and higher reliability. However, due to the sorting and mean square error calculation, when the target has a large ambiguity and a large number of targets, the calculation amount increases rapidly, and it is difficult to meet the real-time requirement.
Han Gongbo in its published paper "improved algorithm for ambiguity resolution in radar signal detection" an improved algorithm based on one-dimensional set algorithm and its implementation steps are described. The thesis mainly improves the sorting part in the one-dimensional set algorithm, adopts the idea of a table look-up method to replace sorting, and does not consider the problems of speed ambiguity, blind areas and multiple targets.
Huang Chinese and the like in the thesis 'method for detecting multiple targets in single beam of airborne radar and realization' adopts a one-dimensional set method to solve range ambiguity. For multiple targets, the paper adopts a pairwise matching method, however, the method needs to know the target number in advance, and when the target number is unknown, the problem of false alarm exists by using all the re-frequency de-ambiguities or selecting several groups of re-frequency de-ambiguities.
Disclosure of Invention
Aiming at solving the problems of low operation speed, serious false alarm and false alarm missing problem and over-ideal verification environment of the existing solution fuzzy algorithm in the face of multiple targets, the invention provides an improved radar solution multi-target two-dimensional fuzzy method, aiming at the problem of low operation speed of a one-dimensional set algorithm, a distance table and a velocity table which are irrelevant to a target are constructed in advance, and corresponding target measurement values are added in the subsequent calculation process, so that the operation amount of sequencing and solving mean square error can be obviously reduced; in consideration of the problem of missed alarm caused by blind speed and blind distance, traversing all repeated frequency combinations, and selecting N groups from M groups of repeated frequencies to carry out multi-target pairing, wherein M is the number of transmitted repeated frequency groups, and N is the minimum number of repeated frequency groups required by ambiguity resolution; in order to avoid the false alarm problem caused by multi-target data interleaving, the corresponding speed of the N groups of data is further solved after the target distance matching is successful, and the false alarm is eliminated through whether the speed is matched or not; aiming at the problem that one target can be judged into a plurality of similar targets by the 'N/M' criterion, the distance and the speed of the solved target are further merged, and the problem of false alarm caused by target splitting is reduced.
The technical scheme adopted by the invention is as follows:
an improved radar multi-target two-dimensional fuzzy solving method comprises the following steps:
s1, arranging trace point data after radar signal processing from small to large according to a pulse repetition Period (PRI);
s2, constructing a reference lookup table TableR for solving distance ambiguity based on the arranged data;
s3, constructing a speed ambiguity solving reference lookup table TableV based on the arranged data;
s4, traversing all repeated frequency combinations transmitted by the radar, and selecting N groups from the M groups of repeated frequencies to carry out multi-target pairing;
s5, solving the real distance of the target: obtaining a distance matching Table Table1 based on a reference lookup Table TableR for resolving distance ambiguity, calculating the standard deviation between rows of the distance matching Table Table1, and if the minimum standard deviation is less than a threshold T R Description of target distanceSuccessfully performing the separation pairing, and calculating the mean value of the N data corresponding to the column with the minimum standard deviation as the target real distance Ran;
s6, solving the target real speed: when the target distance is successfully paired, obtaining a speed pairing Table Table2 based on a reference lookup Table TableV for solving speed ambiguity, calculating the standard deviation between each row of the speed pairing Table Table2, and if the minimum standard deviation is smaller than a threshold T V When the target speed matching is successful, calculating the average value of the N data corresponding to the row as the target real speed Vel, and outputting the target data Tar = [ Ran, vel = [ Vel, vel ])]；
S7, merging the output target data: and repeating the step S4 to the step S6 until all repetition frequency combinations of the target pairs are traversed, merging the output target real distance and the target real speed, and reducing target splitting.
Further, step S2 comprises the following sub-steps:
s201, selecting a first group of pulse repetition periods PRI as a reference period, listing all possible distance values corresponding to the reference period, and calculating all possible distance values of other non-reference periods;
s202, establishing a table of all possible distance values corresponding to each group of repeated frequencies, wherein the row number of the table is the number M of the repeated frequencies transmitted by the radar, the column number of the table is the number M +1 of all the possible distances corresponding to the reference period, and the distance values R in other repeated frequencies are placed on the columns corresponding to the first value which is larger than or equal to R in the reference repeated frequencies;
and S203, filling the spare items in the table into the previous distance value in the repetition frequency to obtain a reference lookup table TableR for solving the distance ambiguity.
Further, all possible distance values corresponding to the reference period in step S201 are:
R 1,p ＝[R 1,0 ,R 1,1 ,...,R 1,m ]
wherein R is 1,i ＝i·R 1,u ，i＝0,1,...,m，
For radar with a maximum detection range of R max Maximum number of ambiguities in time, R 1,u ＝PRI 1 C/2 is the reference repetition frequency PRI 1 Corresponding unambiguous distance.
Further, step S3 comprises the following sub-steps:
s301, selecting the last group of pulse repetition periods PRI as a reference period, and listing all possible speeds corresponding to the reference period:
V M,p ＝[V 1,-k ,...,V 1,-2 ,V 1,-1 ,V 1,0 ,V 1,1 ,V 1,2 ,...,V 1,k ]
wherein V M,i ＝i·V M,u ，i＝-k,...,-1,0,1,...,k，
For radar with a maximum detection velocity of V max Maximum number of ambiguities in time, V M,u ＝1/PRI M λ/2 is the reference repetition frequency PRI M Corresponding unambiguous speed; similarly, all possible speed values of other non-reference periods are calculated;
s302, establishing a table of all possible speed values corresponding to each group of repetition frequencies, wherein the row number of the table is the repetition frequency number M, the column number of the table is the number 2k +1 of all possible speeds corresponding to the reference period, and the speed values V in other repetition frequencies are placed in the column corresponding to the first value more than or equal to V in the reference repetition frequencies;
and S303, filling spare items in the table into a previous speed value in the repetition frequency to obtain a reference lookup table TableV for solving the speed ambiguity.
Further, step S4 comprises the following sub-steps:
s401, slave array [1,2, …, M]In total of N, in total
Species combinations, provided that the ith combination is [ i ] 1 ,i 2 ,...,i N ]；
S402, from the ith 1 ,i 2 ,...,i N Sequentially selecting 1 target data from the group repetition frequency, assuming that the ith target data is selected from q The selected target measuring distance and measuring speed in the group repetition frequency are respectively
And
wherein q =1,2.
Further, step S5 comprises the following sub-steps:
s501, extracting [ i ] th of a reference lookup table TableR for solving distance ambiguity 1 ,i 2 ,...,i N ]Line, for the ith 1 Line, plus target measurement distance
For the other rows, if
Is directly added with
If it is not
Then add to
Wherein
Is the ith q Grouping the unambiguous distances corresponding to the repetition frequencies, thereby obtaining a distance matching Table1, wherein the row number of the Table is N, the column number is m +1, and the values in the Table are all possible distance values of the targets on each repetition frequency;
s502, calculating the standard deviation between the rows of the distance matching Table Table1, and if the minimum standard deviation is smaller than a threshold T R And calculating the mean value of the N data corresponding to the column with the minimum standard deviation as the real distance Ran of the target.
Further, step S6 comprises the following sub-steps:
s601, extracting [ i ] th of a reference lookup table TableV for solving velocity ambiguity 1 ,i 2 ,...,i N ]Line, for the ith N Line, plus target measurement speed
For the other rows, if
Is directly added with
If it is not
Then add to
Wherein
Is the ith q Grouping the unsharp speeds corresponding to the repetition frequencies to obtain a speed matching Table Table2, wherein the number of rows in the Table is N, the number of columns in the Table is 2k +1, and the values in the Table are all possible speed values of the target on each repetition frequency;
s602, calculating the standard deviation between the rows of the speed matching Table Table2, and if the minimum standard deviation is less than the threshold T V Calculating the average value of the N data corresponding to the column as the real speed Vel of the target, and outputting the target data Tar = [ Ran, vel =]Otherwise, the target is not output.
The invention has the beneficial effects that:
the invention aims at solving the problem of multi-target two-dimensional ambiguity resolution by radar, adopts an improved ambiguity resolution method, combines the advantages of a table look-up method and a one-dimensional set method, and solves the problem that the traditional one-dimensional set method is difficult to meet the real-time requirement when the number of targets is large and the ambiguity is large. In the given example, the operation speed is 80 times faster than that of the traditional one-dimensional set method when 4 targets exist; and when 6 targets are available, the operation speed is 580 times faster than that of the traditional one-dimensional set method. In addition, the invention also provides a corresponding solution to the problems of false alarm and false alarm of the traditional fuzzy solution method, and the false alarm rate are obviously reduced in the simulation example.
Drawings
FIG. 1 is a flow chart of an improved radar multi-target two-dimensional ambiguity resolution method.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, specific embodiments of the present invention will now be described. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Examples
As shown in fig. 1, the embodiment provides an improved method for radar solution of multi-target two-dimensional ambiguity, which includes the following steps:
s1, arranging trace point data after radar signal processing from small to large according to a pulse repetition Period (PRI);
s2, constructing a reference lookup table TableR for solving distance ambiguity based on the arranged data;
s3, constructing a speed ambiguity solving reference lookup table TableV based on the arranged data;
s4, traversing all repeated frequency combinations transmitted by the radar, and selecting N groups from the M groups of repeated frequencies to carry out multi-target pairing;
s5, solving the real distance of the target: obtaining a distance matching Table Table1 based on a reference lookup Table TableR for resolving distance ambiguity, calculating the standard deviation between rows of the distance matching Table Table1, and if the minimum standard deviation is less than a threshold T R If the target distance matching is successful, calculating the average value of the N data corresponding to the column with the minimum standard deviation as the target true distance Ran;
s6, solving the real speed of the target: when the target distance is successfully paired, obtaining a speed pairing Table Table2 based on a reference lookup Table TableV for solving speed ambiguity, calculating the standard deviation between each row of the speed pairing Table Table2, and if the minimum standard deviation is smaller than a threshold T V When the target speed matching is successful, calculating the average value of the N data corresponding to the row as the target real speed Vel, and outputting the target data Tar = [ Ran, vel = [ Vel, vel ])]；
S7, merging the output target data: and repeating the step S4 to the step S6 until all repetition frequency combinations of the target pairs are traversed, merging the output target real distance and the target real speed, and reducing target splitting.
The 'N/M' ambiguity resolution criterion can judge a target into a plurality of similar targets, the distances and the speeds of the plurality of targets are the same when no measurement error exists, and the measurement error generally does not exceed 1 radar resolution unit size when the measurement error exists. Therefore, objects for which the distance difference and the speed difference do not exceed 1 resolution unit size are merged into 1 object.
In the Lei Dajie multi-target two-dimensional fuzzy method of the embodiment, for the problem of low operation speed of the one-dimensional set algorithm, a distance table and a speed table which are irrelevant to a target are constructed in advance, and corresponding target measurement values are added in the subsequent calculation process, so that the operation amount of sequencing and solving mean square error can be greatly reduced. And in consideration of the problem of missed alarm caused by blind speed and blind distance, traversing all the repeated frequency combinations, and selecting N groups from M groups of repeated frequencies to carry out multi-target pairing, wherein M is the number of transmitted repeated frequency groups, and N is the minimum number of repeated frequency groups required by ambiguity resolution. In order to avoid the false alarm problem caused by multi-target data interleaving, the speed corresponding to the N groups of data is further solved after the target distance matching is successful, and the false alarm is eliminated through whether the speed is matched or not. Aiming at the problem that one target can be judged into a plurality of similar targets by the 'N/M' criterion, the distance and the speed of the solved target are further merged, and the problem of false alarm caused by target splitting is reduced.
Preferably, step S2 comprises the following sub-steps:
s201, selecting a first group of pulse repetition periods PRI as a reference period, listing all possible distance values corresponding to the reference period, and calculating all possible distance values of other non-reference periods;
s202, establishing a table of all possible distance values corresponding to each group of repeated frequencies, wherein the row number of the table is the number M of the repeated frequencies transmitted by the radar, the column number of the table is the number M +1 of all the possible distances corresponding to the reference period, and the distance values R in other repeated frequencies are placed on the columns corresponding to the first value which is larger than or equal to R in the reference repeated frequencies;
and S203, filling the spare items in the table into the previous distance value in the repetition frequency to obtain a reference lookup table TableR for solving the distance ambiguity.
Preferably, all possible distance values corresponding to the reference period in step S201 are:
R 1,p ＝[R 1,0 ,R 1,1 ,...,R 1,m ]
wherein R is 1,i ＝i·R 1,u ，i＝0,1,...,m，
For radar with a maximum detection range of R max Maximum number of ambiguities in time, R 1,u ＝PRI 1 C/2 is the reference repetition frequency PRI 1 Corresponding unambiguous distance.
Preferably, step S3 comprises the following sub-steps:
s301, selecting the last group of pulse repetition periods PRI as a reference period, and listing all possible speeds corresponding to the reference period:
V M,p ＝[V 1,-k ,...,V 1,-2 ,V 1,-1 ,V 1,0 ,V 1,1 ,V 1,2 ,...,V 1,k ]
wherein V M,i ＝i·V M,u ，i＝-k,...,-1,0,1,...,k，
For radar with a maximum detection velocity of V max Maximum number of ambiguities in time, V M,u ＝1/PRI M λ/2 is the reference repetition frequency PRI M Corresponding unambiguous speed; similarly, all possible speed values of other non-reference periods are calculated;
s302, establishing a table of all possible speed values corresponding to each group of repetition frequencies, wherein the row number of the table is the repetition frequency number M, the column number of the table is the number 2k +1 of all possible speeds corresponding to the reference period, and the speed values V in other repetition frequencies are placed in the column corresponding to the first value more than or equal to V in the reference repetition frequencies;
and S303, filling the spare items in the table into the previous speed value in the repetition frequency to obtain a reference lookup table TableV for solving the speed ambiguity.
Preferably, step S4 comprises the following sub-steps:
s401, slave array [1,2, …, M]In total, N numbers are selected
Species combinations, provided that the ith combination is [ i ] 1 ,i 2 ,...,i N ]；
S402, from the ith 1 ,i 2 ,...,i N Sequentially selecting 1 target data from the group repetition frequency, assuming that the ith target data is selected from q The selected target measuring distance and measuring speed in the group repetition frequency are respectively
And
wherein q =1,2.
Preferably, step S5 comprises the following sub-steps:
s501, extracting [ i ] th of a reference lookup table TableR for solving distance ambiguity 1 ,i 2 ,...,i N ]Line, for the ith 1 Line, plus target measurement distance
For the other rows, if
Is directly added with
If it is used
Then add to
Wherein
Is the ith q Grouping the unambiguous distances corresponding to the repetition frequencies, thereby obtaining a distance matching Table1, wherein the row number of the Table is N, the column number is m +1, and the values in the Table are all possible distance values of the target on each repetition frequency;
s502, calculating the standard deviation between the rows of the distance matching Table Table1, and if the minimum standard deviation is smaller than a threshold T R And calculating the mean value of the N data corresponding to the column with the minimum standard deviation as the real distance Ran of the target.
Preferably, step S6 comprises the following sub-steps:
s601, extracting [ i ] th of a reference lookup table TableV for solving velocity ambiguity 1 ,i 2 ,...,i N ]Line, for the ith N Line, plus target measurement speed
For the other rows, if
Is directly added with
If it is not
Then add to
Wherein
Is the ith q Grouping the unsharp speeds corresponding to the repetition frequencies to obtain a speed matching Table Table2, wherein the number of rows in the Table is N, the number of columns in the Table is 2k +1, and the values in the Table are all possible speed values of the target on each repetition frequency;
s602, calculating the standard deviation between the rows of the speed matching Table Table2, if the standard deviation is minimumStandard deviation less than threshold T V Calculating the average value of the N data corresponding to the column as the real speed Vel of the target, and outputting the target data Tar = [ Ran, vel =]Otherwise, the target is not output.
Verification example
In order to verify the implementation steps and performance of the improved ambiguity resolution method in multi-target two-dimensional ambiguity resolution, a simulation data set with certain errors is constructed. Suppose that the radar transmits 6 sets of repetition frequency periods: [23, 25, 27, 29, 31, 34] us, the minimum repetition frequency required for deblurring is 3, the radar pulse width is 2.5us, the radar bandwidth is 5MHz, and the number of coherent pulses per repetition frequency is 256. Therefore, the radar distance resolution unit is 30m, the speed resolution unit is about 1.7m/s, the measurement error is in 1 resolution unit, the blind distance is 375m, the blind speed is 3m/s, the maximum detection speed of the radar is 10000m/s, and the maximum detection distance is 1000km. The number of targets in the same beam is 4 and the true range, true velocity, apparent range detected and apparent velocity are shown in table 1.
TABLE1 true and measured distances of targets in simulation test
Object 1 and object 2 are at the same distance but at different speeds, and object 3 and object 4 are at the same speed but at different distances. During radar detection, due to the shielding of the blind area, some repetition frequencies cannot detect a certain target and are indicated by a mark.
The specific implementation steps of the verification example are as follows:
s1, arranging trace point data after radar signal processing from small to large according to a pulse repetition Period (PRI).
S2, constructing a reference lookup table TableR for solving the distance ambiguity.
S201, selecting a first group of PRIs as a reference period, and listing all possible distance values corresponding to the reference period:
R 1,p ＝[0,3450,6900,...,993600]
wherein the reference repetition frequency PRI 1 The corresponding unambiguous distance is 3450m. Similarly, all possible distance values for other non-reference periods are calculated.
S202, all possible distance values corresponding to each group of repeated frequencies are tabulated, the row number of the tabulation is the number 6 of the repeated frequencies transmitted by the radar, and the column number of the tabulation is the number 289 of all possible distances corresponding to the reference period. And placing the distance values R in other repetition frequencies on the column corresponding to the first value more than or equal to R in the reference repetition frequency.
And S203, filling the spare items in the table into the previous distance value in the repetition frequency to obtain a reference lookup table TableR for solving the distance ambiguity.
S3, constructing a reference lookup table for solving velocity ambiguity
S301, selecting the last group of PRI as a reference period, listing all possible speeds corresponding to the reference period, and including:
V 6,p ＝[-10435,...,-652,0,652,...,10435]
wherein the reference repetition frequency PRI 6 The corresponding unambiguous speed is 652m/s. Similarly, all possible velocity values for other non-reference periods are calculated.
S302, all possible speed values corresponding to each group of repeated frequencies are tabulated, the row number of the tabulation is the repeated frequency number 6, and the column number of the tabulation is the number 47 of all possible speeds corresponding to the reference period. And placing the velocity values V in other repetition frequencies in a column corresponding to the first value more than or equal to V in the reference repetition frequency.
S303, filling the spare items in the table into the previous speed value in the repetition frequency to obtain a reference lookup table TableV for solving the speed ambiguity:
and S4, selecting 3 groups from the 6 groups of repeated frequencies to carry out multi-target pairing.
S401. Select 3 numbers from the array [1,2, …,6], there are 20 combinations, assuming that the ith combination is [1,2,4], i =1,2.
S402, sequentially selecting 1 target data from the 1 st, 2 nd and 4 th groups of repeated frequencies, wherein the distance and the speed of the selected data are 2059m, 2496m, 852m, 247.3m/s, 300.7m/s and 123.6m/s respectively.
And S5, solving the true distance of the target corresponding to the 3 groups of data.
S501, extracting the 1 st, 2 nd and 4 th lines of a distance reference lookup table TableR, and adding a target measurement distance 2059 to the 1 st line; for row 2, since 2496 is greater than 2059, 2496 is added directly; for row 4, because 852 is less than 2059 and 2059+4350 are added, a distance matching Table1 is obtained, the row number of the Table is 3, the column number is 289, the values in the Table are all possible distance values S502 of the targets on each repetition frequency, and the standard deviation between the rows of the distance matching Table1 is calculated. If the minimum standard deviation is less than the threshold of 30m, the target distance matching is successful, and the mean value of 3 data corresponding to the column is calculated and used as the real distance Ran of the target. For example, the minimum value of the standard deviation obtained by the calculation is 6.5m and is smaller than the threshold 30m, which indicates that there may be a real target distance, and the target distance is 39994m.
And S6, when the target distance is successfully paired, solving the speed of the 3 data.
S601, extracting lines 1,2 and 4 of a speed reference lookup table TableV, and adding a target measurement speed 123.6 corresponding to a fourth group of repetition frequencies to the line 4; for line 1, since measurement 247.3 is greater than 123.6, 247.3 is added directly; for line 2, since 300.7 is greater than 123.6, 300.7 is added directly to obtain velocity matching Table2. The number of rows in the table is 3 and the number of columns 27, the values in the table being all possible velocity values for the target at each repetition frequency.
S602, calculating the standard deviation between the rows of the speed matching Table Table2. If the minimum standard deviation is smaller than the threshold of 1.7m/s, calculating the mean value of the 3 data corresponding to the column as the real speed Vel of the target, and outputting the target Tar = [ Ran, vel ], otherwise, not outputting the target. For example, the minimum value of the standard deviation obtained by the calculation is 0.51m/s and is smaller than the threshold of 1.7m/s, which indicates that the target real speed may exist, the obtained target speed is 900.3m/s, and the output target information Tar = [39994m,900.3m/s ].
And S7, repeating the step S4 to the step S6 until all combinations of target pairs are traversed, merging the output target data, and reducing target splitting. All objects for which the distance difference did not exceed 30m and the speed difference did not exceed 1.7m/s were merged into one object.
In order to compare performances, the one-dimensional set method (method 1) in the Huangzhonghua paper and the improved one-dimensional set method (method 2) in the Han Hong wave paper are expanded to solve the distance and speed two-dimensional ambiguity, namely, the speed ambiguity is solved after the distance pairing is successful. The object merging method in step S7 of the present invention is also applied to both methods. And verifying through the simulation data set provided by the verification example, and selecting the second, third and sixth groups of repeated frequencies with the most measurement targets to resolve the ambiguity. The deblurring results are shown in table 2:
TABLE2 deblurring results in simulation experiments
Since object 3 is not detected in the second set of the repetition frequencies, method 1 and method 2 do not output an object when a fixed repetition frequency is selected for deblurring, whereas in the deblurring method set forth in the present invention, 4 objects are output. In addition, the method of the invention also has obvious advantages in the aspect of operation speed.
More generally, the performance of the method of the invention is verified when the target distance and velocity are randomly distributed within a certain range. Using 1000 monte carlo tests, the average time consumption, average false alarm rate (false alarm rate = number of true targets missed by deblurring/number of true targets), average false alarm rate (false alarm rate = number of non-true targets/total number of targets output by deblurring) and number of true targets are counted, as shown in table 3:
TABLE 3 Monte Carlo simulation test results 1000 times
Therefore, the radar solution multi-target two-dimensional fuzzy method provided by the invention has the advantages that the operation time consumption, the false alarm rate and the false alarm rate are obviously reduced.
It should be noted that the foregoing method embodiments are described as a series of acts or combinations for simplicity in description, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Claims (7)
1. An improved radar multi-target two-dimensional fuzzy solving method is characterized by comprising the following steps:
s1, arranging trace point data after radar signal processing from small to large according to a pulse repetition Period (PRI);
s2, constructing a reference lookup table TableR for solving distance ambiguity based on the arranged data;
s3, constructing a speed ambiguity solving reference lookup table TableV based on the arranged data;
s4, traversing all repeated frequency combinations transmitted by the radar, and selecting N groups from the M groups of repeated frequencies to carry out multi-target pairing;
s5, solving the real distance of the target: obtaining a distance matching Table Table1 based on a reference lookup Table TableR for resolving distance ambiguity, calculating the standard deviation between rows of the distance matching Table Table1, and if the minimum standard deviation is less than a threshold T R If the target distance matching is successful, calculating the average value of the N data corresponding to the column with the minimum standard deviation as the target real distance Ran;
s6, solving the target real speed: when the target distance is successfully paired, obtaining a speed pairing Table Table2 based on a reference lookup Table TableV for solving speed ambiguity, and calculatingThe standard deviation between rows of the velocity matching Table Table2, if the smallest standard deviation is less than the threshold T V If the target speed pairing is successful, calculating a mean value of N data corresponding to the row as a target real speed Vel, and outputting target data Tar = [ Ran, vel = [ Vel, vel ]]；
S7, merging the output target data: and repeating the step S4 to the step S6 until all repetition frequency combinations of the target pairs are traversed, merging the output target real distance and the target real speed, and reducing target splitting.
2. The improved radar solution multi-target two-dimensional fuzzy method of claim 1, wherein the step S2 comprises the following sub-steps:
s201, selecting a first group of pulse repetition periods PRI as reference periods, listing all possible distance values corresponding to the reference periods, and calculating all possible distance values of other non-reference periods;
s202, establishing a table of all possible distance values corresponding to each group of repeated frequencies, wherein the row number of the table is the number M of the repeated frequencies transmitted by the radar, the column number of the table is the number M +1 of all the possible distances corresponding to the reference period, and the distance values R in other repeated frequencies are placed on the columns corresponding to the first value which is larger than or equal to R in the reference repeated frequencies;
and S203, filling spare items in the table into the previous distance value in the repetition frequency to obtain a reference lookup table TableR for resolving the distance ambiguity.
3. The improved radar multi-target two-dimensional ambiguity resolution method of claim 2, wherein all the possible distance values corresponding to the reference period in step S201 are:
R 1,p ＝[R 1,0 ,R 1,1 ,...,R 1,m ]
wherein R is 1,i ＝i·R 1,u ，i＝0,1,...,m，
For radar with a maximum detection range of R max Maximum number of ambiguities in time, R 1,u ＝PRI 1 C/2 is the reference repetition frequency PRI 1 Corresponding unambiguous distance.
4. The improved radar solution multi-target two-dimensional fuzzy method of claim 3, wherein the step S3 comprises the following sub-steps:
s301, selecting the last group of pulse repetition periods PRI as reference periods, and listing all possible speeds corresponding to the reference periods:
V M,p ＝[V 1,-k ,...,V 1,-2 ,V 1,-1 ,V 1,0 ,V 1,1 ,V 1,2 ,...,V 1,k ]
wherein V M,i ＝i·V M,u ，i＝-k,...,-1,0,1,...,k，
For radar with a maximum detection velocity of V max Maximum number of ambiguities in time, V M,u ＝1/PRI M λ/2 is the reference repetition frequency PRI M Corresponding unambiguous speed; similarly, all possible speed values of other non-reference periods are calculated;
s302, establishing a table of all possible speed values corresponding to each group of repetition frequencies, wherein the row number of the table is the repetition frequency number M, the column number of the table is the number 2k +1 of all possible speeds corresponding to the reference period, and the speed values V in other repetition frequencies are placed in the column corresponding to the first value more than or equal to V in the reference repetition frequencies;
and S303, filling the spare items in the table into the previous speed value in the repetition frequency to obtain a reference lookup table TableV for solving the speed ambiguity.
5. The improved radar solution multi-target two-dimensional fuzzy method of claim 4, wherein the step S4 comprises the following sub-steps:
s401, slave array [1,2, …, M]In total of N, in total
Species combinations, provided that the ith combination is [ i ] 1 ,i 2 ,...,i N ]；
S402, from the ith 1 ,i 2 ,...,i N Sequentially selecting 1 target data from the group repetition frequency, assuming that the ith target data is selected from q The selected target measuring distance and measuring speed in the group repetition frequency are respectively
And
wherein q =1,2.
6. The improved radar solution multi-target two-dimensional fuzzy method of claim 5, wherein the step S5 comprises the following sub-steps:
s501, extracting [ i ] th of a reference lookup table TableR for solving distance ambiguity 1 ,i 2 ,...,i N ]Line, for the ith 1 Line, plus target measurement distance R i1 For the other rows, if
Is directly added with
If it is used
Then add to
Wherein
Is the ith q Grouping the unambiguous distances corresponding to the repetition frequencies, thereby obtaining a distance matching Table1, wherein the row number of the Table is N, the column number is m +1, and the values in the Table are all possible distance values of the target on each repetition frequency;
s502, calculating the standard deviation between the rows of the distance matching Table Table1, and if the minimum standard deviation is less thanThreshold T R And calculating the mean value of the N data corresponding to the column with the minimum standard deviation as the real distance Ran of the target.
7. The improved radar solution multi-target two-dimensional fuzzy method of claim 6, wherein the step S6 comprises the following sub-steps:
s601, extracting [ i ] th of a reference lookup table TableV for solving velocity ambiguity 1 ,i 2 ,...,i N ]Line, for the ith N Line, plus target measurement speed
For the other rows, if
Is directly added with
If it is not
Then add to
Wherein
Is the ith q Grouping the unsharp speeds corresponding to the repetition frequencies to obtain a speed matching Table Table2, wherein the number of rows in the Table is N, the number of columns in the Table is 2k +1, and the values in the Table are all possible speed values of the target on each repetition frequency;
s602, calculating the standard deviation between the rows of the speed matching Table Table2, and if the minimum standard deviation is less than the threshold T V Calculating the average value of the N data corresponding to the column as the real speed Vel of the target, and outputting the target data Tar = [ Ran, vel =]Otherwise, the target is not output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211605586.5A CN115755042A (en) | 2022-12-14 | 2022-12-14 | Improved radar multi-target two-dimensional fuzzy solution method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211605586.5A CN115755042A (en) | 2022-12-14 | 2022-12-14 | Improved radar multi-target two-dimensional fuzzy solution method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115755042A true CN115755042A (en) | 2023-03-07 |
Family
ID=85345888
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211605586.5A Pending CN115755042A (en) | 2022-12-14 | 2022-12-14 | Improved radar multi-target two-dimensional fuzzy solution method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115755042A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449329A (en) * | 2023-04-27 | 2023-07-18 | 深圳承泰科技有限公司 | Method, system, equipment and storage medium for disambiguating speed of millimeter wave radar |
| CN116755073A (en) * | 2023-06-21 | 2023-09-15 | 上海雷骥电子科技有限公司 | Method for resolving distance ambiguity by using lookup table and application |
-
2022
- 2022-12-14 CN CN202211605586.5A patent/CN115755042A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449329A (en) * | 2023-04-27 | 2023-07-18 | 深圳承泰科技有限公司 | Method, system, equipment and storage medium for disambiguating speed of millimeter wave radar |
| CN116755073A (en) * | 2023-06-21 | 2023-09-15 | 上海雷骥电子科技有限公司 | Method for resolving distance ambiguity by using lookup table and application |
| CN116755073B (en) * | 2023-06-21 | 2024-03-26 | 上海雷骥电子科技有限公司 | Method for resolving distance ambiguity by using lookup table and application |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115755042A (en) | Improved radar multi-target two-dimensional fuzzy solution method | |
| EP3452845B1 (en) | Ambiguous radar range resolution using range lookup table | |
| CN103364770B (en) | Radar target detecting system based on matrix filling and detecting method thereof | |
| EP1903682B1 (en) | Detection of a non-uniformly sampled sinusoidal signal and a Doppler sensor utilizing the same | |
| CN108919224A (en) | The adaptive fusion detection method of broadband radar target based on skew symmetry structure | |
| KR20120010457A (en) | Os-cfar detection method which stimulated computational complexity | |
| EP3396420B1 (en) | Method and device for determining overall measurement of seismic observation system repeatability | |
| CN110531330B (en) | Bistatic MIMO radar array diagnosis method based on subspace clustering | |
| CN114114197A (en) | Doppler ambiguity calculation method, Doppler velocity calculation method and device | |
| CN115453490B (en) | Coherent accumulation method, device and equipment based on radar signals and storage medium | |
| Cao et al. | Range estimation based on symmetry polynomial aided Chinese remainder theorem for multiple targets in a pulse Doppler radar | |
| CN117110725A (en) | Method and system for evaluating pulse electromagnetic field | |
| Shi | Three GLRT detectors for range distributed target in grouped partially homogeneous radar environment | |
| CN107450048A (en) | A kind of antenna angle-measuring method and system | |
| Ma et al. | Pulse sorting algorithm using TDOA in multiple sensors system | |
| WO2021221734A1 (en) | System and method for calculating a binary cross-correlation | |
| US20240077581A1 (en) | High-resolution antenna array | |
| RU2767598C1 (en) | Device for determining statistical characteristics of ignition delay time of matrix indicator elements | |
| RU2745108C1 (en) | Method for determining the difference in distances to a multiple response-impulse jammer in an active-passive multi-position radar system | |
| EP2144084A2 (en) | Method and processor for reduced ambiguity resolution matrix for interferometric angle determination | |
| US20230003853A1 (en) | Device and method for generating test data for testing a distance determination in an optical time-of-flight measurement | |
| Chenghu et al. | A method based on Chinese remainder theorem with all phase DFT for DOA estimation in sparse array | |
| CN115436908B (en) | Target detection method, device and medium based on radar frequency agile signal | |
| US20240036200A1 (en) | Method and a device for determining a distance to a target | |
| CN116106852B (en) | Method and device for determining airborne main clutter channel and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |