De-masking error correction method, device and terminal for TDM-MIMO radar

Abstract

The application provides a TDM-MIMO radar de-masking error correction method, device and terminal. The method comprises: clustering point cloud data detected by a TDM-MIMO radar to obtain initial point cloud clusters and motion information of to-be-confirmed targets to which each initial point cloud cluster belongs; identifying initial point cloud clusters belonging to the same real target based on the motion information of each to-be-confirmed target to obtain target point cloud clusters, wherein each target point cloud cluster corresponds to a real target; for each target point cloud cluster, taking motion information of to-be-confirmed targets belonging to initial point cloud clusters included in the target point cloud cluster as master information and slave information of a real target to which the target point cloud cluster belongs respectively, and taking the master information and / or the slave information as a measurement value to perform target association to obtain motion information of the real target to which the target point cloud cluster belongs. The application can correct errors when a TDM-MIMO radar de-masking error occurs, and obtain correct target angles or speeds.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, and in particular to a TDM-MIMO radar deambiguity error correction method and device and terminal. BACKGROUND

[0002] The biggest advantage of MIMO (Multiple Input Multiple Output) radar is that it can achieve higher array aperture with fewer antennas, thereby enhancing the resolution and accuracy performance of angle measurement. Therefore, the core task of MIMO signal processing is to separate the data of M transmitting channels from each receiving channel; this requires that the M transmitted signals have separability, which can be achieved through signal diversity technology. TDM-MIMO (Time Division Multiplexing MIMO) refers to transmitting by alternately transmitting each transmitting antenna, that is, each transmitting antenna transmits its own waveform at different times, so that there is no overlap between any two signals. This TDMA (Time Division Multiple Access) transmission method can achieve ideal orthogonality, but this fast-time measurement method has the pulse repetition time (PRT) interval of the chirp signal become M times of the original, and the corresponding unambiguous velocity measurement range is also reduced to M times of the original. Therefore, an unambiguous algorithm is needed to restore the velocity measurement range of the TDM-MIMO radar to the original value.

[0003] At present, commonly used unambiguous algorithms include: an unambiguous algorithm based on Doppler phase offset compensation assumption, an unambiguous algorithm based on overlapping element method, etc. The correct probability of this type of unambiguous algorithm is more or less affected by factors such as target signal-to-noise ratio, array mainlobe-to-sidelobe ratio, and whether there are multiple targets with the same distance and speed. Under the TDM-MIMO system, the angle and velocity information of the radar detected target are coupled, in other words, when the unambiguous error of the detected target occurs, the velocity and angle information of the target are both wrong, and vice versa. The actual working condition is complex and variable, and when the radar detects the unambiguous error of the target, the velocity and angle of the target will both be wrong, which may cause the loss of real targets, the generation of false "ghost" targets, etc. The false negative and false positive of the radar will increase, reducing the reliability of the radar sensor. Therefore, in order to improve the reliability of the TDM-MIMO radar sensor, it is necessary to correct the unambiguous error. SUMMARY

[0004] The embodiment of the present application provides a TDM-MIMO radar de-blinking error correction method, device and terminal, so as to solve the problem of de-blinking error of the TDM-MIMO radar.

[0005] In a first aspect, the embodiment of the present application provides a TDM-MIMO radar de-blinking error correction method, comprising:

[0006] Clustering the point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of to-be-confirmed targets to which each initial point cloud cluster belongs, wherein the initial point cloud clusters correspond to the to-be-confirmed targets one by one;

[0007] Based on the motion information of the to-be-confirmed targets, identifying the initial point cloud clusters belonging to the same real target to obtain target point cloud clusters, wherein each target point cloud cluster corresponds to a real target;

[0008] For each target point cloud cluster, taking the motion information of the to-be-confirmed targets to which the initial point cloud clusters contained in the target point cloud cluster belong as master information and slave information of the real target to which the target point cloud cluster belongs respectively, and taking the master information and / or the slave information as a measurement value to perform target association, so as to obtain the motion information of the real target to which the target point cloud cluster belongs.

[0009] In a possible implementation manner, based on the motion information of the to-be-confirmed targets, the initial point cloud clusters belonging to the same real target are identified, comprising:

[0010] If the motion information of two to-be-confirmed targets satisfies a preset relationship, it is determined that the point cloud clusters corresponding to the two to-be-confirmed targets belong to the same real target;

[0011] The motion information includes distance, speed and angle, and the preset relationship includes:

[0012] The difference between the distances is less than a preset distance threshold value, the difference between the speeds is an integer multiple of a preset speed difference value, and the difference between the angles is equal to a preset angle difference value.

[0013] In a possible implementation manner, the motion information further includes a blurring order;

[0014] The preset relationship further includes:

[0015] The angle of the to-be-confirmed target with a larger blurring order is smaller than the angle of the to-be-confirmed target with a smaller blurring order; or

[0016] The angle of the to-be-confirmed target with a blurring order a is greater than the angle of the to-be-confirmed target with a blurring order b, wherein a is the minimum value of the blurring order and b is the maximum value of the blurring order.

[0017] In a possible implementation, the preset speed difference value is m times of the maximum blurring speed, where m is a blurring coefficient.

[0018] In a possible implementation, for each target point cloud cluster, motion information of a to-be-confirmed target to which an initial point cloud cluster included in the target point cloud cluster belongs is respectively taken as master information and slave information of a real target to which the target point cloud cluster belongs, including:

[0019] For each target point cloud cluster, motion information of a to-be-confirmed target to which an initial point cloud cluster with the maximum signal-to-noise ratio in each initial point cloud cluster included in the target point cloud cluster belongs is taken as master information of a real target to which the target point cloud cluster belongs, and motion information of a to-be-confirmed target to which an initial point cloud cluster with the second maximum signal-to-noise ratio belongs is taken as slave information of the real target.

[0020] In a possible implementation, the master information and / or the slave information are taken as measurement values for target association, including:

[0021] The master information is taken as the measurement value for target association.

[0022] If the master information cannot complete target association as the measurement value, the slave information is taken as the measurement value for target association.

[0023] In a possible implementation, before clustering point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of a to-be-confirmed target to which each initial point cloud cluster belongs, the method further includes:

[0024] Obtaining echo signals detected by the TDM-MIMO radar;

[0025] Performing 2D-FFT and non-coherent accumulation on the echo signals to obtain an RD map;

[0026] Performing constant false alarm detection on the RD map to obtain point cloud data of the to-be-confirmed target.

[0027] In a second aspect, an embodiment of the present application provides a TDM-MIMO radar deblurring error correction device, including:

[0028] A clustering module is configured to cluster point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of a to-be-confirmed target to which each initial point cloud cluster belongs, where the initial point cloud clusters and the to-be-confirmed targets are in one-to-one correspondence;

[0029] A merging module is configured to identify initial point cloud clusters belonging to the same real target in the initial point cloud clusters based on the motion information of each to-be-confirmed target to obtain target point cloud clusters, where each target point cloud cluster corresponds to a real target.

[0030] The association module is configured to, for each target point cloud cluster, take motion information of a to-be-confirmed target to which an initial point cloud cluster contained in the target point cloud cluster belongs as master information and slave information of a real target to which the target point cloud cluster belongs respectively, and take the master information and / or the slave information as a measurement value to perform target association, so as to obtain motion information of the real target to which the target point cloud cluster belongs.

[0031] In a third aspect, an embodiment of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method in the first aspect or any possible implementation manner of the first aspect when executing the computer program.

[0032] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executable by a processor to implement the steps of the method in the first aspect or any possible implementation manner of the first aspect.

[0033] The TDM-MIMO radar deambiguating error correction method, device and terminal provided by the embodiment of the present application have the following beneficial effects:

[0034] When the existing deambiguating method makes an error, the phase complement of the target is wrong, and the point cloud data clustering of the target will obtain multiple clustering clusters instead of one clustering cluster. At this time, if the clustering cluster is randomly selected as a measurement value to perform target association, the possibility of association error is very high. However, the embodiment of the present application uses the coupling relationship between the speed and the angle of the TDM-MIMO radar after deambiguating, identifies the point cloud clusters corresponding to the same target and merges them, can avoid the mutual interference of the point cloud data between different targets, and thus corrects the error when the TDM-MIMO radar makes a deambiguating error, and obtains the correct target angle or speed. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative labor.

[0036] Figure 1 is the implementation flowchart of the TDM-MIMO radar deambiguating error correction method provided by the embodiment of the present application;

[0037] Figure 2 is a schematic diagram of an equivalent virtual array signal model of the TDM-MIMO radar provided by the embodiment of the present application;

[0038] Figure 3 is a signal phase contrast diagram of the TDM-MIMO radar provided by the embodiment of the present application;

[0039] Figure 4 is an angle index change schematic diagram provided by the embodiment of the present application;

[0040] Figure 5 is a structural schematic diagram of the ambiguity error correction device of the TDM-MIMO radar provided by the embodiment of the present application;

[0041] Figure 6 is a schematic diagram of the terminal provided by the embodiment of the present application. DETAILED DESCRIPTION

[0042] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0043] In order to make the objects, technical solutions and advantages of the present application clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0044] Referring to Figure 1 , which shows an implementation flowchart of the ambiguity error correction method of the TDM-MIMO radar provided by the embodiment of the present application, which is described in detail as follows:

[0045] In step 101, the point cloud data detected by the TDM-MIMO radar is clustered to obtain initial point cloud clusters and motion information of the to-be-confirmed target to which each initial point cloud cluster belongs, wherein the initial point cloud cluster is one-to-one corresponding to the to-be-confirmed target, and the motion information includes but is not limited to distance, speed, angle and the like.

[0046] In the embodiment, when the TDM-MIMO radar detects targets, the number of targets is unknown to the radar. In order to determine the number of targets and the information of each target, the point cloud data needs to be clustered. Since the motion information contained in the point cloud data corresponding to the same target is similar, in general, the initial point cloud cluster obtained by clustering corresponds to one target. The motion information of each target can be determined by analyzing the motion information contained in the initial point cloud cluster. However, if an error occurs in the demodulation process before the point cloud data is obtained, the motion information contained in the point cloud data will not be consistent, that is, it may contain false motion information. Therefore, the initial point cloud cluster obtained by clustering will not correspond to one target. The initial point cloud cluster containing false motion information will correspond to a false target, which will eventually lead to the generation of a false "ghost" target, and the motion information of the real target will also be affected.

[0047] In order to avoid the above situation, after obtaining the initial point cloud cluster, it is necessary to confirm whether the target corresponding to each initial point cloud cluster actually exists, and whether each initial point cloud cluster corresponds to one real target. Therefore, the target to be confirmed in the embodiment refers to the target corresponding to the initial point cloud cluster, but it is not determined whether it actually exists.

[0048] Step 102, based on the motion information of each target to be confirmed, identifying the initial point cloud clusters belonging to the same real target to obtain target point cloud clusters, wherein each target point cloud cluster corresponds to one real target.

[0049] In the embodiment, the real target refers to the real target. For the case that multiple initial point cloud clusters correspond to the same real target, the embodiment utilizes the feature that the motion information contained in the point cloud data corresponding to the same real target is close. Based on the motion information of the target to be confirmed, it is determined whether the two or more targets to be confirmed are the same real target. If so, the initial point cloud clusters are merged, the merged point cloud cluster is taken as the target point cloud cluster, and the correct motion information of the real target is further confirmed in the subsequent step. For the case that one initial point cloud cluster corresponds to one real target, it is indicated that no demodulation error occurs, and no correction is needed.

[0050] Step 103, for each target point cloud cluster, taking the motion information of the target to be confirmed to which the initial point cloud cluster contained in the target point cloud cluster belongs as the master information and the slave information of the real target to which the target point cloud cluster belongs respectively, and taking the master information and / or the slave information as the measurement value for target association to obtain the motion information of the real target to which the target point cloud cluster belongs.

[0051] In the embodiment, when there are multiple real targets, a track algorithm can be used for target association, and the trajectories of the multiple real targets are determined respectively, so as to distinguish the real targets. The idea of the track algorithm is: record-predict-use Euclidean distance association, that is, for any real target, the nearest point cloud data is selected from multiple point cloud data corresponding to the current frame based on the nearest neighbor association algorithm as the position of the target in the current frame, so as to associate the point cloud data with the real target, and update the trajectory of the real target.

[0052] On the other hand, before the motion information of the real target is obtained, it cannot be determined which initial point cloud cluster of the multiple initial point cloud clusters corresponding to the real target contains correct motion information, so the master information and the slave information may both contain correct motion information, and in this case, the track algorithm can be used to select correct motion information from the master information and the slave information corresponding to the real target.

[0053] In a possible implementation, the initial point cloud clusters belonging to the same real target are identified based on the motion information of each to-be-confirmed target, and the identification includes:

[0054] If the motion information of the two to-be-confirmed targets satisfies a preset relationship, it is determined that the initial point cloud clusters corresponding to the two to-be-confirmed targets belong to the same real target.

[0055] In some embodiments, the motion information includes distance, speed, and angle, and the preset relationship includes:

[0056] The difference between the distances is less than a preset distance threshold, the difference between the speeds is an integer multiple of a preset speed difference value, and the difference between the angles is equal to a preset angle difference value.

[0057] In the embodiment, when the deblurring error occurs, the motion phase compensation of the radar for the target is wrong, so that the coherent accumulation in the angle dimension cannot be performed as expected. Therefore, the point cloud data belonging to the same target will be divided into two categories due to the difference in speed and angle, and at this time, the probability of occurrence of the missed report and the false alarm will be increased. Meanwhile, for the TDM-MIMO radar, the phase compensation of the echo signal needs to be performed based on the speed of the target when deblurring, and then the angle of the target is calculated, that is, there is a coupling relationship between the speed and the angle obtained by the deblurring of the echo signal of the TDM-MIMO radar. Based on the coupling relationship, the present application determines that the difference between the distances, the difference between the speeds, and the difference between the angles of the multiple initial point cloud clusters corresponding to the same real target all have specific relationships, and based on the specific relationships, whether the two initial point cloud clusters correspond to the same real target can be determined.

[0058] Generally, it is needed to determine whether the to-be-confirmed targets to which the two initial point cloud clusters belong satisfy the preset relationship. If it is needed to determine whether the to-be-confirmed targets A, B and C to which the three initial point cloud clusters belong satisfy the preset relationship, it is respectively determined whether the to-be-confirmed targets A and B satisfy the preset relationship and whether the to-be-confirmed targets A and C satisfy the preset relationship. If both the two combinations satisfy the preset relationship, it is determined that the initial point cloud clusters corresponding to the three to-be-confirmed targets belong to the same real target.

[0059] In a possible implementation, the motion information further includes a blur order;

[0060] The preset relationship further includes:

[0061] An angle of a to-be-confirmed target with a larger blur order is smaller than an angle of a to-be-confirmed target with a smaller blur order; or

[0062] An angle of a to-be-confirmed target with a blur order a is greater than an angle of a to-be-confirmed target with a blur order b, where a is a minimum value of the blur order and b is a maximum value of the blur order.

[0063] In the embodiment, the Time Division Multiple Access (TDMA) transmission mode can achieve ideal orthogonality, but the measurement mode in the fast time interval is changed to M times of the original interval, and the unambiguous measurement range is also reduced to M times of the original range. Therefore, the blur order needs to be introduced, and an unambiguous algorithm is used to restore the measurement range of the TDM-MIMO radar to the original value. Simulation tests can determine that the angles and blur orders of the multiple initial point cloud clusters corresponding to the same real target have a size change relationship, and the judgment based on the size change relationship can improve the accuracy of the judgment result.

[0064] In a possible implementation, the preset speed difference is m times of the maximum blur speed, where m is a blur coefficient.

[0065] In the embodiment, the speed included in each initial point cloud cluster includes a blur speed and a real speed. The blur speed of the initial point cloud cluster can be obtained after constant false alarm detection, and the real speed of the initial point cloud cluster is calculated based on the blur speed and the maximum blur speed of the initial point cloud cluster. The calculation formula of the real speed is:

[0066] v r = m * v max + v a

[0067] where v max is the maximum blur speed, T c M is the period length of the transmitted signal t M is the number of transmitting antennas, λ is the wavelength of the transmitted signal, integer m is the ambiguity factor, v a v is the initial point cloud cluster corresponding to the ambiguous velocity r v is the initial point cloud cluster corresponding to the real velocity calculated. The ambiguity order can represent the number of ambiguous circles of the phase in the motion information, and one circle of the phase is 2pi. If the ambiguity order increases by one, the phase of the motion information is ambiguous by one circle, and the corresponding velocity will increase by one time the maximum ambiguous velocity. Correspondingly, if the ambiguity order decreases by one, the phase is ambiguous by one circle, and the velocity decreases by one time the maximum ambiguous velocity. Therefore, it can be deduced that the difference between the velocities contained in multiple initial point cloud clusters corresponding to the same real target is an integer multiple of the maximum ambiguous velocity.

[0068] In a possible implementation, for each target point cloud cluster, the motion information of the to-be-confirmed target of the initial point cloud cluster to which the initial point cloud cluster contained in the target point cloud cluster belongs is respectively taken as the master information and the slave information of the real target to which the target point cloud cluster belongs, comprising:

[0069] For each target point cloud cluster, the motion information of the to-be-confirmed target of the initial point cloud cluster with the largest signal-to-noise ratio in each initial point cloud cluster contained in the target point cloud cluster is taken as the master information of the real target to which the target point cloud cluster belongs, and the motion information of the to-be-confirmed target of the initial point cloud cluster with the second largest signal-to-noise ratio is taken as the slave information of the real target to which the target point cloud cluster belongs.

[0070] In this embodiment, the higher the signal-to-noise ratio of the initial point cloud cluster is, the higher the confidence is, that is, the higher the possibility that the initial point cloud cluster contains correct motion information is. Taking the motion information of the to-be-confirmed target of the initial point cloud cluster with the largest signal-to-noise ratio as the master information helps to quickly and accurately determine the correct motion information of the real target.

[0071] In a possible implementation, the master information and / or the slave information are taken as the measurement value for target association, comprising:

[0072] The master information is taken as the measurement value for target association;

[0073] If the master information as the measurement value cannot complete target association, the slave information is taken as the measurement value for target association.

[0074] In the embodiment, the Euclidean distance between the comparison measurement value and the predicted value is needed in the target association process, if the Euclidean distance is less than the preset threshold value, the measurement value is added to the trajectory of the target, and the trajectory of the target is updated for the next prediction. Since the main information has a greater probability of containing correct motion information, the main information should be used as the measurement value when performing target association. If the main information cannot complete target association, it means that the main information does not contain the correct motion information of the target, at this time, the slave information is replaced as the measurement value, and the target association is continued.

[0075] In actual use, one real target may correspond to multiple initial point cloud clusters, and in the target point cloud cluster, the real target will obtain one main information and multiple slave information. At this time, target association is performed, the main information and the slave information can be selected in order from high to low according to the signal-to-noise ratio, until the correct motion information of the real target is determined.

[0076] In a possible implementation, before the point cloud data detected by the TDM-MIMO radar is clustered to obtain the initial point cloud cluster and the motion information of the target to be confirmed to which each initial point cloud cluster belongs, the method further includes:

[0077] Obtaining the echo signal detected by the TDM-MIMO radar;

[0078] Performing 2D-FFT and non-coherent accumulation on the echo signal to obtain an RD graph;

[0079] Performing constant false alarm detection on the RD graph to obtain the point cloud data of the target to be confirmed.

[0080] In the embodiment, the RD graph (Range-Doppler) is a range-doppler graph corresponding to the echo signal, and the constant false alarm detection on the RD graph can determine the real distance and the ambiguous velocity of the target. Then, based on the de-ambiguity algorithm and the steps in the above embodiments, the real velocity and the real angle of the target can be determined.

[0081] In a specific embodiment, the complete implementation process of the present application is explained by taking the TDM-MIMO radar as an example, and the number of antennas of the radar is 3. Figure 2 The TDM-MIMO equivalent virtual array signal model is shown in the figure.

[0082] In the patent, the TDM-MIMO radar transmits a frequency-modulated continuous wave (FMCW) signal by time division, and the M t M r The aperture of the radar system of the receiving array is expanded to the equivalent M t M rantenna channels. FMCW is a radar signal with easy modulation, large bandwidth and high resolution. TDM-MIMO radar usually includes RF front-end signal processing, ADC sampling, signal processing algorithm module, data processing algorithm module. The RF part mainly controls the transmission of linear signals, while the radar system generates the LO signal and the received echo signal to do the difference frequency. ADC sampling is mainly to digitize the time domain difference frequency signal. The digitized intermediate frequency signal can be represented as:

[0083]

[0084] where S[n,m t ,m r ] is the digitized intermediate frequency signal, n = 0, 1,...,-1, N is the sampling number, f0 is the center frequency, B is the effective bandwidth, c is the speed of light, φ refl is the unknown phase influence, m t = 0, 1,...,M t -1, m r = 0, 1,...,M r -1, k = 1,...,K, K is the total number of targets, T s is the sampling time interval, d t [m t ] is the reference point position corresponding to the transmitting antenna TX[m t ], d r [m r ] is the reference point position corresponding to the receiving antenna RX[m r ], the position of target k in the scene is (r k , α k ), v r-k is the radial velocity of target k, A k is the signal amplitude value of target k.

[0085] The phase caused by target motion during the time of switching transmitting antennas will be coupled to the channels corresponding to different transmitting antennas, which will affect the accuracy of angle measurement.

[0086] According to the formula, the 2D-FFT (Two-Dimensional Fourier Transform and Filtering) processing is performed on the digitized intermediate frequency signal to obtain:

[0087]

[0088] where S(n,i,m t ,m r ) is the signal after 2D-FFT processing, s i (n,mt r ) is the echo signal of the i th cycle, n = 0, 1,..., N-1, i = 0, 1,..., I-1, I is the total number of cycles.

[0089] The non-coherent accumulation summation of data of all channels will obtain the RD diagram of radar detection, and the range index RangeID and the speed index DopplerID of the target can be extracted by constant false alarm detection on the RD diagram. Correspondingly, the channel information corresponding to RangeID and DopplerID is compensated for the phase change caused by the speed before angle estimation.

[0090] For TDM-MIMO radar, the echo signals other than the echo signals of other corresponding receiving antennas need to be compensated for phase based on TX1. According to the Doppler shift formula f D = 2vf0 / λ, the signal model after compensation can be derived as:

[0091]

[0092] When performing compensation, the real speed of the target needs to be used. When the measured speed of the target is the ambiguous speed, a de-ambiguating algorithm based on Doppler phase compensation assumption or an overlapping element method de-ambiguating algorithm needs to be used to de-ambiguating the target to obtain the real speed of the target. The relationship between the ambiguous speed and the real speed is:

[0093] v r = m*v max +v a

[0094] Where v r is the real speed, v max is the maximum ambiguous speed, the integer m is the ambiguous coefficient, and v a is the ambiguous speed.

[0095] After Doppler compensation, for each valid target, the corresponding peak unit extracted from each 2D-FFT spectrum is subjected to angle dimension FFT to obtain the angle index AzimthID of the target.

[0096] The de-ambiguating algorithm is affected by factors such as signal-to-noise ratio of the target, mainlobe-to-sidelobe ratio of the array, and whether there are multiple targets with the same distance and speed. There may be de-ambiguating errors. Since the target angle obtained by de-ambiguating is based on target speed compensation, when de-ambiguating is incorrect, both the speed and the angle of the target are incorrect.

[0097] ​The above steps yield point cloud data of the target, including information such as radial distance, Doppler velocity, horizontal angle, and signal-to-noise ratio. Typically, the data processing algorithm includes two modules: clustering and trajectory algorithms. The clustering algorithm aims to group initial point cloud clusters belonging to the same real target into a single target point cloud cluster, and calculates the master / slave information of this target point cloud cluster as input measurements for the trajectory algorithm. The trajectory algorithm manages the trajectories of multiple targets based on the input measurements for each frame, performing tasks such as starting, associating, predicting, and determining when targets die.

[0098] This invention, based on the coupling relationship between velocity and angle obtained from the deblurring of TDM-MIMO radar echo signals, proposes the following condition for determining whether to merge initial point cloud clusters. This condition applies not only to uniform arrays but also to sparse arrays. Here, a 3-transmitter, 5-receiver uniform array is used as an example, where d = 2λ, and λ is the wavelength of the radar transmitted signal.

[0099] The angle of the target measured by the radar can be obtained by multiplying AzimthID by the radar resolution δα, where

[0100]

[0101] Where M t M represents the number of transmitting antennas. r Let d be the number of receiving antennas, d be the array element spacing, and α be the horizontal angle of the target. It can be seen that the angle of the target measured by the radar is related to the number of antennas, the array element spacing d, and the horizontal angle α of the target. When deambiguity errors occur, the angle measurement deviation of radars with different antenna parameters will be different. Fortunately, AzimuthID is independent of the number of antennas and the element spacing, and can be considered universally. At the same time, different antenna array configurations correspond to different angular spectra. The arrangement order and number of TDM-MIMO arrays affect the magnitude of the angle index difference. The correspondence between the arrangement order, number, and angle index difference can be confirmed through simulation. Example: Figure 3 As shown, the angle index difference value for the array during unambiguity errors is 32. Simultaneously, through analysis... Figure 3 From the signal phase change, it can be seen that when there are 0th, 1st, and 2nd order unambiguity errors, the angle index change satisfies Figure 4 The addition and subtraction transfer relationship can be used. Therefore, erroneous initial point cloud clusters can be identified and merged through the angle index change relationship. During merging, the motion information contained in the initial point cloud cluster with a high signal-to-noise ratio is used as the master information after merging, while the motion information contained in the point cloud cluster with a low signal-to-noise ratio is used as the slave information. Specifically, point cloud clusters that meet the following conditions are merged:

[0102] (1) The distance is close; if the difference in distance is less than the preset distance threshold, the two point cloud clusters are determined to be close in distance.

[0103] (2) Let the true velocities of the two point cloud clusters be v1 and v2, and let the two true velocities satisfy m*v max +v1=v2, where the true velocity of the point cloud cluster is expressed by the formula v r =m*v max +v a Calculations show that v r For the actual speed, v max The maximum fuzzy velocity is given by m, where m is the fuzzy coefficient and v is the fuzzy velocity. a To illustrate the fuzzy velocity, this application uses an example of three transmitting antennas, resulting in three fuzzy orders: m = -1, 0, and 1. The fuzzy order will change as the number of transmitting antennas varies.

[0104] (3) The angle difference is equal to the preset angle difference value, which can be determined by simulation based on the arrangement order and number of radar arrays;

[0105] (4) The changes in angle and the changes in order satisfy the following conditions. Figure 4 The relationship shown

[0106] In the correlation module of the trajectory algorithm, the primary information of the cluster is used as the measurement value for target correlation first. When the primary information cannot complete the correlation, the secondary information can be used for target correlation. Thus, the true speed and angle information of the target can be determined.

[0107] After acquiring multiple initial point cloud clusters, this invention utilizes the coupling relationship between velocity and angle after TDM-MIMO radar deblurring to identify and merge initial point cloud clusters corresponding to the same target. This avoids mutual interference between point cloud data of different targets, thereby correcting the deblurring error when TDM-MIMO radar occurs and obtaining the correct target angle or velocity.

[0108] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0109] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0110] Figure 5 A schematic diagram of the deambiguity error correction device for TDM-MIMO radar provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0111] like Figure 5 As shown, the deambiguation error correction device 5 of the TDM-MIMO radar includes:

[0112] a clustering module 51, configured to cluster point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of to-be-confirmed targets to which the initial point cloud clusters belong, wherein the initial point cloud clusters correspond to the to-be-confirmed targets one by one;

[0113] a merging module 52, configured to identify initial point cloud clusters belonging to a same real target in the initial point cloud clusters based on the motion information of the to-be-confirmed targets, to obtain target point cloud clusters, wherein each target point cloud cluster corresponds to a real target;

[0114] an association module 53, configured to, for each target point cloud cluster, take motion information of to-be-confirmed targets to which initial point cloud clusters included in the target point cloud cluster belong as master information and slave information of a real target to which the target point cloud cluster belongs respectively, and perform target association by taking the master information and / or the slave information as a measurement value, to obtain motion information of the real target to which the target point cloud cluster belongs.

[0115] In a possible implementation, the merging module 52 is specifically configured to:

[0116] determine that initial point cloud clusters corresponding to two to-be-confirmed targets belong to a same real target when the motion information of the two to-be-confirmed targets satisfies a preset relationship;

[0117] The motion information includes distance, speed, and angle, and the preset relationship includes:

[0118] a difference between distances is less than a preset distance threshold, a difference between speeds is an integer multiple of a preset speed difference value, and a difference between angles is equal to a preset angle difference value.

[0119] In a possible implementation, the motion information further includes a blur order.

[0120] The preset relationship further includes:

[0121] an angle of a to-be-confirmed target with a larger blur order is less than an angle of a to-be-confirmed target with a smaller blur order; or

[0122] an angle of a to-be-confirmed target with a blur order of a is greater than an angle of a to-be-confirmed target with a blur order of b, where a is a minimum value of the blur order and b is a maximum value of the blur order.

[0123] In a possible implementation, the preset speed difference value is m times of a maximum blur speed, where m is a blur coefficient.

[0124] In a possible implementation, the association module 53 is specifically configured to:

[0125] For each target point cloud cluster, the motion information of the to-be-confirmed target in the initial point cloud cluster with the maximum signal-to-noise ratio among the point cloud clusters included in the target point cloud cluster is taken as the primary information of the real target to which the target point cloud cluster belongs, and the motion information of the to-be-confirmed target in the initial point cloud cluster with the second maximum signal-to-noise ratio is taken as the secondary information of the real target to which the target point cloud cluster belongs.

[0126] In a possible implementation, the association module 53 is specifically configured to:

[0127] perform target association by taking the primary information as a measurement value;

[0128] perform target association by taking the secondary information as a measurement value if the target association cannot be completed by taking the primary information as a measurement value.

[0129] In a possible implementation, the clustering module 51 is further configured to:

[0130] obtain echo signals detected by the TDM-MIMO radar before clustering the point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and the motion information of the to-be-confirmed target to which each initial point cloud cluster belongs;

[0131] perform 2D-FFT and non-coherent accumulation on the echo signals to obtain an RD map;

[0132] perform constant false alarm detection on the RD map to obtain point cloud data of the to-be-confirmed target.

[0133] After obtaining a plurality of point cloud clusters, the coupling relationship between the speed and the angle after deambiguating by the TDM-MIMO radar is used to identify and merge the point cloud clusters corresponding to the same target, so that the point cloud data of different targets can be prevented from interfering with each other, and the correct target angle or speed can be obtained when deambiguating errors occur in the TDM-MIMO radar.

[0134] Figure 6 is a schematic diagram of a terminal provided by an embodiment of the present application. As shown in the figure, the terminal 6 of this embodiment comprises a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. The processor 60 implements the steps in each of the TDM-MIMO radar deambiguating error correction method embodiments described above when executing the computer program 62, such as steps 101 to 103 shown in the figure. Alternatively, the processor 60 implements the functions of each module / unit in each of the device embodiments described above when executing the computer program 62, such as the functions of modules / units 51 to 53 shown in the figure. Figure 6 Figure 1 Figure 5

[0135] ​​​For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 62 in the terminal 6. For example, the computer program 62 can be divided into Figure 5 the modules / units 51 to 53 shown.

[0136] The terminal 6 can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like computing device. The terminal 6 can include, but is not limited to, the processor 60, the memory 61. Those skilled in the art can understand that the terminal 6 can include more or less components, or combine certain components, or different components, for example, the terminal can also include an input / output device, a network access device, a bus and the like. Figure 6 The terminal 6 shown is only an example and does not constitute a limitation on the terminal 6, and can include more or less components than shown, or combine certain components, or different components, for example, the terminal can also include an input / output device, a network access device, a bus and the like.

[0137] The processor 60 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0138] The memory 61 can be an internal storage unit of the terminal 6, such as a hard disk or a memory of the terminal 6. The memory 61 can also be an external storage device of the terminal 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card and the like provided on the terminal 6. Further, the memory 61 can include both the internal storage unit and the external storage device of the terminal 6. The memory 61 is used to store the computer program and other programs and data required by the terminal. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0140] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0142] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented by other ways. For example, the above-mentioned apparatus / terminal embodiments are only schematic, and the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0143] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0144] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0145] The integrated module / unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can implement the steps of each TDM-MIMO radar error correction method embodiment described above when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. that can carry the computer program code. It should be noted that the computer-readable medium can include or exclude contents according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0146] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for correcting deambiguity errors in TDM-MIMO radar, characterized in that, include: Clustering is performed on the point cloud data detected by TDM-MIMO radar to obtain initial point cloud clusters and motion information of the target to be confirmed to each initial point cloud cluster, wherein the initial point cloud clusters correspond one-to-one with the target to be confirmed. Based on the motion information of each target to be confirmed, an initial point cloud cluster belonging to the same real target is identified to obtain a target point cloud cluster, wherein one target point cloud cluster corresponds to one real target; For each target point cloud cluster, the motion information of the target to be confirmed to which the initial point cloud cluster contained in the target point cloud cluster belongs is used as the main information and the secondary information of the real target to which the target point cloud cluster belongs, respectively. The main information and / or the secondary information are used as measurement values ​​for target association to obtain the motion information of the real target to which the target point cloud cluster belongs. The step of identifying initial point cloud clusters belonging to the same real target based on the motion information of each target to be confirmed includes: If the motion information of two targets to be confirmed satisfies a preset relationship, then the initial point cloud clusters corresponding to the two targets to be confirmed are determined to belong to the same real target. The motion information includes distance, speed, and angle, and the preset relationship includes: The distance difference is less than the preset distance threshold, the speed difference is an integer multiple of the preset speed difference, and the angle difference is equal to the preset angle difference. The motion information also includes the fuzzy order; The preset relationship also includes: The angle of the target with the higher fuzzy order among the two targets to be confirmed is smaller than the angle of the target with the lower fuzzy order; or The angle of the target to be confirmed with fuzzy order a is greater than the angle of the target to be confirmed with fuzzy order b, where a is the minimum value of the fuzzy order and b is the maximum value of the fuzzy order. For each target point cloud cluster, the motion information of the target to be confirmed belonging to the initial point cloud cluster contained in the target point cloud cluster is respectively used as the master information and slave information of the real target to which the target point cloud cluster belongs, including: For each target point cloud cluster, the motion information of the target to be confirmed belonging to the initial point cloud cluster with the highest signal-to-noise ratio among all the initial point cloud clusters contained in the target point cloud cluster is taken as the master information of the real target to which the target point cloud cluster belongs, and the motion information of the target to be confirmed belonging to the initial point cloud cluster with the second highest signal-to-noise ratio is taken as the slave information of the real target to which the target point cloud cluster belongs. The step of using the master information and / or the slave information as measurement values ​​for target association includes: The master information is used as a measurement value for target association; If the master information cannot be used as a measurement value to complete the target association, then the slave information will be used as a measurement value for target association.

2. The method for correcting deambiguity errors in TDM-MIMO radar according to claim 1, characterized in that, The preset speed difference is the maximum fuzzy speed. times, of which, is the fuzzy coefficient.

3. The method for deambiguity correction of TDM-MIMO radar according to any one of claims 1 or 2, characterized in that, Before clustering the point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of the target to be confirmed to each initial point cloud cluster, the method further includes: Acquire echo signals obtained from TDM-MIMO radar detection; The echo signal is subjected to 2D-FFT and non-coherent accumulation to obtain the RD diagram; Constant false alarm rate (CFAR) detection is performed on the RD map to obtain point cloud data of the target to be confirmed.

4. A deambiguity error correction device for TDM-MIMO radar, characterized in that, include: The clustering module is used to cluster the point cloud data detected by the TDM-MIMO radar to obtain initial point cloud clusters and motion information of the target to be confirmed to each initial point cloud cluster, wherein the initial point cloud clusters correspond one-to-one with the target to be confirmed. The merging module is used to identify initial point cloud clusters belonging to the same real target based on the motion information of each target to be confirmed, and to obtain target point cloud clusters, wherein each target point cloud cluster corresponds to a real target; The association module is used to, for each target point cloud cluster, take the motion information of the target to be confirmed to which the initial point cloud cluster contained in the target point cloud cluster belongs as the main information and the secondary information of the real target to which the target point cloud cluster belongs, respectively, and use the main information and / or the secondary information as measurement values ​​to perform target association, so as to obtain the motion information of the real target to which the target point cloud cluster belongs; The merging module is specifically used for: If the motion information of two targets to be confirmed satisfies a preset relationship, then the initial point cloud clusters corresponding to the two targets to be confirmed are determined to belong to the same real target. The motion information includes distance, speed, and angle, and the preset relationship includes: The distance difference is less than the preset distance threshold, the speed difference is an integer multiple of the preset speed difference, and the angle difference is equal to the preset angle difference. The motion information also includes the fuzzy order; The preset relationship also includes: The angle of the target with the higher fuzzy order among the two targets to be confirmed is smaller than the angle of the target with the lower fuzzy order; or The angle of the target to be confirmed with fuzzy order a is greater than the angle of the target to be confirmed with fuzzy order b, where a is the minimum value of the fuzzy order and b is the maximum value of the fuzzy order. The association module is specifically used for: For each target point cloud cluster, the motion information of the target to be confirmed belonging to the initial point cloud cluster with the highest signal-to-noise ratio among all the initial point cloud clusters contained in the target point cloud cluster is taken as the master information of the real target to which the target point cloud cluster belongs, and the motion information of the target to be confirmed belonging to the initial point cloud cluster with the second highest signal-to-noise ratio is taken as the slave information of the real target to which the target point cloud cluster belongs. The association module is specifically used for: The master information is used as a measurement value for target association; If the master information cannot be used as a measurement value to complete the target association, then the slave information will be used as a measurement value for target association.

5. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 3 above.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 3 above.

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