Millimeter wave radar false alarm filtering method and device, computer device and storage medium

Abstract

The application relates to the technical field of signal processing, and discloses a millimeter wave radar false alarm filtering method and device, computer equipment and a storage medium, the method comprising the following steps: acquiring a current signal data set corresponding to a current frame; processing the current signal data set to determine a current point cloud data set based on a time sequence; performing point trail matching on all target points in the current point cloud data set and a to-be-updated track set to obtain a new to-be-updated track set; calculating the associated point cloud density and track continuity rate of each track in the new to-be-updated track set; filtering out invalid tracks in the new to-be-updated track set according to the associated point cloud density and the track continuity rate to obtain a target track set; acquiring a signal data set corresponding to a next frame, and taking the signal data set as the current signal data set to repeat the above steps. The method can greatly reduce the associated multi-path false alarm probability, improve the accuracy of false target track identification, and does not affect the alarm rate.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and specifically to a method, apparatus, computer equipment, and storage medium for filtering false alarms in millimeter-wave radar. Background Technology

[0002] Millimeter-wave radar is a detection radar that operates in the millimeter-wave band. It is used to emit electromagnetic waves and receive the reflected electromagnetic waves to obtain parameter information of obstacles. The installation environment of millimeter-wave radar is usually very complex. The cluttered external environment brings serious false alarm problems to the perception of millimeter-wave radar. In particular, metal fences and barriers in the radar coverage area are prone to multipath effects due to their strong reflection of the electromagnetic waves emitted by the radar, which can easily cause false targets and trigger false alarms.

[0003] Current methods for handling multipath false alarms suffer from a significant drop in false alarm discrimination accuracy and low accuracy in identifying false target tracks due to the unstable relative positional relationship between the original target and the multipath false target trajectories. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, computer equipment and storage medium for filtering false alarms from millimeter-wave radar, in order to solve the problem of low accuracy in identifying false target tracks in current methods for processing multipath false alarms.

[0005] In a first aspect, the present invention provides a method for filtering false alarms from millimeter-wave radar, the method comprising: Step 101: Obtain the current signal dataset corresponding to the current frame. The signal dataset includes the continuously transmitted signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmitted signals. Step 102: Process the current signal dataset to determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, as well as the target point information parameters corresponding to the target points. Step 103: Match all target points in the current point cloud dataset with the track set to be updated to obtain a new track set to be updated; the track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point; Step 104: Calculate the associated point cloud density and track continuity rate for each track in the new set of tracks to be updated; Step 105: Based on the associated point cloud density and track continuity rate, filter out invalid tracks in the new track set to be updated to obtain the target track set; Step 106: Obtain the signal dataset corresponding to the next frame and use this signal dataset as the current signal dataset. Repeat steps 102 to 105 until the millimeter-wave radar stops transmitting signals.

[0006] The millimeter-wave radar false alarm filtering method provided in this embodiment processes the data obtained by the millimeter-wave radar to ensure that only the target points of real targets can form valid tracks and output, while the target points of false targets cannot form valid tracks and are thus filtered out. No additional prior information is required, and there are no additional requirements for radar installation and construction. Even in complex multipath reflection environments, it still exhibits good performance, significantly reducing the probability of associated multipath false alarms, improving the accuracy of false target track identification, and without affecting the alarm rate.

[0007] In one alternative implementation, all target points in the current point cloud dataset are matched with the track set to be updated, including: Obtain the target point information parameters corresponding to each target point in the current point cloud dataset; Determine the end track point of each track in the track set to be updated, as well as the end track point information parameters corresponding to the end track point. The end track point is the last track point of the current track. Based on the target point information parameters and the terminal track point information parameters, calculate the first distance value between each target point and each terminal track point, wherein a terminal track point traverses all target points; Determine the minimum distance value among all the first distance values ​​corresponding to the terminal track point, and determine whether the minimum distance value is less than or equal to the first preset value; If the minimum distance value is less than or equal to the first preset value, the target point corresponding to the minimum distance value is determined to be successfully matched with the corresponding track.

[0008] In this embodiment, point cloud clustering is used to aggregate multiple target points in the current point cloud dataset into point track data. For each target point in the point cloud, if it fails to match an existing track, a new track is created and a new track identifier is assigned, marking the track as a pre-established state. By updating the track set, the number of tracks and the status of tracks can be dynamically adjusted, thereby improving the accuracy and robustness of target tracking.

[0009] In one optional implementation, if the minimum distance between the target point and all tracks in the track set to be updated is greater than a first preset value, it is determined that the target point fails to match the tracks in the track set to be updated. To match the failed target point, a new track is re-established, and a pre-established track is obtained. The new set of tracks to be updated includes the pre-established track.

[0010] In one alternative implementation, the associated point cloud density of each track in the new set of tracks to be updated is calculated, including: Determine the first end track point of the first track, where the first track is any track in the new set of tracks to be updated; Calculate the second distance value between each target point in the current point cloud dataset and the first end point of the trajectory; Determine the number of target points in the current point cloud dataset that satisfy the second distance value being less than or equal to the second preset value; Calculate the associated point cloud density of the first track based on the number of target points.

[0011] In one alternative implementation, the track continuity rate is calculated through the following steps: Determine the total number of frames since the establishment of the first track. The first track is any track in the new set of tracks to be updated. One frame corresponds to one point cloud dataset. Determine the number of matching frames that successfully match the first track out of the total number of frames; The track continuity rate of the first track is calculated based on the total number of frames and the number of matched frames.

[0012] In this embodiment, the point cloud density and track continuity rate on the track are used as one of the key factors for judging valid and invalid tracks. This can effectively filter out invalid tracks and improve the accuracy of false target track identification.

[0013] In one alternative implementation, before matching all target points in the current point cloud dataset with the track set to be updated, the method further includes: Predict the next predicted track point for the track in the track set to be updated; The predicted waypoints are used as the terminal waypoints.

[0014] In this embodiment, the Kalman filter algorithm is used to process the point position data of each track using the optimal estimation method. That is, the estimated value is used to update the track position and predict the maximum possible position of the target in the next frame, thereby reducing the influence of measurement noise and obtaining more accurate track position information.

[0015] In one optional implementation, the millimeter-wave radar is a multi-channel radar, and the target point information parameters include target point velocity, target point distance, and target point angle. The target point information parameters are determined through the following steps: The transmitted signal is mixed with the corresponding echo signal to obtain a single-frequency intermediate frequency signal. The intermediate frequency signal is sampled, and frequency information is extracted; Distance information is determined based on frequency information; Determine the phase difference between two adjacent cycles in the intermediate frequency signal, and determine the velocity information based on the phase difference; Based on distance and velocity information, determine multiple distance-velocity matrices corresponding to multiple channels; The amplitudes of multiple distance-velocity matrices are summed to obtain a two-dimensional amplitude matrix; CFAR processing is performed on the two-dimensional amplitude matrix to determine the horizontal and vertical coordinate indices in the two-dimensional amplitude matrix, thereby obtaining the target point distance and the initial target point velocity corresponding to the target point; The initial target point velocity is defuzzified to obtain the true target point velocity; Doppler phase compensation is performed on multiple channels of the multi-channel radar, and DOA estimation is performed on the compensated target point to obtain the target point angle.

[0016] Secondly, the present invention provides a millimeter-wave radar false alarm filtering device, the device comprising: The acquisition module is used to acquire the current signal dataset corresponding to the current frame. The signal dataset includes the continuously transmitted signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmitted signals. The processing module is used to process the current signal dataset and determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, as well as the target point information parameters corresponding to the target points. The matching module is used to match all target points in the current point cloud dataset with the track set to be updated to obtain a new track set to be updated. The track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point. The calculation module is used to calculate the associated point cloud density and track continuity rate of each track in the new set of tracks to be updated; The filtering module is used to filter out invalid tracks in the new track set to be updated based on the associated point cloud density and track continuity rate, so as to obtain the target track set. The loop module is used to obtain the signal dataset corresponding to the next frame and use this signal dataset as the current signal dataset until the millimeter-wave radar stops transmitting signals.

[0017] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the millimeter-wave radar false alarm filtering method described in the first aspect or any corresponding embodiment thereof.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the millimeter-wave radar false alarm filtering method described in the first aspect or any corresponding embodiment thereof.

[0019] It should be noted that the millimeter-wave radar false alarm filtering device, electronic device, and computer-readable storage medium provided by this invention correspond to the millimeter-wave radar false alarm filtering method described above. Therefore, for the beneficial effects of the millimeter-wave radar false alarm filtering device, computer device, and computer-readable storage medium, please refer to the description of the corresponding beneficial effects of the millimeter-wave radar false alarm filtering method above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a millimeter-wave radar false alarm filtering method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a flight path containing false flight paths according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the flight path after filtering out false flight paths according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the method included in step S103 according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a millimeter-wave radar false alarm filtering device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Currently used methods for handling multipath false alarms include: 1. Set up a filtering zone to exclude radar-detected targets in areas prone to false alarms. The drawback of this method is that since multipath false alarms are accompanied by false intrusion targets, the specific location of the false target's trajectory cannot be predicted due to the multipath reflection echo of the moving target. Therefore, a precise filtering zone cannot be set in advance. If the filtering zone is set too large, it is easy to miss the alarm when the real target intrudes.

[0024] 2. Utilizing prior information to assist in multipath false alarm judgment; the drawback of this method is that it lacks universality and is only applicable to radars for specific purposes in specific scenarios.

[0025] 3. The relative positional relationship between the original target trajectory and the accompanying multipath false target trajectory is used to determine whether it is a multipath false alarm. The disadvantage of this method is that the scene adaptability is limited. If the environment is very complex, such as when there are metal barriers reflecting off both sides of the radar field of view, the relative positional relationship between the original target and the multipath false target trajectory is unstable, which makes the accuracy of false alarm identification drop significantly.

[0026] 4. Use multi-sensor fusion technology, such as millimeter-wave radar and vision fusion, to make secondary judgments on false alarms using heterogeneous sensors; the drawback of this method is its cost-effectiveness. In addition, due to the unique shortcomings of each type of sensor, such as the reduced detection capability of vision sensors in environments such as rain, fog, and strong light, this technical solution may not achieve the expected results in some scenarios.

[0027] The above methods all have many shortcomings. In view of this, according to the embodiments of the present invention, a method embodiment for filtering false alarms of millimeter-wave radar is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0028] This embodiment provides a method for filtering false alarms from millimeter-wave radar, which can be executed by computer devices such as servers and terminals, including desktop computers and tablets. Figure 1 This is a flowchart of a millimeter-wave radar false alarm filtering method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain the current signal dataset corresponding to the current frame. The signal dataset includes the continuous transmission signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmission signals.

[0029] In radar systems, a frame refers to a collection of consecutive echo signals. That is, a frame typically contains multiple pulses, each pulse corresponding to a periodic transmission by the radar system. The process of a millimeter-wave radar transmitting and receiving a set of consecutive pulse signals can be understood as one frame. The current frame is the most recently transmitted and received set of consecutive pulse signals by the millimeter-wave radar.

[0030] Millimeter-wave radar can periodically transmit linear frequency modulated continuous wave chirp signals, where a single period of signal transmission... for:

[0031] in, For time The function, The amplitude of the transmitted signal, The starting frequency is [frequency value], and the bandwidth of the transmitted chirp signal sweep is [bandwidth value]. The launch width is , For frequency modulation slope, This is the phase value.

[0032] Assume the distance between the target and the radar is... The relative radial velocity is The speed of electromagnetic waves in air is The time delay between the received echo signal and the transmitted signal is... The target echo signal is:

[0033] in, This is the signal attenuation coefficient.

[0034] Step S102: Process the current signal dataset to determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, as well as the target point information parameters corresponding to the target points.

[0035] The received echo signal and transmit signals After mixing and passing through a low-pass filter, a single frequency of [frequency value] can be obtained. Intermediate frequency sine wave signal:

[0036] The intermediate frequency (IF) signal is sampled by an ADC, and then a Fast Fourier Transform (FFT) is performed to extract the frequency information of the signal, identify the IF frequency peak and frequency point, and determine the distance to the target. It can be calculated using the following formula:

[0037] in, Sampling frequency, This represents the frequency index corresponding to the FFT spectral peak. The number of FFT points.

[0038] Furthermore, a small distance change between two adjacent cycles will cause a change in the initial phase of the intermediate frequency signal. According to the characteristics of Fourier transform, the initial phase of the signal is reflected in the complex phase corresponding to the peak value. Calculating the phase difference between adjacent cycles yields the target's velocity. For multiple pulses, the phase change is periodic; the peak value after performing an FFT on the velocity dimension is the phase difference. In other words, by obtaining the peak value and frequency corresponding to the velocity, and then solving the problem, the target's velocity can be obtained.

[0039] in, The wavelength of electromagnetic waves. For the initial phase change, The chirp signal is repeated periodically.

[0040] The millimeter-wave radar used in this embodiment can be a TDM-MIMO radar with a channel number of The TDM-MIMO radar combines multiple pulses received from each channel into frames, and performs FFT on each frame in both the range and velocity dimensions to obtain... A distance-velocity matrix.

[0041] right The amplitudes of the distance-velocity matrices are summed to obtain a two-dimensional amplitude matrix, which is then processed using CFAR (Constant False Alarm Rate).

[0042] After CFAR processing, the target distance is obtained based on the horizontal and vertical coordinate indices of the detected target point in the two-dimensional amplitude matrix. and initial target point velocity .

[0043] The true target point velocity obtained after deblurring the velocity. :

[0044] in, This is the correction factor.

[0045] For the total number of transmitting antennas Doppler phase compensation is performed on multiple channels of the TDM-MIMO radar: using the first transmitting antenna as a reference, subsequent channels are... Each transmitting antenna corresponds to a receiving channel, minus the phase difference caused by the target's motion. This phase difference is the phase difference introduced between the transmitting antennas of different channels.

[0046] Furthermore, the DOA (Direction of Arrival) estimation is performed on the target point after Doppler phase compensation to obtain the target point angle, thereby obtaining information such as the target point's distance, velocity, angle, and SNR (Signal-to-Noise Ratio).

[0047] By continuously processing the signal dataset corresponding to each frame using the above method, a series of point cloud datasets based on time series can be obtained. The point cloud datasets not only include target points of several suspected target objects, but also information parameters such as the distance, speed, and angle of the target points.

[0048] Step S103: Match all target points in the current point cloud dataset with the track set to be updated to obtain a new track set to be updated; the track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point.

[0049] In other words, the set of tracks to be updated can be a set of tracks generated based on the point cloud dataset corresponding to the previous frame or the previous few frames, as shown in the reference. Figure 2 As shown, this represents a real target (pedestrian) moving towards the radar from a distance (corresponding to...). Figure 2 When the point cloud within the right-hand box (a time-accumulated sequence of multiple consecutive frames) is observed, multiple false target tracks will appear due to multipath reflections from the metal enclosure on the left. Figure 2 As shown in the ellipse on the left. Due to the reflection from the metal fence, the previous point cloud dataset included several false targets in addition to the real targets. The track set to be updated includes tracks generated from real targets, as well as false tracks generated from false targets.

[0050] After identifying all target points in the current point cloud dataset, these target points are further matched with false and real tracks in the track set to be updated. For a target point that successfully matches a track in the track set to be updated, the track point corresponding to that track is added and the track is updated. If a target point fails to match any track in the track set to be updated, a new track is created for that target point and added to the track set to be updated to update the track set.

[0051] Step S104: Calculate the associated point cloud density and track continuity rate for each track in the new set of tracks to be updated. Calculate the associated point cloud density and track continuity rate for each track in the updated set of tracks to be updated after the track matching process. The associated point cloud density reflects the point cloud density in that track, and the track continuity rate reflects the number of consecutively successfully matched frames.

[0052] Step S105: Based on the associated point cloud density and track continuity rate, invalid tracks are filtered out from the new set of tracks to be updated, obtaining the target track set. Using the associated point cloud density and track continuity rate, not only invalid tracks can be identified, but also valid tracks. Invalid tracks can be removed, thus obtaining the target track set containing tracks. (Refer to...) Figure 2 and Figure 3 As shown, Figure 2 For a new set of tracks to be updated, including invalid tracks, i.e. Figure 2 The red line track, the blue line track, the purple line track, and the green line track, while Figure 3 This diagram illustrates the result after filtering out invalid tracks, meaning the target track set only includes tracks corresponding to real targets (marked by green lines). In other words, only point clouds of real targets can form valid tracks and be output; point clouds of false targets cannot form valid tracks and are therefore filtered out. (Refer to...) Figure 3 As shown, the radar output ultimately showed one track, consistent with the actual situation.

[0053] Step S106: Obtain the signal dataset corresponding to the next frame and use the signal dataset as the current signal dataset. Repeat steps S102 to S105 until the millimeter-wave radar stops transmitting signals.

[0054] Since millimeter-wave radar continuously transmits and retrieves signals over a continuous period of time, the target track set can be continuously updated in real time through the above steps S102 to S105 to achieve real-time filtering of invalid tracks until the millimeter-wave radar stops transmitting signals.

[0055] The millimeter-wave radar false alarm filtering method provided in this embodiment processes the data obtained by the millimeter-wave radar to ensure that only the target points of real targets can form valid tracks and output, while the target points of false targets cannot form valid tracks and are thus filtered out. No additional prior information is required, and there are no additional requirements for radar installation and construction. Even in complex multipath reflection environments, it still exhibits good performance, significantly reducing the probability of associated multipath false alarms, improving the accuracy of false target track identification, and without affecting the alarm rate.

[0056] Reference Figure 4As shown, in some optional implementations, step S103 above, which involves matching all target points in the current point cloud dataset with the track set to be updated, includes: Step S1031: Obtain the target point information parameters corresponding to each target point in the current point cloud dataset.

[0057] Step S1032: Determine the end track point of each track in the track set to be updated, and the end track point information parameters corresponding to the end track point. The end track point is the last track point of the current track.

[0058] The track points in the track set to be updated are actually tracks composed of target points from multiple frames. Therefore, the terminal track point information parameters can be directly obtained from the corresponding target point information parameters, and the last track point is the last track point that matches the previous one in time.

[0059] Step S1033: Based on the target point information parameters and the terminal track point information parameters, calculate the first distance value between each target point and each terminal track point, wherein a terminal track point traverses all target points.

[0060] For example: based on the current frame The current point cloud dataset is determined as follows: }, where j is the number of target points detected in the current frame; the current set of tracks to be updated is: }, This represents the current number of tracks.

[0061] For the current point cloud dataset and Point matching is performed using the nearest neighbor clustering algorithm. Each point in Take out its distance and angle Transform into a rectangular coordinate system:

[0062] For each track, iterate through j target points, calculate the distance between the current point on the track and the target point, and match 0 or 1 target points according to the nearest distance principle, where 0 indicates a failed match:

[0063] Until After all j tracks are matched with j target points, the first distance value between each track and each target point is determined.

[0064] Step S1034: Determine the minimum distance value among all first distance values ​​corresponding to the terminal track point, and determine whether the minimum distance value is less than or equal to the first preset value.

[0065] Step S1035: If the minimum distance value is less than or equal to the first preset value, determine that the target point corresponding to the minimum distance value and the corresponding track are successfully matched.

[0066] In some optional implementations, step S1035 further includes determining that if the minimum distance between the target point and all tracks in the track set to be updated is greater than a first preset value, the target point fails to match the tracks in the track set to be updated. Step S1036: To match the failed target point, re-establish the track and obtain the pre-established track. The new set of tracks to be updated includes the pre-established track.

[0067] In other words, if a target point cannot be matched with any of the tracks in the track to be updated, then the target point will fail to match. After all j trajectories have been matched, let the number of times all j target points are successfully matched be . ,satisfy That is, the remainder If a target point fails to match any track, then this Create new flight paths for each target point and assign... Each track is marked as pre-established: When a track is marked as pre-established, the radar does not output the real-time position of the target in the pre-established state. ; At this point, the current set of tracks to be updated is updated to the new set of tracks to be updated: .

[0068] In this embodiment, point cloud clustering is used to aggregate multiple target points in the current point cloud dataset into point track data. For each target point in the point cloud, if it fails to match an existing track, a new track is created and a new track identifier is assigned, marking the track as a pre-established state. By updating the track set, the number of tracks and the status of tracks can be dynamically adjusted, thereby improving the accuracy and robustness of target tracking.

[0069] In some optional implementations, step S104 above, calculating the associated point cloud density for each track in the new set of tracks to be updated, includes: Step a1: Determine the first end track point of the first track. The first track is any track in the new set of tracks to be updated.

[0070] Step a2: Calculate the second distance value between all target points in the current point cloud dataset and the first end track point.

[0071] That is, for Each point in traversal All flight paths Perform matching and calculate points and flight path The second distance value, Step a3: Determine the number of target points in the current point cloud dataset that satisfy the second distance value being less than or equal to the second preset value.

[0072] Determined to meet Number of target points .

[0073] in, This is the second distance value. This is the second preset value.

[0074] Step a4: Calculate the associated point cloud density of the first track based on the number of target points.

[0075] when No operation was performed when Update the density of its associated point cloud:

[0076] In this embodiment, the point cloud density on the track is used as one of the key factors for judging valid and invalid tracks, which can effectively filter out invalid tracks and improve the accuracy of false target track identification.

[0077] In some alternative implementations, the track continuity rate is calculated through the following steps: Step b1: Determine the total number of frames since the establishment of the first track. The first track is any track in the new set of tracks to be updated, where one frame corresponds to one point cloud dataset.

[0078] Step b2: Determine the number of matching frames that successfully match the first track out of the total number of frames.

[0079] Step b3: Calculate the track continuity rate of the first track based on the total number of frames and the number of matched frames.

[0080] For any track ,calculate: Total number of frames since the track was pre-established: ; Since the establishment of this flight path, the number of matching frames successfully matched using the nearest neighbor clustering algorithm is: The number of matching frames satisfies:

[0081] Track continuity rate:

[0082] In this embodiment, the track continuity rate is used as one of the key factors in determining valid and invalid tracks, which can effectively filter out invalid tracks and improve the accuracy of false target track identification.

[0083] In some alternative implementations, before matching all target points in the current point cloud dataset with the track set to be updated, the following steps are also included: Predict the next predicted track point for the track in the track set to be updated; The predicted waypoints are used as the terminal waypoints.

[0084] Treatment of updated track sets For each track, regardless of whether it is pre-established, valid, or invalid, the Kalman filter algorithm is used to calculate the latest polar coordinate position and velocity of the track, and predict the maximum possible position of the target in the next frame. The predicted track point is used as the end track point of the track for matching with the target point.

[0085] In this embodiment, the Kalman filter algorithm is used to process the point position data of each track using the optimal estimation method. That is, the estimated value is used to update the track position and predict the maximum possible position of the target in the next frame, thereby reducing the influence of measurement noise and obtaining more accurate track position information.

[0086] In some optional implementations, invalid tracks in the track set to be updated are filtered out based on the associated point cloud density and track continuity rate to obtain the target track set, including: If the associated point cloud density of a track is less than the preset associated point cloud density, and / or the track continuity rate is less than the preset track continuity rate, the track is determined to be an invalid track.

[0087] If the associated point cloud density of the track is greater than or equal to the preset associated point cloud density, the track continuity rate is greater than or equal to the preset track continuity rate, and the number of matching frames is greater than or equal to the preset number of matching frames, the track is determined to be a valid track.

[0088] Specifically, the tracks in the new set of tracks to be updated are evaluated, including those whose current state is a pre-established track, i.e., those that meet the requirements. Given the flight path, determine whether the following conditions are true:

[0089]

[0090]

[0091] in, Preset matching frame count, To preset the associated point cloud density, The preset track continuity rate.

[0092] If all of the above conditions are met, then the track is marked as a valid track. ; If any of the above conditions are not met, the track is marked as an invalid track: ; The final target track set after track status management is:

[0093] Furthermore, invalid tracks that have timed out can be cleared to ensure the real-time nature of the radar output tracks.

[0094] right Each track Determine whether the following timeout conditions are met:

[0095] This represents the number of consecutive frames that failed to match. If the current frame's clustering match is successful, then... ,otherwise Accumulation, This is the preset number of frames that failed to match.

[0096] If the above conditions are met, then the relevant information about the flight path will be cleared and the flight path identifier will be retrieved.

[0097] The final set of target tracks after track clearing is:

[0098] After track pre-establishment, multiple track matching frames need to be accumulated, and the threshold requirements for associated point cloud density and track continuity rate must be met before a track can be finally formed, meaning the track changes from pre-established to valid. At this point, the radar will output the target's real-time position, velocity, SNR, and other information for that track. If a track in the pre-established or valid state fails to complete track matching in consecutive frames, or if the associated point cloud density and track continuity rate drop below the threshold requirements, it is marked as invalid, and the radar will not output the target's real-time position for tracks in invalid states.

[0099] Compared to native target tracks, accompanying false target tracks exhibit more scattered point cloud distribution, lower density, and lower track continuity. By setting thresholds for the number of matching frames, point cloud density, and track continuity rate, the generation of false target tracks can be effectively suppressed. This effectively solves the false alarm problem caused by reflections from metal fences, barriers, and vehicles in the millimeter-wave radar operating environment. Utilizing the essential characteristics of native target tracks and accompanying false targets generated by multipath effects, and by constraining the number of matching frames, associated point cloud density, and track continuity rate, accurate identification of accompanying false target tracks can be achieved in real-time and with high reliability, yielding excellent results in practice.

[0100] In some optional implementations, the millimeter-wave radar is a multi-channel radar, and the target point information parameters include target point velocity, target point distance, and target point angle. The target point information parameters are determined through the following steps: The transmitted signal is mixed with the corresponding echo signal to obtain a single-frequency intermediate frequency signal. The intermediate frequency signal is sampled, and frequency information is extracted; Distance information is determined based on frequency information; Determine the phase difference between two adjacent cycles in the intermediate frequency signal, and determine the velocity information based on the phase difference; Based on distance and velocity information, determine multiple distance-velocity matrices corresponding to multiple channels; The amplitudes of multiple distance-velocity matrices are summed to obtain a two-dimensional amplitude matrix; CFAR processing is performed on the two-dimensional amplitude matrix to determine the horizontal and vertical coordinate indices in the two-dimensional amplitude matrix, thereby obtaining the target point distance and the initial target point velocity corresponding to the target point; The initial target point velocity is defuzzified to obtain the true target point velocity; Doppler phase compensation is performed on multiple channels of the multi-channel radar, and DOA estimation is performed on the compensated target point to obtain the target point angle.

[0101] The technical solution designed in this embodiment is specifically referred to in step S101 above, and will not be repeated here.

[0102] This embodiment also provides a millimeter-wave radar false alarm filtering device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0103] This embodiment provides a millimeter-wave radar false alarm filtering device, such as... Figure 5As shown, it includes: The acquisition module 201 is used to acquire the current signal dataset corresponding to the current frame. The signal dataset includes the continuously transmitted signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmitted signals. The processing module 202 is used to process the current signal dataset and determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, as well as the target point information parameters corresponding to the target points. The matching module 203 is used to perform point matching between all target points in the current point cloud dataset and the track set to be updated, so as to obtain a new track set to be updated; the track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point; The calculation module 204 is used to calculate the associated point cloud density and track continuity rate of each track in the new set of tracks to be updated; The filtering module 205 is used to filter out invalid tracks in the new track set to be updated based on the associated point cloud density and track continuity rate, so as to obtain the target track set. The loop module 206 is used to acquire the signal dataset corresponding to the next frame and use the signal dataset as the current signal dataset until the millimeter-wave radar stops transmitting signals.

[0104] In some alternative implementations, the matching module 203 includes: The parameter unit is used to obtain the target point information parameters corresponding to each target point in the current point cloud dataset; it is also used to determine the end track point of each track in the track set to be updated, as well as the end track point information parameters corresponding to the end track point. The end track point is the last track point of the current track. The first distance calculation unit is used to calculate the first distance value between each target point and each end point based on the target point information parameters and the end point information parameters, wherein a one end point traverses all target points; The judgment unit is used to determine the minimum distance value among all first distance values ​​corresponding to the terminal track point, and to determine whether the minimum distance value is less than or equal to a first preset value; if the minimum distance value is less than or equal to the first preset value, it is determined that the target point corresponding to the minimum distance value matches the corresponding track successfully. If the minimum distance value between the target point and all tracks in the track set to be updated is greater than the first preset value, it is determined that the target point fails to match the tracks in the track set to be updated. The reconstruction unit is used to re-establish tracks for target points that failed to match, obtain pre-established tracks, and the new set of tracks to be updated includes pre-established tracks.

[0105] In some alternative implementations, the computing module 204 includes: The determining unit is used to determine the first end track point of the first track, where the first track is any track in the new set of tracks to be updated. The second distance calculation unit is used to calculate the second distance value between all target points in the current point cloud dataset and the first end track point respectively; The target point quantity determination unit is used to determine the number of target points in the current point cloud dataset that satisfy the second distance value being less than or equal to the second preset value. The associated point cloud density calculation unit is used to calculate the associated point cloud density of the first track based on the number of target points.

[0106] The track continuity unit is used to determine the total number of frames since the establishment of the first track, which is any track in the new set of tracks to be updated. One frame corresponds to one point cloud dataset. The unit determines the number of matching frames that successfully match the first track out of the total number of frames. Based on the total number of frames and the number of matching frames, the track continuity of the first track is calculated.

[0107] In some alternative embodiments, the apparatus further includes: The prediction unit is used to predict the tracks in the track set to be updated, predict the next predicted track point of the track, and use the predicted track point as the end track point.

[0108] In some alternative embodiments, the apparatus further includes: The target point information parameter determination unit is used to mix the transmitted signal with the corresponding echo signal to obtain a single-frequency intermediate frequency (IF) signal; sample the IF signal and extract frequency information; determine range information based on the frequency information; determine the phase difference between two adjacent cycles in the IF signal and determine velocity information based on the phase difference; determine multiple range-velocity matrices corresponding to multiple channels based on the range and velocity information; accumulate the amplitudes of the multiple range-velocity matrices to obtain a two-dimensional amplitude matrix; perform CFAR processing on the two-dimensional amplitude matrix to determine the horizontal and vertical coordinate indices in the two-dimensional amplitude matrix, and obtain the target point range and initial target point velocity corresponding to the target point; de-ambiguously process the initial target point velocity to obtain the true target point velocity; perform Doppler phase compensation on multiple channels of the multi-channel radar, and perform DOA estimation on the compensated target point to obtain the target point angle.

[0109] In this embodiment, the millimeter-wave radar false alarm filtering device is presented in the form of a functional unit. Here, a unit refers to an ASIC circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0110] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0111] This invention also provides a computer device having the above-described features. Figure 5 The millimeter-wave radar false alarm filtering device shown.

[0112] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0113] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0114] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0115] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0117] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0118] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0119] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for filtering false alarms in millimeter-wave radar, characterized in that, The method includes: Step 101: Obtain the current signal dataset corresponding to the current frame. The signal dataset includes the continuously transmitted signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmitted signals. Step 102: Process the current signal dataset to determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, and the target point information parameters corresponding to the target points. Step 103: Match all target points in the current point cloud dataset with the track set to be updated to obtain a new track set to be updated; the track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point; Step 104: Calculate the associated point cloud density and track continuity rate for each track in the new set of tracks to be updated; wherein, the calculation includes: Determine the first end point of the first track, where the first track is any track in the new set of tracks to be updated; calculate the second distance value between all the target points in the current point cloud dataset and the first end point; determine the number of target points in the current point cloud dataset that satisfy the second distance value being less than or equal to a second preset value; and calculate the associated point cloud density of the first track based on the number of target points. Determine the total number of frames since the establishment of the first track, where one frame corresponds to one point cloud dataset; determine the number of matching frames that successfully match the first track from the total number of frames; calculate the track continuity rate of the first track based on the total number of frames and the number of matching frames; Step 105: Based on the associated point cloud density and the track continuity rate, filter out invalid tracks in the new track set to be updated to obtain the target track set; Step 106: Obtain the signal dataset corresponding to the next frame and use the signal dataset as the current signal dataset. Repeat steps 102 to 105 until the millimeter-wave radar stops transmitting signals.

2. The method according to claim 1, characterized in that, The step of matching all target points in the current point cloud dataset with the track set to be updated includes: Obtain the target point information parameters corresponding to each target point in the current point cloud dataset; Determine the end track point of each track in the set of tracks to be updated, and the end track point information parameters corresponding to the end track point, wherein the end track point is the last track point of the current track; Based on the target point information parameters and the terminal track point information parameters, a first distance value between each target point and each terminal track point is calculated, wherein a terminal track point traverses all target points; Determine the minimum distance value among all the first distance values ​​corresponding to the terminal track point, and determine whether the minimum distance value is less than or equal to a first preset value; If the minimum distance value is less than or equal to the first preset value, it is determined that the target point corresponding to the minimum distance value is successfully matched with the corresponding track.

3. The method according to claim 2, characterized in that, If the minimum distance between the target point and all tracks in the track set to be updated is greater than the first preset value, it is determined that the target point fails to match the tracks in the track set to be updated. To match the failed target point, a new track is established to obtain a pre-established track, and the new set of tracks to be updated includes the pre-established track.

4. The method according to claim 2, characterized in that, Before matching all target points in the current point cloud dataset with the track set to be updated, the method further includes: Predict the next predicted track point for the track in the set of tracks to be updated; The predicted waypoint is used as the final waypoint.

5. The method according to claim 1, characterized in that, The millimeter-wave radar is a multi-channel radar. The target point information parameters include target point velocity, target point distance, and target point angle. The target point information parameters are determined through the following steps: The transmitted signal is mixed with the corresponding echo signal to obtain a single-frequency intermediate frequency signal; The intermediate frequency signal is sampled, and frequency information is extracted; Based on the frequency information, the distance information is determined; Determine the phase difference between two adjacent cycles in the intermediate frequency signal, and determine the velocity information based on the phase difference; Based on the distance information and the speed information, multiple distance-speed matrices corresponding to multiple channels are determined; The magnitudes of the multiple distance-velocity matrices are summed to obtain a two-dimensional magnitude matrix; CFAR processing is performed on the two-dimensional amplitude matrix to determine the horizontal and vertical coordinate indices in the two-dimensional amplitude matrix, thereby obtaining the target point distance and the initial target point velocity corresponding to the target point; The initial target point velocity is defuzzified to obtain the true target point velocity; Doppler phase compensation is performed on multiple channels of the multi-channel radar, and DOA estimation is performed on the compensated target point to obtain the target point angle.

6. A millimeter-wave radar false alarm filtering device, characterized in that, The device includes: The acquisition module is used to acquire the current signal dataset corresponding to the current frame. The signal dataset includes the continuously transmitted signals emitted by the millimeter-wave radar and the echo signals corresponding to the transmitted signals. The processing module is used to process the current signal dataset to determine the current point cloud dataset based on the time series. The current point cloud dataset includes the target points corresponding to the identified suspected target objects, and the target point information parameters corresponding to the target points. The matching module is used to perform point matching between all the target points in the current point cloud dataset and the track set to be updated, so as to obtain a new track set to be updated; the track set to be updated is a track set generated based on the point cloud dataset before the current frame, and each track includes at least one track point; The calculation module is used to calculate the associated point cloud density and track continuity rate of each track in the new set of tracks to be updated; The filtering module is used to filter out invalid tracks in the new track set to be updated based on the associated point cloud density and the track continuity rate, so as to obtain the target track set; The loop module is used to acquire the signal dataset corresponding to the next frame and use the signal dataset as the current signal dataset until the millimeter-wave radar stops transmitting signals. The computing module includes: A determining unit is used to determine the first end track point of the first track, wherein the first track is any track in the new set of tracks to be updated; The second distance calculation unit is used to calculate the second distance value between all the target points in the current point cloud dataset and the first end track point; The target point quantity determination unit is used to determine the number of target points in the current point cloud dataset that satisfy the second distance value being less than or equal to the second preset value. The associated point cloud density calculation unit is used to calculate the associated point cloud density of the first track based on the number of target points; The track continuity unit is used to determine the total number of frames experienced since the establishment of the first track, wherein one frame corresponds to one point cloud dataset; determine the number of matching frames that successfully match the first track from the total number of frames; and calculate the track continuity rate of the first track based on the total number of frames and the number of matching frames.

7. A computer device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the millimeter-wave radar false alarm filtering method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the millimeter-wave radar false alarm filtering method according to any one of claims 1-5.

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