Multi-channel joint interference mitigation

Abstract

This document describes techniques, apparatus, and systems for multi-channel joint interference mitigation. Radar radiation received by a radar system can include interference from other nearby radar systems. The interference can cause a decrease in sensitivity of the radar system. The techniques, apparatus, and systems described herein mitigate the interference by identifying a group of samples of the radar radiation that has the interference. The interference can be estimated and mitigated by comparing the group (e.g., with the interference) to a group of samples without the interference and suppressing the interference without suppressing the detected signal. Furthermore, the interference can be analyzed to determine whether the interference contains detection information of one or more objects (e.g., other vehicles with radar systems that caused the interference). In this way, interference mitigation can be obtained without losing information included in the group of samples that includes the interference.

Description

Background Technology

[0001] Radar systems can provide distance, speed, azimuth, and elevation information of objects in the surrounding environment of vehicles equipped with perception systems (including autonomous or semi-autonomous driving systems). This object information is used to provide features such as adaptive cruise control, lane centering, lane change assist, and emergency braking. As radar-equipped vehicles become increasingly common, the presence of radar interference can hinder radar performance. Such interference can increase the noise floor during signal processing, potentially leading to unreliable object detection and tracking. Summary of the Invention

[0002] This document describes techniques, apparatus, and systems for multi-channel joint interference mitigation. Radar radiation received by a radar system can include interference from other nearby radar systems. Interference can reduce the sensitivity of a radar system. The techniques, apparatus, and systems described herein mitigate interference by identifying sample groups of radar radiation with interference. Interference can be estimated and mitigated by comparing this group (e.g., with interference) to a sample group without interference and suppressing the interference without suppressing the detected signal. Furthermore, the interference can be analyzed to determine whether it contains detection information of one or more objects (e.g., other vehicles with the interfering radar system). In this way, interference mitigation can be achieved without losing information included in the sample group containing interference.

[0003] In one example, the processor of the radar system determines that interference exists in the radar radiation received by the radar system. The method further includes mitigating the interference in the radar radiation by: identifying a first sample group of interference-free radar radiation; identifying a second sample group of radar radiation including interference; estimating the interference in the second sample group by comparing the first sample group with the second sample group; and suppressing the interference in the radar radiation based on the interference estimation. The method further includes generating radar data based on the mitigation of interference in the radar radiation. The method further includes: outputting the radar data for use in detecting or tracking at least one of the objects in the environment of the radar system.

[0004] In another example, a system includes a processor configured to perform this method and other methods. In addition to describing systems configured to perform the methods outlined above and other methods set forth herein, this document also describes a computer-readable storage medium including instructions that, when executed, configure at least one processor to perform the methods outlined above and other methods set forth herein.

[0005] This invention provides a simplified concept related to multi-channel joint interference mitigation, which is further described in the detailed description and accompanying drawings. This invention is not intended to identify essential features of the claimed subject matter, nor is it intended to define the scope of the claimed subject matter. Although primarily described in the context of mitigating inter-vehicle interference to reduce false detections and increase sensitivity, the techniques for multi-channel joint interference mitigation can be applied to other applications where radar interference mitigation is desired. Attached Figure Description

[0006] This document describes in detail one or more aspects of multi-channel joint interference mitigation with reference to the following figures: Figure 1 An example environment in which multi-channel joint interference mitigation can be applied according to the techniques of this disclosure is shown; Figure 2 An example of an automotive system configured to perform multi-channel joint interference mitigation according to the technology of this disclosure is shown; Figure 3 An example diagram of received radar radiation in which multi-channel joint interference mitigation can be applied according to the technology of this disclosure is shown; Figure 4 An example method for multi-channel joint interference mitigation according to the technology of this disclosure is shown; and Figure 5 An example general method for mitigating joint interference in multiple channels according to the techniques of this disclosure is shown. Detailed Implementation Overview

[0007] Vehicles equipped with radar systems use various techniques to counter radar jamming. However, many of these techniques have drawbacks. Typically, these techniques assume that the jamming and target signals originate from two different angles. However, in a vehicle environment, for example, the jamming might originate from a target with a radar system. That is, the target signal and the jamming signal originate from the same angle.

[0008] A common technique involves zero-forcing interference. Zero-forcing sets the sample amplitude within a signal segment to zero to eliminate interference. However, applying zero amplitude to segments of the sample introduces gaps in the signal. During further radar processing, these gaps can introduce spectral spread around strong targets and lead to false detections by the radar system, potentially increasing the processing burden on the radar system.

[0009] Suppose there are M channels (i.e., M receiving antennas), and an example is implemented on a per-channel basis. The fast-time sample of the m-th channel can be modeled using equation (1): Where s m (t), i m (t) and n m (t) represent the statistically independent components of the signal, interference, and noise, respectively. Index set T i This represents the duration of the interference, and its complement T i This indicates the duration of no interference. Here, An index containing all samples, while Equal to empty set After interference detection and localization, zero-forcing interference can be achieved using equation (2): In other words, all detected samples will be forced to zero, while all signal samples will be output unchanged. However, since this method is implemented on a channel-by-channel basis, it does not take advantage of the coherence characteristics between different channels.

[0010] Nulling detected interference can lead to phase discontinuities because the radar signal (e.g., for each channel) is simultaneously nulled. Consequently, spectral spread exists around strong targets, which can reduce resolution (e.g., weak targets are concealed in the spread spectrum) and increase false detections. Furthermore, while nulling is easily achieved, it loses the possibility of recovering the radar signal at the nulling location because it does not utilize the spatial coherence characteristics between different channels.

[0011] Different methods have been implemented to mitigate the adverse effects of zero-forcing. One such method involves approximating the lost samples forced to zero by interference via the inverse transform of the strongest peak in the spectrum. This per-channel approximation is achieved using a pair of chirped-length point Fast Fourier Transforms (FFTs) and Inverse Fast Fourier Transforms (IFFTs), which increases computational complexity. Similarly, the interference mitigation here is implemented per channel, and consequently, the coherence characteristics between different channels are not utilized.

[0012] Alternatively, non-zero-forcing techniques can also be implemented. For example, non-zero-forcing techniques can use a projection scheme to suppress interference, where the autocorrelation matrix used for eigenvalue decomposition to generate the projection matrix is ​​calculated from an L-trajectory matrix, where both rows and columns of the L-trajectory matrix are subsequences of the fast time samples. For example, the value of the parameter L can be chosen from 128 to 256. Therefore, the computational complexity of eigenvalue decomposition of the autocorrelation matrix can be very high. Furthermore, choosing an appropriate threshold for interference suppression can be difficult. This method is applicable only to a single contaminated time series.

[0013] Compared to techniques that may involve too much complexity, radar data loss, or high false detection rates, multi-channel joint interference mitigation, as described in this paper, is a more effective and efficient technique for mitigating interference present in radar signals. The technique described in this paper can detect and locate radar radiation samples contaminated by interference. The radiation associated with the contaminated samples can include target signals and interference caused by radar energy emitted by the target. These contaminated samples are compared with uncontaminated samples to estimate the interference. This interference can then be suppressed in the radiation. Furthermore, the interference can be analyzed against additional radar data (e.g., additional targets emitting radar energy from different angles).

[0014] These techniques overcome the drawbacks of other techniques by utilizing the spatial coherence characteristics between different channels. By projecting only the contaminated samples from all samples using the techniques described herein, there may be no phase discontinuities in the projected output. Consequently, the noise floor can be reduced, and there is almost no spectral spread that would reduce resolution and / or increase false detection. Similarly, the computational complexity of multi-channel joint interference mitigation depends on the length of the fast time sample and the number of receiving antennas. When the number of receiving channels is small (e.g., M=4), the computational complexity of these techniques is negligible. Example Environment

[0015] Figure 1 An example environment 100 in which multi-channel joint jamming mitigation can be applied according to the technology of this disclosure is shown. Vehicle 102 is equipped with a radar system 104 configured for multi-channel joint jamming mitigation. Although depicted as a car, vehicle 102 can represent other types of vehicles and machinery (e.g., motorcycles, buses, tractors, semi-trailer trucks, watercraft, aircraft, other heavy equipment), including manned or unmanned systems that can be used for various purposes.

[0016] Radar system 104 may include one or more radar sensors 106 and radar tracking module 108. Through the radar sensors 106 specifically located on vehicle 102, radar system 104 provides an instrumented field of view (FOV) that can cover one or more vehicles 110 (including vehicles 110-1 and 110-2). For example, positioning the radar sensors 106 together can ensure that the FOV includes the area above road 112, the area adjacent to road 112, or the area on road 112 on which vehicle 102 may be traveling. Radar sensors 106 can capture signals from any location on vehicle 102 (including behind side mirrors, bumpers, roofs, or any other part of vehicle 102, or integrated into side mirrors, bumpers, roofs, or any other part of vehicle 102) to obtain the desired FOV.

[0017] Through radar tracking module 108, radar system 104 is configured to detect other vehicles, pedestrians, or other objects traveling on or near road 112. Radar tracking module 108 correlates emitted radar signal 114-1 with received radar signal 114-2 (e.g., received radar radiation), which is reflected from the surface of an object (such as vehicle 110-1). Within environment 100, other objects (including vehicles 110-1 and 110-2) may be using radar, and vehicles 110-1 and 110-2 may be emitting radar signals 116 and 118, respectively. Radar system 104 can detect interference to radar signal 114-2 caused by radar signals 116 and 118. If tracking relies on interference, this interference may cause radar tracking module 108 to have difficulty tracking and may report inaccurate results.

[0018] Radar tracking module 108 is configured to enable radar system 104 to mitigate radar interference using multi-channel joint interference mitigation. Radar tracking module 108 may employ an angle filter to filter out interference in received radar radiation (e.g., radar signal 114-2) and locate the angle of the interference source. Radar tracking module 108 can achieve this by identifying contaminated and uncontaminated sample groups. Radar tracking module 108 can compare (e.g., subtract) the contaminated and uncontaminated sample groups to identify the interference and then suppress the interference in the received radar radiation. Furthermore, the angle filter can determine that the interference originates from vehicle 110-1 (e.g., radar signal 116) and / or vehicle 110-2 (e.g., radar signal 118). Radar tracking module 108 can use the angle of the interference source and the reflected radar signal 114-2 to identify and track objects in the FOV. Removing interference from radar radiation and retaining information derived from the interference both result in more accurate radar data and reduce false positives detected by radar system 104. Example System

[0019] Figure 2 An example of an automotive system 200 configured to perform multi-channel joint interference mitigation according to the technology of this disclosure is shown. The automotive system 200 can be integrated into... Figure 1The vehicle 102 is shown and described in this context. For example, vehicle system 200 may include controller 202 and radar system 104. Radar system 104 and controller 202 communicate via link 204. Link 204 may be a wired or wireless link and in some cases includes a communication bus. Controller 202 performs operations based on information received via link 204 (such as indications from radar system 104 of one or more objects traveling on road 112 when objects in the FOV are identified according to processed radar signal 114-2).

[0020] The controller 202 includes a processor 206 and a computer-readable storage medium (CRM) 208 (e.g., memory, long-term storage, short-term storage) that stores instructions for the vehicle module 210. The radar system 104 includes one or more radar sensors 106. The radar system 104 may include processing hardware including one or more processors 212 (e.g., hardware processors, processing units) and a CRM 214. The CRM 214 stores instructions associated with the radar tracking module 108, which may include instructions associated with the interference mitigation submodule 216 and the object tracking submodule 218.

[0021] Processors 206 and 212 can be two separate processing units or a single processing unit (e.g., a microprocessor, multiple processors within a processing unit) or a pair of on-chip systems or a single on-chip system of a computing device, controller, or control unit. Processors 206 and 212 execute computer-executable instructions stored within CRMs 208 and 214. As an example, processor 206 can execute vehicle module 210 to perform driving functions of vehicle system 200 (e.g., autonomous lane change maneuvers, semi-autonomous lane keeping features) or other operations. Similarly, processor 212 can execute radar tracking module 108, including executing instructions for implementing interference mitigation submodule 216.

[0022] The interference mitigation submodule 216 configures the radar tracking module 108 to mitigate interference present in the radar signal 114-2 obtained from the radar sensor 106 of the vehicle 102. The interference mitigation submodule 216 can modify (e.g., preprocess) the radar signal 114-2 before the radar tracking module 108 can use the radar signal 114-2 to detect objects or perform tracking.

[0023] The object tracking submodule 218 receives output from the interference mitigation submodule 216 and enables the radar tracking module 108 to infer objects in the field of view (FOV) based on a pre-processed version of the radar signal 114-2 provided by the interference mitigation submodule 216. In response to the object tracking submodule 218 detecting and tracking objects determined from these pre-processed versions of the radar signal 114-2, the radar tracking module 108 outputs indications of one or more objects detected by the object tracking submodule 218.

[0024] Typically, vehicle system 200 executes vehicle module 210 to perform vehicle functions, which may include using output from radar system 104. When vehicle module 210 is executed at processor 206, vehicle module 210 may receive indications of objects detected by object tracking submodule 218 to enable one or more of these vehicle functions.

[0025] For example, vehicle module 210 can provide adaptive cruise control and monitor radar system 104 to obtain output indicating the presence of an object in or near the field of view (FOV), for example, to reduce speed and prevent a collision with the rear of vehicle 110-1. In such an example, object tracking submodule 218 provides radar-based data as output to vehicle module 210. The radar data indicates objects detected or tracked from preprocessed radar signals 114-2, rather than objects detected or tracked based on radar signals 114-2 initially received at the antenna of radar system 104.

[0026] When interference-mitigated radar data obtained from the object tracking submodule 218 indicates one or more objects in the collision zone around vehicle 102, vehicle module 210 can provide an alert or perform specific maneuvers. By using preprocessed radar signals 114-2 obtained from interference mitigation submodule 216, object tracking submodule 218 can more accurately detect objects within the collision zone and FOV, which reduces false detections caused by radar interference in noisy environments, such as driving scenarios where other vehicles operate their own onboard radar systems. Example Implementation

[0027] Figure 3 An example figure 300 is shown of received radar radiation in which multi-channel joint interference mitigation can be applied according to the techniques of this disclosure. The figure has a frequency dimension on a sample size including radar radiation receiving interference from a radar system (e.g., radar system 104) configured to mitigate interference using the techniques described herein.

[0028] The first sample group 302 is identified as having no interference 306, and the second sample group 304 includes interference 306. The second sample group 304 may be a combination of multiple individual groups. The first sample group 302 and the second sample group 304 may include radar signals and noise radiation reflected from an object. Interference 306 may originate from the same object reflecting radar signals (e.g., Figure 1 The means of transport 110-1) and / or other objects (e.g., Figure 1 (Vehicles in 110-2).

[0029] The interference 306 in the second sample group 304 is estimated by comparing the first sample group 302 with the second sample group 304. In one example, this can be achieved by calculating the interference-signal-noise covariance matrix using equation (3): Using equation (4) in index set T i And on the signal-noise covariance matrix: In the index Set above. Here, x(t) = [x1(t),…,x M (t)] T This represents the radar received signal at the t-th snapshot. The number of snapshots used for covariance matrix calculation (|T) i | and The number of channels in a radar system is much greater than the number of channels in a radar system (i.e., |T) i |>>M and For example, the number of samples in a chirp (i.e., The number of channels can be as high as 1024, and the number of channels can be, for example, four.

[0030] The interference covariance matrix (e.g., the estimated interference) can be obtained from the interference-signal-noise covariance using equation (5). Subtract signal-noise covariance To determine: When |T i |→∞ and At that time, the interference covariance matrix Approximately pure interference (e.g., no noise / estimation error).

[0031] Interference 306 can then be suppressed in the radar radiation received by the radar system. Continuing this example, the interference covariance can be estimated using equation (6). eigenvalue decomposition to construct projection matrix Where D is a diagonal matrix representing eigenvalues ​​in descending order, and matrix V contains the corresponding eigenvectors. The number of main interference components can be determined by finding the maximum ratio of adjacent eigenvalues ​​using equation (7): Where d(m) is the m-th eigenvalue. Besides the maximum ratio criterion, other complex source enumeration methods (including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Minimum Description Length (MDL)) are also suitable for determining the number of major interference components. Therefore, the projection matrix can be constructed using equation (8): Where I is the identity matrix. Furthermore, the number of primary interference components can be related to the number of objects generating the interference. The angle of origin of the interference source is also determined here. This allows the radar system to determine whether the object represented in the reflected radar signal is the source of the primary interference component, and how many other interference sources might be present.

[0032] To avoid phase discontinuities, equation (9) can be used to project the matrix. Applied to all fast-time samples x(t): In the estimated covariance matrix Since there are no signal components in the time domain, only the interference components will be projected (i.e., suppressed). In this way, the radar system can have less spectral spread due to the removal of phase discontinuities in the time-domain interference. This can lead to higher radar resolution and fewer false detections. Furthermore, since the number of channels M is much smaller than the number of fast time samples... The computational complexity can be reduced to a negligible level. Example Method

[0033] Figure 4 An example method 400 for multi-channel joint interference mitigation according to the technology of this disclosure is shown. At 402, samples contaminated by interference are detected and located (Equation 1). The radar radiation received by the radar system configured for multi-channel joint interference mitigation may include radar signals reflected from objects, noise, and / or interference. Interference can be detected by several different techniques. In a non-limiting example, interference detection may be based on the amplitude of the received radar radiation. Amplitudes above a threshold can be considered interference. If interference is detected, the group of samples containing interference is identified and distinguished from samples without interference.

[0034] At position 404, the interference-signal-noise covariance matrix and the signal-noise covariance matrix are calculated (Equations 3 and 4, respectively). The interference-signal-noise covariance matrix is ​​based on contaminated samples, while the signal-noise covariance matrix is ​​based on uncontaminated samples. Over time, the total number of samples (e.g., a combination of contaminated and uncontaminated samples) may increase and become much larger than the number of channels in the radar system, potentially reducing the computational complexity of these calculations to a negligible level.

[0035] At position 406, the interference covariance matrix is ​​estimated by comparing the interference-signal-noise covariance matrix with the signal-noise covariance matrix (Equation 5). The signal and noise components can be subtracted from the interference-signal-noise covariance, leaving only the interference component and the estimation error. The estimation error decreases as more radar radiation is received.

[0036] At position 408, the projection matrix is ​​constructed based on the eigenvalue decomposition of the interference covariance matrix (Equations 6 to 8). During this step, the number of major interference components can be determined, and the object causing the interference (e.g., other vehicles radiating radar energy) can be identified based on the angle of origin of the major interference components.

[0037] At position 410, all fast-time samples of the radar radiation are multiplied by the projection matrix (Equation 9). This calculation results in the projection of interference components from the radar radiation, leaving only the signal and noise components in the radar radiation samples. By projecting all samples instead of just the contaminated ones, the projected output may have no phase discontinuities. This reduces spectral spread, thereby improving resolution and reducing false detections in the radar system.

[0038] Figure 5 An example general method 500 for multi-channel joint interference mitigation according to the techniques of this disclosure is illustrated. At 502, interference present in the radar radiation received by the radar system is determined. The radar system can use different techniques to determine the presence of interference in the radar radiation. One example technique may involve identifying samples of radar radiation with amplitudes above a threshold.

[0039] At 504, interference in the radar radiation is mitigated. The process for mitigating interference includes several steps. At 504-1, a first sample group of radar radiation without interference is identified, while at 504-2, a second sample group of radar radiation including interference is identified. In some examples, the signal-noise covariance can be calculated based on the first sample group, and the interference-signal-covariance matrix can be calculated based on the second sample group. At 504-3, the interference can be estimated by comparing the first sample group with the second sample group. In some examples, the signal-noise covariance matrix can be subtracted from the interference-signal-noise covariance matrix to estimate the interference covariance matrix representing the interference in the radar radiation. At 504-4, interference in the radar radiation is suppressed. In some examples, eigenvalue decomposition of the interference covariance matrix can be performed, and an interference projection matrix can be generated. The interference projection matrix can be applied to all fast samples of the radar radiation to project the interference present in the radar radiation.

[0040] At point 506, radar data is generated based on the mitigation of interference present in the radar radiation. Once the preprocessing steps (steps 504-1 to 504-4) are completed, the mitigated radar radiation can be processed to obtain radar data relating to objects in the environment of the radar system. Based on these steps so far, the generated mitigated radar radiation can reduce spectral spread and result in fewer false detections.

[0041] At step 508, radar data is output for at least detecting and tracking objects in the environment of the radar system. For example, if the radar system is equipped on a vehicle, the radar data can be used to detect and track objects in the vehicle's path. Because this radar data can include fewer false detections than radar data generated by conventional means, it can be more accurate and result in a safer user experience for vehicle users. Additional examples

[0042] Example 1: A method comprising: determining, by a processor of a radar system, that interference exists in radar radiation received by the radar system; mitigating the interference in the radar radiation by: identifying a first sample group of radar radiation without interference; identifying a second sample group of radar radiation including interference; estimating the interference in the second sample group by comparing the first sample group with the second sample group; suppressing the interference in the radar radiation based on the estimation of the interference; generating radar data based on the mitigation of the interference in the radar radiation; and outputting the radar data for use in detecting or tracking at least one of objects in the environment of the radar system.

[0043] Example 2: The method of Example 1, wherein the first sample group and the second sample group include: radar signals reflected from the object; and noise radiation.

[0044] Example 3: A method in any of the preceding examples, wherein identifying the first sample group includes: calculating the signal-noise covariance matrix based on the signal and noise components of the radar radiation; and calculating the interference-signal-noise covariance matrix based on the signal, interference, and noise components of the radar radiation.

[0045] Example 4: The method of any of the previous examples, wherein estimating the interference in the second sample group includes estimating the interference covariance matrix by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

[0046] Example 5: The method of any of the preceding examples further includes: determining one or more angles from which the interference originates, to identify one or more sources of interference.

[0047] Example 6: A method in any of the previous examples, wherein determining one or more angles of the source of interference includes: performing eigenvalue decomposition on the interference covariance matrix to determine the number of major interference components in the interference.

[0048] Example 7: A method in any of the previous examples, wherein suppressing interference includes: constructing an interference projection matrix based on performing eigenvalue decomposition on the interference covariance matrix; and applying the interference projection matrix to the radar radiation to remove interference from the radar radiation.

[0049] Example 8: A system comprising: one or more processors configured to: determine, by a processor of a radar system, that interference exists in radar radiation received by the radar system; mitigate the interference in the radar radiation by: identifying a first sample group of radar radiation without interference; identifying a second sample group of radar radiation including interference; estimating the interference in the second sample group by comparing the first sample group with the second sample group; suppressing the interference in the radar radiation based on the estimated interference; generating radar data based on the mitigated radar radiation; and outputting the radar data for at least detecting or tracking objects in the environment of the radar system.

[0050] Example 9: A system in any of the preceding examples, wherein the first and second sample groups comprise: radar signals reflected from an object; and noise radiation.

[0051] Example 10: A system in any of the preceding examples, wherein one or more processors are configured to identify a first sample group by at least: calculating a signal-noise covariance matrix comprising the signal component and noise component of the radar radiation; and calculating an interference-signal-noise covariance matrix comprising the signal component, interference component and noise component of the radar radiation.

[0052] Example 11: In any of the previous examples, a system in which one or more processors are configured to estimate the interference in the second sample group by at least the following manner: by estimating the interference covariance matrix by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

[0053] Example 12: A system in any of the preceding examples, wherein one or more processors are further configured to: determine one or more angles from which the interference originates, in order to identify one or more sources of the interference.

[0054] Example 13: In any of the previous examples, a system in which one or more processors are configured to analyze radar-detected interference by performing eigenvalue decomposition on the interference covariance matrix to determine the number of major interference components in the interference.

[0055] Example 14: In any of the preceding examples, a system in which one or more processors are configured to suppress interference by at least: constructing an interference projection matrix based on performing eigenvalue decomposition on the interference covariance matrix; and applying the interference projection matrix to radar radiation to remove interference from the radar radiation.

[0056] Example 15: A computer-readable storage medium comprising instructions that, when executed, configure at least one processor to: determine, by a processor of a radar system, that interference is present in radar radiation received by the radar system; mitigate the interference present in the radar radiation by: identifying a first sample group of radar radiation without interference; identifying a second sample group of radar radiation including interference; estimating the interference in the second sample group by comparing the first sample group with the second sample group; suppressing the interference in the radar radiation based on the estimated interference; generating radar data based on the mitigated radar radiation; and outputting the radar data for at least detecting or tracking objects in the environment of the radar system.

[0057] Example 16: A computer-readable storage medium of any of the preceding examples, wherein the instructions further configure at least one processor to identify a first sample group by at least: calculating a signal-noise covariance matrix based on the signal and noise components of radar radiation; and calculating an interference-signal-noise covariance matrix based on the signal, interference, and noise components of radar radiation.

[0058] Example 17: A computer-readable storage medium of any of the preceding examples, wherein instructions further configure at least one processor to estimate interference in a second sample group by at least estimating the interference covariance matrix by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

[0059] Example 18: A computer-readable storage medium of any of the preceding examples, wherein the instructions are further configured to: determine one or more angles of the source of interference to identify one or more sources of interference.

[0060] Example 19: A computer-readable storage medium of any of the preceding examples, wherein instructions are further configured to determine one or more angles of the source of interference by performing eigenvalue decomposition on the interference covariance matrix to determine the number of major interference components in the interference.

[0061] Example 20: A computer-readable storage medium of any of the preceding examples, wherein the instructions are further configured to suppress interference by at least: constructing an interference projection matrix based on performing eigenvalue decomposition on the interference covariance matrix; and applying the interference projection matrix to radar radiation to remove interference from the radar radiation. Conclusion

[0062] While various embodiments of this disclosure have been described in the foregoing description and illustrated in the accompanying drawings, it should be understood that this disclosure is not limited thereto, but can be practiced in various ways within the scope of the following claims. It will be apparent from the foregoing description that various modifications can be made without departing from the spirit and scope of this disclosure as defined by the following claims. Problems associated with radar interference mitigation may occur in other systems. Therefore, although described as one way to improve radar interference mitigation in vehicle-based radar, the techniques described above can be applied to other systems requiring electromagnetic interference mitigation. Furthermore, the techniques described herein have been described in the context of digital signal processing. In theory, similar techniques can be applied using analog signal processing.

[0063] Unless the context explicitly states otherwise, the use of "or" and grammatically related terms indicates an unrestricted, non-exclusive alternative. As used herein, the phrase referring to "at least one" of a list of items means any combination of those items, including a single member. As an example, "at least one of a, b, or c" is intended to cover: a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other ordering of a, b, and c).

Claims

1. A method, the method comprising: The radar system's processor determines that multi-channel joint interference exists in the radar radiation received by the radar system's multiple receiving channels; Before generating radar data based on the radar radiation, the multi-channel joint interference in the radar radiation is mitigated by the following methods: The first receiving channel from the plurality of receiving channels identifies a first sample group of radar radiation that is free from interference. A second sample group including the interference in the radar radiation is identified from the plurality of receiving channels by a second receiving channel that is different from the first receiving channel; The interference in the second sample group is estimated by comparing the first sample group with the second sample group; as well as Based on the estimated interference, the interference in the radar radiation is suppressed; The radar data is generated based on the mitigation of the multi-channel joint interference in the radar radiation; as well as The radar data is output for use in detecting or tracking at least one of the objects in the environment of the radar system.

2. The method as described in claim 1, characterized in that, The first sample group and the second sample group include: Radar signals reflected from the object; and Noise radiation.

3. The method as described in claim 2, characterized in that, The first sample group is identified by: The signal-noise covariance matrix is ​​calculated based on the signal and noise components of the radar radiation; and The interference-signal-noise covariance matrix is ​​calculated based on the signal component, interference component, and noise component of the radar radiation.

4. The method as described in claim 3, characterized in that, Estimating the interference in the second sample group includes: The interference covariance matrix is ​​estimated by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

5. The method of claim 4, further comprising: Determine one or more angles from which the interference originates to identify one or more sources of the interference.

6. The method as described in claim 5, characterized in that, Determining the source of the interference from one or more angles includes: Eigenvalue decomposition is performed on the interference covariance matrix to determine the number of major interference components in the interference.

7. The method as described in claim 6, characterized in that, Suppressing the interference includes: Based on the eigenvalue decomposition performed on the interference covariance matrix, an interference projection matrix is ​​constructed; and The interference projection matrix is ​​applied to the radar radiation to remove the interference from the radar radiation.

8. A system comprising: One or more processors, said one or more processors being configured to: The radar system's processor determines that multi-channel joint interference exists in the radar radiation received by the radar system's multiple receiving channels; Before generating radar data based on the radar radiation, the interference present in the radar radiation is mitigated by the following methods: A first sample group of radar radiation without interference is identified from the first receiving channel among the plurality of receiving channels; A second sample group of radar radiation that includes the interference is identified from the plurality of receiving channels, which is different from the first receiving channel. The interference in the second sample group is estimated by comparing the first sample group with the second sample group; Based on the estimated interference, the interference in the radar radiation is suppressed; The radar data is generated based on the mitigation of the multi-channel joint interference in the radar radiation; as well as The radar data is output for at least the detection or tracking of objects in the environment of the radar system.

9. The system as described in claim 8, characterized in that, The first sample group and the second sample group include: Radar signals reflected from the object; and Noise radiation.

10. The system as described in claim 9, characterized in that, The one or more processors are configured to identify the first sample group at least in the following ways: Calculate the signal-noise covariance matrix that includes the signal and noise components of the radar radiation; as well as Calculate the interference-signal-noise covariance matrix, which includes the signal component, interference component, and noise component of the radar radiation.

11. The system as claimed in claim 10, characterized in that, The one or more processors are configured to estimate the interference in the second sample group at least by: The interference covariance matrix is ​​estimated by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

12. The system as claimed in claim 11, characterized in that, The one or more processors are further configured to: Determine one or more angles from which the interference originates to identify one or more sources of the interference.

13. The system as described in claim 12, characterized in that, The one or more processors are configured to analyze the radar-detected interference in at least the following ways: Eigenvalue decomposition is performed on the interference covariance matrix to determine the number of major interference components in the interference.

14. The system as claimed in claim 11, characterized in that, The one or more processors are configured to suppress the interference at least in the following ways: Based on the eigenvalue decomposition of the interference covariance matrix, an interference projection matrix is ​​constructed; as well as The interference projection matrix is ​​applied to the radar radiation to remove the interference from the radar radiation.

15. A computer-readable storage medium comprising instructions that, when executed, configure at least one processor to: The radar system's processor determines that multi-channel joint interference exists in the radar radiation received by the radar system's multiple receiving channels; Before generating radar data based on the radar radiation, the interference present in the radar radiation is mitigated by the following methods: A first sample group of radar radiation without interference is identified from the first receiving channel among the plurality of receiving channels; A second sample group of radar radiation that includes the interference is identified from the plurality of receiving channels, which is different from the first receiving channel. The interference in the second sample group is estimated by comparing the first sample group with the second sample group; Based on the estimated interference, the interference in the radar radiation is suppressed; The radar data is generated based on the mitigation of the multi-channel joint interference in the radar radiation; as well as The radar data is output for at least the detection or tracking of objects in the environment of the radar system.

16. The computer-readable storage medium as claimed in claim 15, characterized in that, The instructions further configure the at least one processor to identify the first sample group at least in the following ways: The signal-noise covariance matrix is ​​calculated based on the signal and noise components of the radar radiation. as well as The interference-signal-noise covariance matrix is ​​calculated based on the signal component, interference component, and noise component of the radar radiation.

17. The computer-readable storage medium as claimed in claim 16, characterized in that, The instructions further configure the at least one processor to estimate the interference in the second sample group at least by: The interference covariance matrix is ​​estimated by comparing the signal-noise covariance matrix with the interference-signal-noise covariance matrix.

18. The computer-readable storage medium as claimed in claim 17, characterized in that, The instruction is further configured to be used for: Determine one or more angles from which the interference originates to identify one or more sources of the interference.

19. The computer-readable storage medium as claimed in claim 18, characterized in that, The instructions are further configured to determine one or more angles from which the interference originates, at least in the following ways: Eigenvalue decomposition is performed on the interference covariance matrix to determine the number of major interference components in the interference.

20. The computer-readable storage medium as claimed in claim 18, characterized in that, The instructions are further configured to suppress the interference at least in the following ways: Based on the eigenvalue decomposition of the interference covariance matrix, an interference projection matrix is ​​constructed; as well as The interference projection matrix is ​​applied to the radar radiation to remove the interference from the radar radiation.

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