Improved radar demultiple target two-dimensional ambiguity method

Abstract

The application discloses an improved radar two-dimensional ambiguity resolution method for multi-targets, comprising the following steps: arranging point trail data after radar signal processing according to pulse repetition period from small to large; constructing a reference lookup table for resolving range ambiguity based on the arranged data; constructing a reference lookup table for resolving velocity ambiguity based on the arranged data; traversing all the frequency combinations of radar transmission, selecting N groups from M groups of frequency for multi-target pairing; obtaining a range pairing table based on the reference lookup table for resolving range ambiguity, calculating the standard deviation between rows of the range pairing table, and solving the real range of targets; obtaining a velocity pairing table based on the reference lookup table for resolving velocity ambiguity, calculating the standard deviation between rows of the velocity pairing table, and solving the real velocity of targets; and merging and processing output target data. The application can solve the problem that the traditional one-dimensional set method cannot meet the real-time requirement when the number of targets is large and the ambiguity is large.

Description

Technical Field

[0001] This invention relates to the field of radar defuzzification technology, and in particular to an improved radar defuzzification method for two-dimensional fuzziness of multiple targets. Background Technology

[0002] Traditional radar deambiguity methods include the residual lookup table method, the residual theorem method, and the one-dimensional set method.

[0003] The main advantage of the residual difference lookup table method is that when the ranging range is small, it can quickly match the actual distance of the target from the difference in apparent distance by looking up the table. However, as the ranging range of the target increases, the storage space becomes large, the lookup time becomes slow, and it is difficult to meet the requirements of real-time signal processing.

[0004] The Remainder Theorem method (also known as the Chinese Remainder Method) requires that the number of data points in several PRI spaces be pairwise coprime with the number of distance gates; that is, the product of several PRIs and the signal bandwidth must satisfy the pairwise coprime relationship. The Remainder Theorem method yields correct results when there are no ranging errors. However, when errors exist, the solution will be flawed.

[0005] The one-dimensional set method (also known as the least squares method) can simultaneously resolve two-dimensional fuzziness of distance and velocity, and has good fault tolerance and high reliability. However, due to the involvement of sorting and calculating the mean square error, the computational load increases rapidly when the fuzziness of the target is large and the number of targets is large, making it difficult to meet real-time requirements.

[0006] In his paper "An Improved Algorithm for Deambiguity in Radar Signal Detection," Han Hongbo describes an improved algorithm based on a one-dimensional set algorithm and its implementation steps. The paper primarily improves the sorting part of the one-dimensional set algorithm by replacing sorting with a lookup table approach. However, this method does not consider issues such as velocity ambiguity, blind zones, and multiple targets.

[0007] Huang Zhonghua et al., in their paper "Method and Implementation of Detecting Multiple Targets in a Single Beam of Airborne Radar," employed a one-dimensional set method to resolve range ambiguity. For multiple targets, the paper uses a pairwise pairing method; however, this method requires prior knowledge of the number of targets. When the number of targets is unknown, using all repetition frequencies or selecting a few sets of repetition frequencies for ambiguity resolution results in missed detections. Summary of the Invention

[0008] To address the issues of slow computation speed, severe false alarm and missed alarm problems, and overly idealized verification environments in existing defuzzification algorithms when dealing with multiple targets, this invention proposes an improved two-dimensional radar defuzzification method for multiple targets. To address the slow computation speed of one-dimensional set algorithms, a range and velocity table independent of the target is pre-constructed. The corresponding target measurements are then added during subsequent calculations, significantly reducing the computational load for sorting and calculating the mean square error. Considering the missed alarm problem caused by blind speed and blind range, all repetition frequency combinations are traversed, and N groups are selected from M groups for multi-target pairing, where M is the number of transmitted repetition frequency groups and N is the minimum number of repetition frequency groups required for defuzzification. To avoid false alarms caused by interleaved multi-target data, after successful target range matching, the velocity corresponding to the N groups of data is further calculated, and false alarms are eliminated based on velocity matching. To address the issue that the "N / M" criterion might classify a target as multiple similar targets, the solved target range and velocity data are further merged to reduce false alarms caused by target splitting.

[0009] The technical solution adopted in this invention is as follows:

[0010] An improved radar method for resolving two-dimensional ambiguity in multi-target scenarios includes the following steps:

[0011] S1. Arrange the radar signal processed point data in ascending order according to the pulse repetition period PRI;

[0012] S2. Construct a benchmark lookup table TableR based on the rearranged data to resolve distance ambiguity;

[0013] S3. Construct a benchmark lookup table TableV for resolving velocity fuzziness based on the rearranged data;

[0014] S4. Traverse all combinations of repetition frequencies emitted by the radar, and select N groups from M groups of repetition frequencies for multi-target pairing;

[0015] S5. Determine the true distance to the target: Based on the benchmark lookup table TableR for resolving distance ambiguity, obtain the distance pairing table Table1. Calculate the standard deviation between each row of the distance pairing table Table1. If the minimum standard deviation is less than the threshold T... R This indicates that the target distance pairing is successful. The mean of the N data points corresponding to the column with the smallest standard deviation is calculated as the true target distance Ran.

[0016] S6. Determine the true target velocity: After successful target distance pairing, obtain the velocity pairing table Table2 based on the benchmark lookup table TableV for resolving velocity ambiguity. Calculate the standard deviation between each row of the velocity pairing table Table2. If the minimum standard deviation is less than the threshold T... VThis indicates that the target speed has been successfully matched. Calculate the mean of the N data points corresponding to this column as the target's true speed Vel, and output the target data Tar = [Ran, Vel].

[0017] S7. Merge the output target data: Repeat steps S4 to S6 until all repetition frequency combinations of target pairings have been traversed. Then merge the output target true distance and target true velocity to reduce target splitting.

[0018] Further, step S2 includes the following sub-steps:

[0019] S201. Select the first group of pulse repetition cycles PRI as the reference cycle, list all possible distance values ​​corresponding to the reference cycle, and calculate all possible distance values ​​for other non-reference cycles;

[0020] S202. Create a table of all possible range values ​​corresponding to each group of repetition frequencies. The number of rows in the table is the number of repetition frequencies M transmitted by the radar, and the number of columns in the table is the number of all possible ranges corresponding to the reference period m+1. Place the range value R in other repetition frequencies in the column corresponding to the first value ≥ R in the reference repetition frequency.

[0021] S203. Fill the blank items in the table with the previous distance value in this repetition frequency to obtain the reference lookup table TableR for resolving distance ambiguity.

[0022] Furthermore, the possible distance values ​​corresponding to the reference period in step S201 are:

[0023] R 1,p =[R 1,0 ,R 1,1 ,...,R 1,m ]

[0024] Where R 1,i =i·R 1,u i = 0, 1, ..., m The maximum detection range of the radar is R. max The maximum ambiguity number, R 1,u =PRI1·c / 2 is the unambiguous distance corresponding to the reference repetition frequency PRI1.

[0025] Furthermore, step S3 includes the following sub-steps:

[0026] S301. Select the last pulse repetition period PRI as the reference period, and list all possible speeds corresponding to the reference period:

[0027] V M,p =[V 1,-k ,...,V 1,-2 V 1,-1 V1,0 V 1,1 V 1,2 ,...,V 1,k ]

[0028] Where V M,i =i·V M,u i = -k,...,-1,0,1,...,k The maximum detection speed of the radar is V max The maximum ambiguity number, V M,u =1 / PRI M ·λ / 2 is the reference repetition frequency PRI M The corresponding unambiguous velocity; similarly, calculate all possible velocity values ​​for other non-reference periods;

[0029] S302. Create a table of all possible velocity values ​​corresponding to each group of repetition frequencies. The number of rows in the table is the number of repetition frequencies M, and the number of columns in the table is the number of all possible velocities corresponding to the reference period 2k+1. Place the velocity value V in other repetition frequencies in the column corresponding to the first value ≥ V in the reference repetition frequency.

[0030] S303. Fill the blanks in the table with the previous velocity value in this repetition frequency to obtain the reference lookup table TableV for resolving velocity ambiguity.

[0031] Further, step S4 includes the following sub-steps:

[0032] S401. Select N numbers from the array [1,2,…,M], and there are a total of There are several combinations, let the i-th combination be [i1, i2, ..., i...]. N ];

[0033] S402. From the i1, i2, ..., i N Select one target data point sequentially from the group repetition frequency, assuming it starts from the i-th group. q The target measurement distance and measurement speed selected in the group repetition frequency are respectively and Where q = 1, 2, ..., N.

[0034] Further, step S5 includes the following sub-steps:

[0035] S501. Extract the [i1,i2,...,i]th element of the reference lookup table TableR for dissolving distance ambiguity. N For the i1th row, add the target measured distance. For other rows, if Add directly if Then add in For the i-th q The unambiguous distances corresponding to the repetition frequencies are used to obtain the distance pairing table Table1. The table has N rows and m+1 columns. The values ​​in the table are all possible distance values ​​of the target at each repetition frequency.

[0036] S502. Calculate the standard deviation between each row of the distance pairing table Table1. If the minimum standard deviation is less than the threshold T... R This indicates that the target distance pairing was successful. The mean of the N data points corresponding to the column with the smallest standard deviation is calculated as the true distance Ran of the target.

[0037] Further, step S6 includes the following sub-steps:

[0038] S601. Extract the reference lookup table TableV for de-fuzzing speed from the [i1,i2,...,i]th frame. N ] row, for the i-th N Okay, add the target measurement speed. For other rows, if Add directly if Then add in For the i-th q The unambiguous velocities corresponding to the repetition frequencies are used to obtain the velocity pairing table Table2. The table has N rows and 2k+1 columns. The values ​​in the table are all possible velocity values ​​of the target at each repetition frequency.

[0039] S602. Calculate the standard deviation between each row of the speed pairing table Table2. If the smallest standard deviation is less than the threshold T... V Calculate the mean of the N data points corresponding to the column, which is taken as the true velocity Vel of the target, and output the target data Tar = [Ran, Vel]. Otherwise, do not output the target.

[0040] The beneficial effects of this invention are as follows:

[0041] This invention addresses the problem of resolving two-dimensional ambiguity in radar multi-target deambiguity by employing an improved deambiguity method. This method combines the advantages of lookup table methods and one-dimensional set methods, solving the problem that traditional one-dimensional set methods struggle to meet real-time requirements when dealing with a large number of targets and high ambiguity. In the given examples, with 4 targets, the computation speed is 80 times faster than the traditional one-dimensional set method; with 6 targets, the computation speed is 580 times faster. Furthermore, this invention also provides corresponding solutions to the false alarm and missed alarm problems of traditional deambiguity methods. Simulation examples show a significant reduction in both the false alarm and missed alarm rates. Attached Figure Description

[0042] Figure 1A flowchart of an improved radar multi-target two-dimensional ambiguity resolution method according to the present invention. Detailed Implementation

[0043] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0044] Example

[0045] like Figure 1 As shown, this embodiment provides an improved radar multi-target two-dimensional ambiguity resolution method, including the following steps:

[0046] S1. Arrange the radar signal processed point data in ascending order according to the pulse repetition period PRI;

[0047] S2. Construct a benchmark lookup table TableR based on the rearranged data to resolve distance ambiguity;

[0048] S3. Construct a benchmark lookup table TableV for resolving velocity fuzziness based on the rearranged data;

[0049] S4. Traverse all combinations of repetition frequencies emitted by the radar, and select N groups from M groups of repetition frequencies for multi-target pairing;

[0050] S5. Determine the true distance to the target: Based on the benchmark lookup table TableR for resolving distance ambiguity, obtain the distance pairing table Table1. Calculate the standard deviation between each row of the distance pairing table Table1. If the minimum standard deviation is less than the threshold T... R This indicates that the target distance pairing is successful. The mean of the N data points corresponding to the column with the smallest standard deviation is calculated as the true target distance Ran.

[0051] S6. Determine the true target velocity: After successful target distance pairing, obtain the velocity pairing table Table2 based on the benchmark lookup table TableV for resolving velocity ambiguity. Calculate the standard deviation between each row of the velocity pairing table Table2. If the minimum standard deviation is less than the threshold T... V This indicates that the target speed has been successfully matched. Calculate the mean of the N data points corresponding to this column as the target's true speed Vel, and output the target data Tar = [Ran, Vel].

[0052] S7. Merge the output target data: Repeat steps S4 to S6 until all repetition frequency combinations of target pairings have been traversed. Then merge the output target true distance and target true velocity to reduce target splitting.

[0053] The "N / M" unambiguity criterion classifies a single target as multiple similar targets. When there is no measurement error, these multiple targets have the same range and velocity. When there is a measurement error, the error generally does not exceed the size of one radar resolution cell. Therefore, targets with range and velocity differences not exceeding the size of one resolution cell are grouped into one target.

[0054] In this embodiment of the radar multi-target two-dimensional ambiguity resolution method, to address the slow computation speed of the one-dimensional set algorithm, a range table and velocity table independent of the target are pre-constructed. The corresponding target measurements are then added during subsequent calculations, significantly reducing the computational load of sorting and calculating the mean square error. Considering the missed detection problem caused by blind speed and blind range, all repetition frequency combinations are traversed, and N groups are selected from M groups for multi-target pairing, where M is the number of transmitted repetition frequency groups and N is the minimum number of repetition frequency groups required for ambiguity resolution. To avoid false alarms caused by interleaved multi-target data, after successful target range matching, the velocity corresponding to the N groups of data is further calculated, and false alarms are eliminated based on velocity matching. To address the issue that the "N / M" criterion might classify a target as multiple similar targets, the resolved target range and velocity are further merged to reduce false alarms caused by target splitting.

[0055] Preferably, step S2 includes the following sub-steps:

[0056] S201. Select the first group of pulse repetition cycles PRI as the reference cycle, list all possible distance values ​​corresponding to the reference cycle, and calculate all possible distance values ​​for other non-reference cycles;

[0057] S202. Create a table of all possible range values ​​corresponding to each group of repetition frequencies. The number of rows in the table is the number of repetition frequencies M transmitted by the radar, and the number of columns in the table is the number of all possible ranges corresponding to the reference period m+1. Place the range value R in other repetition frequencies in the column corresponding to the first value ≥ R in the reference repetition frequency.

[0058] S203. Fill the blank items in the table with the previous distance value in this repetition frequency to obtain the reference lookup table TableR for resolving distance ambiguity.

[0059] Preferably, the possible distance values ​​corresponding to the reference period in step S201 are:

[0060] R 1,p =[R 1,0 ,R 1,1 ,...,R 1,m ]

[0061] Where R 1,i =i·R 1,u i = 0, 1, ..., m The maximum detection range of the radar is R. max The maximum ambiguity number, R 1,u =PRI1·c / 2 is the unambiguous distance corresponding to the reference repetition frequency PRI1.

[0062] Preferably, step S3 includes the following sub-steps:

[0063] S301. Select the last pulse repetition period PRI as the reference period, and list all possible speeds corresponding to the reference period:

[0064] V M,p =[V 1,-k ,...,V 1,-2 V 1,-1 V 1,0 V 1,1 V 1,2 ,...,V 1,k ]

[0065] Where V M,i =i·V M,u i = -k,...,-1,0,1,...,k The maximum detection speed of the radar is V max The maximum ambiguity number, V M,u =1 / PRI M ·λ / 2 is the reference repetition frequency PRI M The corresponding unambiguous velocity; similarly, calculate all possible velocity values ​​for other non-reference periods;

[0066] S302. Create a table of all possible velocity values ​​corresponding to each group of repetition frequencies. The number of rows in the table is the number of repetition frequencies M, and the number of columns in the table is the number of all possible velocities corresponding to the reference period 2k+1. Place the velocity value V in other repetition frequencies in the column corresponding to the first value ≥ V in the reference repetition frequency.

[0067] S303. Fill the blanks in the table with the previous velocity value in this repetition frequency to obtain the reference lookup table TableV for resolving velocity ambiguity.

[0068] Preferably, step S4 includes the following sub-steps:

[0069] S401. Select N numbers from the array [1,2,…,M], and there are a total of There are several combinations, let the i-th combination be [i1, i2, ..., i...]. N ];

[0070] S402. From the i1, i2, ..., i N Select one target data point sequentially from the group repetition frequency, assuming it starts from the i-th group. q The target measurement distance and measurement speed selected in the group repetition frequency are respectively and Where q = 1, 2, ..., N.

[0071] Preferably, step S5 includes the following sub-steps:

[0072] S501. Extract the [i1,i2,...,i]th element of the reference lookup table TableR for dissolving distance ambiguity. N For the i1th row, add the target measured distance. For other rows, if Add directly if Then add in For the i-th q The unambiguous distances corresponding to the repetition frequencies are used to obtain the distance pairing table Table1. The table has N rows and m+1 columns. The values ​​in the table are all possible distance values ​​of the target at each repetition frequency.

[0073] S502. Calculate the standard deviation between each row of the distance pairing table Table1. If the minimum standard deviation is less than the threshold T... R This indicates that the target distance pairing was successful. The mean of the N data points corresponding to the column with the smallest standard deviation is calculated as the true distance Ran of the target.

[0074] Preferably, step S6 includes the following sub-steps:

[0075] S601. Extract the reference lookup table TableV for de-fuzzing speed from the [i1,i2,...,i]th frame. N ] row, for the i-th N Okay, add the target measurement speed. For other rows, if Add directly if Then add in For the i-th q The unambiguous velocities corresponding to the repetition frequencies are used to obtain the velocity pairing table Table2. The table has N rows and 2k+1 columns. The values ​​in the table are all possible velocity values ​​of the target at each repetition frequency.

[0076] S602. Calculate the standard deviation between each row of the speed pairing table Table2. If the smallest standard deviation is less than the threshold T... VCalculate the mean of the N data points corresponding to the column, which is taken as the true velocity Vel of the target, and output the target data Tar = [Ran, Vel]. Otherwise, do not output the target.

[0077] Verification Example

[0078] To verify the implementation steps and performance of the improved defuzzification method proposed in this invention when defuzzifying two-dimensional fuzziness of multiple targets, a simulation dataset with a certain error was constructed. It is assumed that the six repetition frequency periods of the radar transmission are [23, 25, 27, 29, 31, 34] μs, the minimum number of repetition frequencies required for defuzzification is 3, the radar pulse width is 2.5 μs, the radar bandwidth is 5 MHz, and the number of coherent pulses on each repetition frequency is 256. Therefore, the radar range resolution cell size is 30 m, the velocity resolution cell size is approximately 1.7 m / s, the measurement error is within one resolution cell, the blind range is 375 m, the blind velocity is 3 m / s, the maximum radar detection velocity is 10000 m / s, and the maximum detection range is 1000 km. The number of targets within the same beam is 4. The actual range, actual velocity, detected apparent range, and apparent velocity are shown in Table 1.

[0079] Table 1. Actual and measured distances of the target in the simulation experiment.

[0080]

[0081] Target 1 and Target 2 are at the same distance but have different speeds; Target 3 and Target 4 have the same speed but are at different distances. During radar detection, due to blind spots, some repetition frequencies cannot detect a certain target, which is indicated by "-".

[0082] The specific implementation steps of this verification example are as follows:

[0083] S1. Arrange the radar signal processed point data in ascending order of pulse repetition period (PRI).

[0084] S2. Construct a benchmark lookup table TableR for resolving distance ambiguity.

[0085] S201. Select the first PRI group as the baseline period, and list all possible distance values ​​corresponding to the baseline period:

[0086] R 1,p =[0,3450,6900,...,993600]

[0087] The unambiguous distance corresponding to the reference repetition frequency PRI1 is 3450m. Similarly, all possible distance values ​​for other non-reference periods are calculated.

[0088] S202. Create a table containing all possible range values ​​corresponding to each group of repetition frequencies. The number of rows in the table is 6, which is the number of repetition frequencies transmitted by the radar, and the number of columns in the table is 289, which is the number of all possible ranges corresponding to the reference period. Place the range value R from other repetition frequencies in the column corresponding to the first value ≥ R in the reference repetition frequency.

[0089] S203. Fill the blank items in the table with the previous distance value in this repetition frequency to obtain the reference lookup table TableR for resolving distance ambiguity.

[0090]

[0091] S3. Construct a benchmark lookup table for resolving velocity fuzziness.

[0092] S301. Select the last set of PRI as the reference period, and list all possible speeds corresponding to the reference period as follows:

[0093] V 6,p = [-10435,...,-652,0,652,...,10435]

[0094] The unambiguous velocity corresponding to the reference repetition frequency PRI6 is 652 m / s. Similarly, calculate all possible velocity values ​​for other non-reference periods.

[0095] S302. Create a table containing all possible velocity values ​​corresponding to each group of repetition frequencies. The number of rows in the table is 6 (the number of repetition frequencies), and the number of columns is 47 (the number of all possible velocities corresponding to the reference period). Place the velocity value V from other repetition frequencies in the column corresponding to the first value ≥ V in the reference repetition frequency.

[0096] S303. Fill the blank entries in the table with the previous velocity value in this repetition frequency to obtain the reference lookup table TableV for resolving velocity ambiguity:

[0097]

[0098] S4. Select 3 groups from the 6 repetition frequencies for multi-target pairing.

[0099] S401. Select 3 numbers from the array [1,2,…,6]. There are 20 possible combinations. Assume that the i-th combination is [1,2,4], where i = 1,2,...,20.

[0100] S402. Select one target data point from the repetition frequencies of groups 1, 2, and 4 in sequence. The distance and velocity of the selected data are 2059m, 2496m, 852m and 247.3m / s, 300.7m / s, 123.6m / s, respectively.

[0101] S5. Solve for the true distance to the target corresponding to these 3 sets of data.

[0102] S501. Extract rows 1, 2, and 4 of the distance reference lookup table TableR. For row 1, add the target measured distance of 2059. For row 2, since 2496 is greater than 2059, directly add 2496. For row 4, since 852 is less than 2059, add 2059 + 4350, resulting in the distance pairing table Table1. The table has 3 rows and 289 columns, and the values ​​in the table are all possible distance values ​​of the target at each repetition frequency. S502. Calculate the standard deviation between each row of the distance pairing table Table1. If the minimum standard deviation is less than the threshold of 30m, it indicates that the target distance pairing is successful. Calculate the mean of the three data points corresponding to that column as the true target distance Ran. For example, in this calculation, the minimum standard deviation is 6.5m, which is less than the threshold of 30m, indicating that a true target distance may exist. The target distance is calculated to be 39994m.

[0103] S6. The speed at which these three data points are calculated after the target distance is successfully matched.

[0104] S601. Extract rows 1, 2, and 4 of the velocity reference lookup table TableV. For row 4, add the target measured velocity of 123.6 corresponding to the fourth repetition frequency. For row 1, since the measured velocity 247.3 is greater than 123.6, directly add 247.3. For row 2, since 300.7 is greater than 123.6, directly add 300.7, thus obtaining the velocity pairing table Table2. The table has 3 rows and 27 columns, and the values ​​in the table are all possible velocity values ​​of the target at each repetition frequency.

[0105] S602. Calculate the standard deviation between each row of the velocity pairing table Table2. If the minimum standard deviation is less than the threshold of 1.7 m / s, calculate the mean of the three data points corresponding to that column as the true velocity Vel of the target, and output the target Tar = [Ran, Vel]. Otherwise, do not output the target. For example, in this calculation, the minimum standard deviation is 0.51 m / s, which is less than the threshold of 1.7 m / s, indicating that the true velocity of the target may exist. The target velocity is calculated to be 900.3 m / s, and the target information Tar = [39994 m, 900.3 m / s] is output.

[0106] S7. Repeat steps S4 to S6 until all target pairings have been traversed. Then, merge the output target data to reduce target splitting. Merge all targets with a distance difference of no more than 30m and a speed difference of no more than 1.7m / s into one target.

[0107] To compare performance, the one-dimensional set method (Method 1) in Huang Zhonghua's paper and the improved one-dimensional set method (Method 2) in Han Hongbo's paper are extended to resolve two-dimensional ambiguity of distance and velocity, i.e., the velocity ambiguity is resolved only after successful distance pairing. The target merging method in step S7 of this invention is also applied to these two methods. Verification is performed using the simulation dataset provided in this verification example, selecting the second, third, and sixth repetition frequencies with the largest number of measured targets for ambiguity resolution. The ambiguity resolution results are shown in Table 2.

[0108] Table 2 shows the unfuzziness results from the simulation experiment.

[0109]

[0110]

[0111] Since target 3 was not detected in the second set of repetition frequencies, methods 1 and 2 do not output this target when selecting a fixed repetition frequency for deblurring. However, in the deblurring method described in this invention, all four targets are output. Furthermore, the method described in this invention also has a significant advantage in terms of computational speed.

[0112] More generally, this invention verifies the performance of the method described in this invention when the target distance and velocity are randomly distributed within a certain range. 1000 Monte Carlo tests were conducted, and the relationships between the average time consumed, the average missed detection rate (missed detection rate = number of real targets missed during defuzzification / number of real targets), the average false alarm rate (false alarm rate = number of non-real targets / total number of targets output during defuzzification), and the number of real targets were statistically analyzed, as shown in Table 3.

[0113] Table 3 Results of 1000 Monte Carlo Simulation Tests

[0114]

[0115] As can be seen, the radar multi-target two-dimensional fuzzy resolution method of the present invention has significantly reduced computation time, false alarm rate, and false alarm rate.

[0116] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. An improved method of resolving two-dimensional ambiguities in a radar multi-target solution, characterized by, The method comprises the following steps: S1. Arranging the point data after radar signal processing according to pulse repetition period PRI from small to large; S2. Construct a reference look-up table that resolves ambiguity based on the arranged data ; S3. Construct a reference look-up table for resolving velocity ambiguity based on the arranged data ; S4. Iterate through all the PRF combinations transmitted by the radar, from M selecting N groups to perform multi-target pairing; S5. Solve the target real distance: based on the reference lookup table of solving distance ambiguity Get the distance pairing table Table1, calculate the standard deviation between each row of the distance pairing table Table1, if the smallest standard deviation is less than the threshold , it means that the target distance pairing is successful, calculate the mean of the N data corresponding to the column with the smallest standard deviation as the target real distance ; S6. Solve the target real speed: when the target distance pairing is successful, the reference lookup table based on the solution speed ambiguity Get the speed pairing table Table2, calculate the standard deviation between each row of the speed pairing table Table2, if the smallest standard deviation is less than the threshold , it means that the target speed pairing is successful, calculate the mean of the corresponding N data in this column as the target real speed , and output the target data ; S7. Merging the output target data: repeating steps S4-S6 until all the frequency combinations of the target pairing are traversed, and then merging the output target real distance and target real speed to reduce target splitting; Step S5 comprises the following sub-steps: S501. Extracting the reference lookup table of the ambiguity-free range of the first row, for the first row, adding the target measurement range ; for other rows, if , directly adding ; if , adding + , so as to obtain the range pairing table Table1, the number of rows of the table is , the number of columns is +1, and the values in the table are all possible distance values of the target on each PRF; wherein is the target measurement range selected from the first group of PRFs, is the unambiguous distance corresponding to the first group of PRFs. S502. Calculate the standard deviation between each row of the distance pairing table Table1, if the smallest standard deviation is less than a threshold , it means the target distance pairing is successful, calculate the mean of the data corresponding to the column with the smallest standard deviation as the real distance of the target . ​ 2. The improved radar resolutes two-dimensional ambiguity method of claim 1, wherein, Step S2 comprises the following sub-steps: S201. Selecting the first group of pulse repetition periods PRI as the reference period, listing all possible distance values corresponding to the reference period, and calculating all possible distance values of other non-reference periods; S202. Build a table for all possible distance values corresponding to each group of repetition frequencies, the number of rows of the table is the number of repetition frequencies transmitted by the radar M , the number of columns of the table is the number of all possible distances corresponding to the reference period + 1, put the distance values in other repetition frequencies in the column corresponding to the first value ≥ R in the reference repetition frequency R ; S203. Fill the empty item in the table as the previous distance value in the current Doppler frequency, to obtain the reference look-up table for resolving distance ambiguity .

3. The improved radar resolutes two-dimensional ambiguity method of claim 2, wherein, All possible distance values corresponding to the reference period in step S201 are: wherein , , is the maximum number of ambiguities for a radar maximum detection range of , is the reference pulse repetition frequency corresponding unambiguous range.

4. The improved radar resolutes two-dimensional ambiguity method of claim 3, wherein, Step S3 comprises the following sub-steps: S301. Selecting the last group of pulse repetition periods PRI as the reference period, listing all possible speeds corresponding to the reference period: wherein , , is the maximum number of ambiguities for the radar maximum detection speed , is the reference pulse repetition frequency corresponding unambiguous speed; similarly, all possible speed values for other non-reference periods are calculated; S302. Build a table for all possible speed values corresponding to each group of re-frequencies, the number of rows of the table is the number of re-frequencies , the number of columns of the table is the number of all possible speeds corresponding to the reference period , put the speed values in other re-frequencies in the column corresponding to the first value ≥ in the reference re-frequency S303. Fill the empty item in the table as the previous velocity value in the current repetition frequency, to obtain the reference look-up table of velocity ambiguity .

5. The improved radar resoluiton of two-dimensional ambiguities method according to claim 4, characterized in that, Step S4 comprises the following sub-steps: S401. Select from the array [1, 2, …, M] number, in total combinations, assuming the first combination is ; S402. Select one target data from the first group of heavy frequencies Assume that the target measurement distance and measurement speed selected from the first group of heavy frequencies are and respectively. wherein .

6. The improved radar resolutes two-dimensional ambiguity method of claim 5, wherein, Step S6 comprises the following sub-steps: S601. Extracting the reference lookup table of the speed ambiguity solution of the first row, for the first row, add the target measurement speed ; for other rows, if , directly add , if , add + , where is the unambiguous speed corresponding to the first group of PRFs, thereby obtaining the speed pairing table Table 2, the number of rows of the table is , the number of columns is , and the values in the table are all possible speed values of the target on each PRF; S602. Calculate the standard deviation between each row of the speed pairing table Table2. If the smallest standard deviation is less than the threshold... Calculate the corresponding column The mean of the data points is used as the target's true speed. and output target data. Otherwise, the target will not be output.

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