A method and apparatus for identifying anomalies in de-fuzzing velocity

Abstract

This application discloses a method and apparatus for identifying de-velocity fuzziness anomalies. The embodiments of this application use de-velocity fuzziness confidence levels to filter target points, quickly identifying de-velocity fuzziness anomalies, thereby rapidly and effectively identifying target points where the de-velocity fuzziness multiple is incorrectly resolved. Furthermore, by deleting the identified de-velocity fuzziness anomalies, the generation of false alarms in the de-velocity fuzziness resolution of Chirp Delay is reduced.

Description

Technical Field

[0001] This application relates to, but is not limited to, radar technology, and in particular to a method and apparatus for identifying de-velocity ambiguity anomalies. Background Technology

[0002] Chirp Delay is an algorithm or technique commonly used to solve velocity ambiguity problems, particularly in radar systems. Velocity ambiguity is typically caused by radar signals transmitting signals to a moving target, making the target appear blurry in radar images. Chirp Delay helps overcome this ambiguity, providing more accurate velocity information. The basic idea behind Chirp Delay for velocity ambiguity resolution is to use chirps to transmit radar signals and then process the received signals. A chirp is a signal whose frequency varies over time. By analyzing the frequency changes in the echo signal, the target's velocity can be identified.

[0003] In the field of millimeter-wave radar, chirp delay is a commonly used algorithm for velocity ambiguity resolution. Chirp delay resolution generally involves the radar transmitting two sets of chirs: a normal chirp and a delayed chirp. The phase information from the normal and delayed chirs is then used to calculate the target's velocity ambiguity factor. However, due to the complexity of real-world environments, chirp delay resolution may result in incorrect velocity ambiguity factor calculations, leading to false alarms. Summary of the Invention

[0004] This application provides a method and apparatus for identifying de-velocity fuzziness anomalies, which can quickly identify de-velocity fuzziness anomalies, thereby quickly and effectively identifying target points where the velocity fuzziness multiple is incorrectly resolved.

[0005] This invention provides a method for identifying de-fuzzy anomalies in de-fuzzing speed, comprising: The receiver constructs a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of normal chirp and delayed chirp in the echo signal, and solves for the velocity ambiguity factor corresponding to the minimum cost function. Based on the cost function for different velocity ambiguity factors, the likelihood probability of resolving velocity ambiguity is obtained respectively; The difference between the second minimum and the minimum of the likelihood probability is used as the fuzzy confidence level for solving the velocity problem. The target point corresponding to the fuzzy confidence score of the solution velocity that meets the abnormal conditions is identified as the fuzzy abnormal point of the solution velocity.

[0006] In one exemplary instance, the method further includes: deleting the solution speed fuzzy anomalies.

[0007] In one exemplary instance, prior to constructing the cost function for resolving the velocity fuzz, the method further includes: The receiving end converts the echo signal into a frequency domain signal, wherein the echo signal includes signals from the normal chirp and the delayed chirp, and the normal chirp and the delayed chirp are alternately transmitted within the same frame by the transmitting antenna.

[0008] In one exemplary instance, the cost function for constructing the solution to the velocity fuzziness includes: For each receiving antenna, a first phase difference is obtained between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp. The cost function for resolving velocity ambiguity is constructed based on the first phase difference.

[0009] In one exemplary instance, the cost function is as follows: ; in, Indicates the first in the receiving antenna In the FFT transform result of the receiving antenna, the first of the transmitting antennas The peak signal of the normal chirped sequence corresponding to each transmitted antenna signal; Indicates the first In the FFT transformation result of the n receiving antennas, the nth The peak signal of the sequence of delayed chirps corresponding to each transmitted antenna signal; To the speed ambiguity factor The relevant first phase difference; The number of the transmitting antennas; The number of receiving antennas.

[0010] In one exemplary instance, the velocity fuzziness factor corresponding to the minimum cost function is... for: ; in, This represents the operation of minimizing the cost function, finding the value of q that minimizes the cost function. .

[0011] In one exemplary instance, the first phase difference As shown in the following formula: ; in, Indicates unambiguous speed The corresponding Doppler frequency, This indicates the speed ambiguity factor. This represents the period of the normal chirp and the delayed chirp. This indicates the time delay between the normal chirp and the delayed chirp.

[0012] In one exemplary instance, the velocity fuzziness factor corresponding to the minimum cost function is... for: ; The second phase difference is between the normal chirp and the delayed chirp.

[0013] In one exemplary instance, obtaining the likelihood probability of resolving velocity ambiguity based on the cost function for different velocity ambiguity factors includes: Take the logarithm of the cost function for each of the different speed ambiguity factors to obtain the likelihood probability of the corresponding different solution speed ambiguities.

[0014] In one exemplary instance, the abnormal condition includes: the fuzzy confidence level of the solution speed is less than a preset fuzzy confidence level threshold for the solution speed.

[0015] In one exemplary instance, the target point includes a blurring factor according to the speed. The first preset number of target points are sorted by absolute value from largest to smallest.

[0016] This application also provides a computer-readable storage medium storing computer-executable instructions for performing the method for identifying speed fuzzy anomalies described in any of the above embodiments.

[0017] This application embodiment further provides an apparatus for identifying de-speed fuzzy anomalies, including a memory and a processor, wherein the memory stores the following instructions executable by the processor: steps for performing the method for identifying de-speed fuzzy anomalies described in any of the above claims.

[0018] This application embodiment further provides an apparatus for identifying de-fuzzy anomalies in de-fuzzing speed, comprising: a cost function processing module, a second processing module, a confidence acquisition module, and a third processing module; wherein, The cost function processing module is used to construct a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of normal chirp and delayed chirp in the echo signal, and to solve for the velocity ambiguity factor corresponding to the minimum cost function. The second processing module is used to obtain the likelihood probability of de-velocity ambiguity based on the cost function of different velocity ambiguity multiples. The confidence acquisition module is used to take the difference between the second minimum and the minimum of the likelihood probability as the fuzzy confidence of the solution speed. The third processing module is used to identify the target points corresponding to the fuzzy confidence scores of the solution velocity that meet the abnormal conditions as fuzzy abnormal points of the solution velocity.

[0019] In one exemplary instance, the third processing module is further configured to: delete the solution speed fuzzy anomaly points.

[0020] In one exemplary instance, it also includes: A first processing module is configured to convert the echo signal into a frequency domain signal, wherein the echo signal includes signals from the normal chirp and the delayed chirp, and the normal chirp and the delayed chirp are alternately transmitted within the same frame by a transmitting antenna.

[0021] In one exemplary instance, the cost function processing module is configured to: For each receiving antenna, the first phase difference between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp is obtained; the cost function for resolving velocity ambiguity is constructed based on the first phase difference.

[0022] This application embodiment filters target points by using the de-velocity fuzziness confidence score, quickly identifying de-velocity fuzziness anomalies, thereby rapidly and effectively identifying target points with incorrect de-velocity fuzziness multiples. Furthermore, this application embodiment reduces false alarms in Chirp Delay de-velocity fuzziness by deleting identified de-velocity fuzziness anomalies.

[0023] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0024] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0025] Figure 1 This is a flowchart illustrating the method for identifying de-velocity fuzzy anomalies in an embodiment of this application. Figure 2 This is a schematic diagram of the transmitted waveform of the 4T4R millimeter-wave radar system based on DDM mode in the embodiments of this application; Figure 3 For each receiving antenna in the embodiments of this application, in the 2D-FFT results A schematic diagram of a peak signal; Figure 4 This is an example schematic diagram illustrating the fuzzy likelihood probability of the Chirp Delay solution speed in an embodiment of this application; Figure 5(a) is an example schematic diagram of the fuzzy confidence of the solution velocity of a normal target point in an embodiment of this application; Figure 5(b) is an example schematic diagram of the fuzzy confidence of the solution velocity of abnormal target points in an embodiment of this application; Figure 6(a) is a schematic diagram of a radar point cloud example before deleting the target point with abnormal speed in an embodiment of this application; Figure 6(b) is a schematic diagram of a radar point cloud example after deleting target points with abnormal speeds in an embodiment of this application; Figure 7 This is a different embodiment of the present application. A schematic diagram of the phase circle diagram corresponding to the simplified phase difference of the value; Figure 8 In this embodiment of the application, only the speed ambiguity factor is determined. A schematic diagram illustrating an example of whether a target with a high absolute value is a velocity anomaly. Figure 9 This is a schematic diagram of the structure of the device for identifying de-fuzzy anomalies in the embodiments of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be arbitrarily combined with each other.

[0027] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0029] It is understood that the terms "first" and "second" used in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0030] It is understood that the term "connection" in the following embodiments should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have electrical signal or data transmission with each other.

[0031] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0032] Figure 1 This is a flowchart illustrating the method for identifying de-velocity fuzzy anomalies in an embodiment of this application, as shown below. Figure 1 As shown, it may include: Step 102: The receiver constructs a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of the normal chirp and the delayed chirp in the echo signal, and solves for the velocity ambiguity factor corresponding to the minimum cost function.

[0033] In one exemplary instance, step 102 may also include: Step 101: The receiver converts the echo signal into a frequency domain signal. The echo signal includes signals from both normal and delayed chirps, which are alternately transmitted by the transmitting antenna within the same frame. In an exemplary instance, using a four-transmit, four-receive (4T4R) millimeter-wave radar system based on Doppler Division Multiplexing (DDM) mode, the transmitted waveform is as follows: Figure 2 As shown, in this embodiment, the phase shift step of TX0 is 0°, the phase shift step of TX1 is 45°, the phase shift step of TX2 is 180°, and the phase shift step of TX3 is 270°. The radar alternately transmits Normal Chirp and Delayed Chirp within the same frame, and the period of both types of Chirp is... There is a time delay between Normal Chirp and Delayed Chirp. The chirp cycle of the combination of Normal Chirp and Delayed Chirp is... .

[0034] In one exemplary instance, a time-domain echo signal can be converted into a frequency-domain signal using a Fast Fourier Transform (FFT), such as a two-dimensional Fast Fourier Transform (2D-FFT).

[0035] Still with Figure 2 For example, due to the motion of the target, there exists a velocity ambiguity factor between the peak values ​​of the frequency domain signals of the Normal Chirp and Delayed Chirp, such as the 2D-FFT results. The first phase difference is relevant, and the velocity fuzzy factor can be solved using the first phase difference.

[0036] In one embodiment, it is assumed that the following is adopted: and These represent Normal Chirp sequences and Delayed Chirp sequences, respectively. This represents the index of the sampling point within the Chirp. This represents the index of a chirp within the chirp sequence. and Perform 2D-FFT processing respectively to obtain and ,in, and These represent the positions of a point in the 2D-FFT output.

[0037] Assumption This indicates the position of the target point in the 2D-FFT output. and These represent the 2D-FFT peak signals of the Normal Chirp sequence and the Delayed Chirp sequence at this position, respectively. and There exists a speed ambiguity factor. The relevant first phase difference It can be expressed as shown in formula (1): (1) In formula (1), Indicates unambiguous speed The corresponding Doppler frequency, Indicates the speed blur factor. This indicates the period of the two chirps. This indicates the time delay between the Normal Chirp and the Delayed Chirp.

[0038] In one exemplary instance, step 102 may include: For each receiving antenna, the first phase difference between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp is obtained. Construct a cost function for resolving velocity ambiguity based on the obtained first phase difference, and solve for the velocity ambiguity factor corresponding to the minimum cost function.

[0039] Still with Figure 2 For example, in one embodiment, assume the number of transmitting antennas is... , Figure 2 In the example shown The number of receiving antennas is , Figure 2 In the example shown .like Figure 3 As shown, in the 2D-FFT results of each receiving antenna, there exists A peak signal, Figure 3 The left side represents the FFT transform result of each receiving antenna in Normal Chirp. As shown in the 2D-FFT result, there exists... One (e.g.) Figure 3 (4 peak signals) Figure 3 The right side represents the FFT transform result of each receiving antenna in DelayedChirp, as shown in the 2D-FFT result. One (e.g.) Figure 3 (4 peak signals) In this embodiment, for each receiving antenna, there are 4 peak signals, including those related to the velocity ambiguity factor. The relevant first phase difference (like Figure 3 In The cost function for resolving velocity ambiguity constructed based on the obtained first phase difference in step 102 is shown in equation (2): (2) In formula (2), Indicates the first The FFT transform result of the receiving antenna is shown in the 2D-FFT result, where the first... The peak signal corresponding to each transmitting antenna signal.

[0040] The result of solving for the velocity fuzziness factor is the velocity fuzziness factor corresponding to the minimum cost function. It can be expressed as shown in formula (3): (3) In formula (3), This represents the operation of minimizing the cost function, finding the value of q that minimizes the cost function. .

[0041] Step 103: Obtain the likelihood probability of solving the velocity ambiguity based on the cost function for different velocity ambiguity factors.

[0042] In one exemplary instance, step 103 may include: taking the logarithm of the cost function for different speed ambiguity factors to obtain the likelihood probability of the corresponding different solution speed ambiguities.

[0043] Still with Figure 2 For example, taking the logarithm of the cost function for different speed ambiguity factors yields the likelihood probability of resolving speed ambiguity in Chirp Delay, such as... Figure 4 As shown.

[0044] Step 104: Use the difference between the second minimum and the minimum of the likelihood probability as the fuzzy confidence level for solving the velocity problem.

[0045] Still with Figure 2 For example, using This represents the minimum value of the speed ambiguity factor. This represents the fuzzy multiple of the velocity that can be solved. The quantity is: ,in, Represents the greatest common divisor. .

[0046] The minimum likelihood probability is expressed as The second minimum value is represented as Define the fuzzy confidence level for Chirp Delay solution speed. Let be the difference between the second minimum and the minimum of the likelihood probability, as shown in formula (4): (4) Step 105: Determine the target point corresponding to the solution velocity fuzzy confidence that meets the abnormal conditions as the solution velocity fuzzy abnormal point.

[0047] In one exemplary instance, due to the complexity of the actual environment, the deveining of velocity ambiguity in Chirp Delay has a high error probability, resulting in numerous velocity anomalies in the radar output point cloud. Typically, the confidence level of the deveining velocity ambiguity for these anomalies is much lower than that for normal points. Figure 5(a) shows an example of the confidence level of the deveining velocity ambiguity for a normal target point, and Figure 5(b) shows an example of the confidence level for the deveining velocity ambiguity for an anomaly target point. Thus, this confidence level can effectively identify velocity anomalies, i.e., deveining velocity ambiguity anomalies. In one embodiment, the anomaly condition can be that the confidence level of the deveining velocity ambiguity is less than a pre-set confidence level threshold.

[0048] After identifying the target point with abnormal speed, it can be deleted. The comparison results of the radar point cloud before and after deletion are shown in Figure 6(a) and Figure 6(b).

[0049] The method for resolving velocity ambiguity in Chirp Delay provided in this application uses the confidence level of velocity ambiguity resolution to filter target points, quickly identify abnormal points in velocity ambiguity resolution, and thus quickly and effectively identify target points with incorrect velocity ambiguity resolution, reducing the generation of false alarms.

[0050] In one exemplary instance, assuming all channels are perfectly identical, and the amplitudes of the Normal Chirp and Delayed Chirp are perfectly identical, the second phase difference between the Normal Chirp and Delayed Chirp is... The velocity fuzziness factor corresponding to the minimum cost function can also be expressed as shown in formula (5): (5) different The first phase difference corresponding to the value You can only consider with One of the relevant items, namely Thus, simplifying to its simplest form, we obtain formula (6): (6) In formula (6), The simplified molecule, The simplified denominator, which is also the range of the maximum solution velocity ambiguity factor, is assumed to be in the phase circle diagram. The interval of the values ​​is ,but satisfy Therefore, in one embodiment, when the amplitude-phase consistency of each channel is good, and the amplitude consistency of the Normal Chirp and Delayed Chirp is also good, in order to reduce the identification time of velocity anomalies, it is only necessary to compare... Cost functions in three cases.

[0051] For example, suppose the waveform parameters for Normal Chirp and Delayed Chirp are configured as follows: , , As shown in formula (6), different The first phase difference corresponding to the value, simplified to its simplest form, is: The corresponding phase circle diagram is adjacent The interval of the values ​​is ,like Figure 7 As shown, in this embodiment, only comparison is needed. The cost function can be obtained for all three cases.

[0052] In practical applications, we are more concerned with targets with higher speeds in the distance-velocity point cloud, i.e., the speed blur factor. For targets with large absolute values, in order to further reduce the identification time of velocity anomalies, it is also possible to only determine the velocity ambiguity factor. Are targets with high absolute values ​​velocity anomalies, such as...? Figure 8 As shown, this means that the target point only includes points simplified by a speed blur factor. The target points are sorted by absolute value from largest to smallest, up to a predetermined number. In one embodiment, the value of the predetermined number is related to the actual application scenario. For example, for application scenarios that require short processing time, the value of the predetermined number can be set smaller, while for application scenarios that do not have special requirements on processing time, the value of the predetermined number can be set larger.

[0053] In one exemplary instance, the method may further include deleting identified de-speed fuzziness anomalies. This embodiment of the application reduces the generation of false alarms in the de-speed fuzziness implementation of Chirp Delay by deleting identified de-speed fuzziness anomalies.

[0054] This application describes the DDM mode as an example only, but it is not intended to limit the scope of protection of this application. The method for identifying de-velocity ambiguity anomalies provided in this application is not only applicable to the DDM mode, but also applicable to the Chirp Delay de-velocity ambiguity algorithm under various multiple-input multiple-output (MIMO) systems such as Time Division Multiplexing (TDM).

[0055] This application also provides a computer-readable storage medium storing computer-executable instructions for performing the method for identifying speed-de-fuzzy anomalies described in any of the preceding claims.

[0056] This application further provides an apparatus for identifying de-speed fuzzy anomalies, including a memory and a processor, wherein the memory stores the following instructions executable by the processor: steps for performing the method for identifying de-speed fuzzy anomalies described in any of the preceding claims.

[0057] Figure 9 This is a schematic diagram of the structure of the device for identifying de-fuzzing ambiguity anomalies in an embodiment of this application, as shown below. Figure 9 As shown, it includes: a cost function processing module, a second processing module, a confidence acquisition module, and a third processing module; wherein, The cost function processing module is used to construct a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of normal chirp and delayed chirp in the echo signal, and to solve for the velocity ambiguity factor corresponding to the minimum cost function. The second processing module is used to obtain the likelihood probability of de-velocity ambiguity based on the cost function of different velocity ambiguity multiples. The confidence acquisition module is used to take the difference between the second minimum and the minimum of the likelihood probability as the fuzzy confidence of the solution speed. The third processing module is used to identify the target points corresponding to the fuzzy confidence scores of the solution velocity that meet the abnormal conditions as fuzzy abnormal points of the solution velocity and delete them.

[0058] The apparatus for identifying de-velocity fuzzy anomalies provided in this application identifies de-velocity fuzzy anomalies by filtering target points through de-velocity fuzzy confidence, thereby quickly and effectively identifying target points with incorrect de-velocity fuzzy multiples.

[0059] In one exemplary instance, the third processing module is further configured to: delete identified unseen points in the solution speed fuzziness. This embodiment of the application reduces the generation of false alarms in the implementation of Chirp Delay solution speed fuzziness by deleting identified unseen points in the solution speed fuzziness.

[0060] In one exemplary instance, it may also include: The first processing module is used to convert the echo signal into a frequency domain signal, wherein the echo signal includes signals from normal chirp and delayed chirp, which are alternately transmitted by the transmitting antenna within the same frame.

[0061] In one exemplary instance, the cost function processing module can be used to: For each receiving antenna, obtain the first phase difference between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp: construct the cost function for resolving velocity ambiguity based on the obtained first phase difference.

[0062] Although the embodiments disclosed in this application are as described above, the content described is merely for the purpose of understanding this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for identifying de-velocity fuzzy anomalies, characterized in that, include: The receiver constructs a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of normal chirp and delayed chirp in the echo signal, and solves for the velocity ambiguity factor corresponding to the minimum cost function. Based on the cost function for different velocity ambiguity factors, the likelihood probability of resolving velocity ambiguity is obtained respectively; The difference between the second minimum and the minimum of the likelihood probability is used as the fuzzy confidence level for solving the velocity problem. The target point corresponding to the fuzzy confidence score of the solution velocity that meets the abnormal conditions is identified as the fuzzy abnormal point of the solution velocity.

2. The method according to claim 1, further comprising: Delete the fuzzy anomalies in the solution velocity.

3. The method according to claim 1 or 2, further comprising, before constructing the cost function for resolving the velocity fuzziness: The receiving end converts the echo signal into a frequency domain signal, wherein the echo signal includes signals from the normal chirp and the delayed chirp, and the normal chirp and the delayed chirp are alternately transmitted within the same frame by the transmitting antenna.

4. The method according to claim 3, wherein, The cost function for constructing the velocity ambiguity solution includes: For each receiving antenna, a first phase difference is obtained between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp. The cost function for resolving velocity ambiguity is constructed based on the first phase difference.

5. The method according to claim 4, wherein, The cost function is as follows: ； in, Indicates the first in the receiving antenna In the FFT transform result of the receiving antenna, the first of the transmitting antennas The peak signal of the normal chirped sequence corresponding to each transmitted antenna signal; Indicates the first In the FFT transform result of the receiving antenna, the first... The peak signal of the sequence of delayed chirps corresponding to each transmitted antenna signal; To the speed ambiguity factor The relevant first phase difference; The number of the transmitting antennas; The number of receiving antennas.

6. The method according to claim 5, wherein, The velocity fuzziness factor corresponding to the minimum cost function for: ； in, This represents the operation of minimizing the cost function, finding the value of q that minimizes the cost function. .

7. The method according to claim 6, wherein, First phase difference As shown in the following formula: ； in, Indicates unambiguous speed The corresponding Doppler frequency, This indicates the speed ambiguity factor. This represents the period of the normal chirp and the delayed chirp. This indicates the time delay between the normal chirp and the delayed chirp; The chirp period represents the combination of the normal chirp and the delayed chirp. .

8. The method according to claim 5, wherein, The velocity fuzziness factor corresponding to the minimum cost function for: ; The second phase difference is between the normal chirp and the delayed chirp.

9. The method according to claim 1 or 2, wherein, The step of obtaining the likelihood probability of resolving velocity ambiguity based on the cost function for different velocity ambiguity factors includes: Take the logarithm of the cost function for each of the different speed ambiguity factors to obtain the likelihood probability for each of the different speed ambiguities.

10. The method according to claim 1 or 2, wherein, The abnormal conditions include: the fuzzy confidence level of the solution speed is less than a preset fuzzy confidence level threshold for the solution speed.

11. The method according to claim 1 or 2, wherein, The target point includes points blurred according to the speed factor. The first preset number of target points are sorted by absolute value from largest to smallest.

12. A computer-readable storage medium storing computer-executable instructions for performing the method for identifying de-fuzzy anomalies according to any one of claims 1-11.

13. A device for identifying de-fuzzy anomalies, comprising a memory and a processor, wherein, The memory stores the following instructions executable by a processor: steps for performing the method for identifying unambiguous points in the de-accuracy resolution process as described in any one of claims 1-11.

14. A device for identifying anomalies in defuzzification speed, characterized in that, include: The system comprises a cost function processing module, a second processing module, a confidence acquisition module, and a third processing module; among which, The cost function processing module is used to construct a cost function for resolving velocity ambiguity based on the peak values ​​of the frequency domain signals of normal chirp and delayed chirp in the echo signal, and to solve for the velocity ambiguity factor corresponding to the minimum cost function. The second processing module is used to obtain the likelihood probability of de-velocity ambiguity based on the cost function of different velocity ambiguity multiples. The confidence acquisition module is used to take the difference between the second minimum and the minimum of the likelihood probability as the fuzzy confidence of the solution speed. The third processing module is used to identify the target points corresponding to the fuzzy confidence scores of the solution velocity that meet the abnormal conditions as fuzzy abnormal points of the solution velocity.

15. The apparatus according to claim 14, wherein the third processing module is further configured to: delete the de-fuzzy abnormal points in the de-fuzzing speed.

16. The apparatus according to claim 14 or 15, further comprising: A first processing module is configured to convert the echo signal into a frequency domain signal, wherein the echo signal includes signals from the normal chirp and the delayed chirp, and the normal chirp and the delayed chirp are alternately transmitted within the same frame by a transmitting antenna.

17. The apparatus according to claim 16, wherein, The cost function processing module is used for: For each receiving antenna, a first phase difference is obtained between the peak values ​​of the frequency domain signal corresponding to the normal chirp and the frequency domain signal corresponding to the delayed chirp. The cost function for resolving the velocity ambiguity is constructed based on the first phase difference.

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