Techniques for mitigating interference in radar signals

Abstract

Techniques for improving detection and mitigation of synchronous and asynchronous interference in radar signals. Corrupted received signals can be processed to reduce the effects of interference while preserving existing targets. Various can use a two-step approach: (1) detect and mask corrupted samples, and (2) recover samples hidden by the mask. The recovery step enforces sparsity of existing targets, preventing target smearing, a common problem after interference mitigation. The sparsity-enforced recovery step can successfully remove interference while preserving small targets. The technique does not require any prior knowledge of the parameters of the interfering radar.

Description

[Technical field]

[0001] This document relates generally, but not exclusively, to radar systems, and more particularly, to radar systems for use in vehicles. [Background technology]

[0002] Radars are present on passenger cars to provide several safety-related and convenience features, including emergency braking, adaptive cruise control, and automatic parking. The scene observed by a vehicle-mounted radar may contain many scattering centers: other vehicles, the road surface, objects at the edge of the road, pedestrians, etc. The raw measurements made by the radar are a combination of echoes generated by each of these objects, and noise. Using various techniques, the radar processes the raw measurements and is thereby able to determine several quantities related to each target in the scene, such as the range to the target, the radial component of the target's relative velocity, and the angle the line of sight to the target makes with the radar antenna. Summary of the Invention [Means for solving the problem]

[0003] The present disclosure relates to techniques that can improve the detection and mitigation of synchronous and asynchronous interference in radar signals. Various techniques of the present disclosure can be used to process corrupted received signals to reduce the effects of interference while preserving existing targets. Various techniques of the present disclosure are based on a two-step approach: (1) detect and mask corrupted samples, and (2) recover samples hidden by the mask. The recovery step enforces sparsity of existing targets and prevents them from being blurred, which is a common problem after interference mitigation. The sparsity-enforced recovery step can successfully remove interference while preserving small targets. The techniques of the present disclosure do not require any prior knowledge of the parameters of the interfering radar.

[0004] In some aspects, the present disclosure relates to a radar system having a first transceiver unit for mitigating interference caused by a signal transmitted by a second transceiver unit of another radar system, the radar system comprising: the first transceiver unit for transmitting a first signal toward a target and receiving an interference corrupted combined signal including an echo signal from the target responsive to the transmitted first signal and the second signal transmitted by the second transceiver unit, the first transceiver unit and the second transceiver unit having corresponding transmission parameters; and a processor for detecting synchronous interference in the interference corrupted combined signal, the processor for determining a frequency domain representation of the interference corrupted combined signal, determining a variation in phase characteristics of the representation corresponding to designated range bins, and assigning designated distance ranges as exhibiting synchronous interference based on the variation.

[0005] In some aspects, the present disclosure relates to a radar system for mitigating interference caused by a signal transmitted by a second transceiver unit of another radar system, the radar system having a first transceiver unit for transmitting a first signal toward a target and receiving an interference corrupted combined signal including an echo signal from the target in response to the transmitted signal and a second signal transmitted by the second transceiver unit, the first transceiver unit and the second transceiver unit having non-identical transmission parameters, and a processor for mitigating asynchronous interference in the interference corrupted combined signal, the processor for determining whether interference is present in a time domain representation of the interference corrupted combined signal, suppressing samples corresponding to the interference to create a masked signal from a mask (M), and using the time domain representation (Y) of the interference corrupted combined signal and constructing a corrected frequency domain representation (X*) of the interference corrupted combined signal using the mask (M).

[0006] In some aspects, the present disclosure provides a radar system for mitigating interference caused by a signal transmitted by a second transceiver unit of another radar system, the first transceiver unit for transmitting a first signal toward a target and receiving an interference corrupted combined signal including an echo signal from the target in response to the transmitted first signal and the second signal transmitted by the second transceiver unit, and a processor for detecting synchronous interference in the combined signal, the processor determining a frequency domain representation of the combined signal and determining variations in phase characteristics of the representation corresponding to designated range bins. and assigning designated range bins as exhibiting synchronous interference based on the dispersion; and a processor for mitigating asynchronous interference in an interference corrupted combined signal, comprising: a processor for determining whether interference is present in a time domain representation of the interference corrupted combined signal; suppressing samples corresponding to the interference to create a masked signal from a mask (M); and using the time domain representation (Y) of the interference corrupted combined signal and constructing a corrected frequency domain representation (X*) of the interference corrupted combined signal using the mask (M).

[0007] The drawings are not necessarily to scale, but like numbers may describe similar components in different figures. Like numbers with different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a conceptual diagram of an example vehicle including a radar system in which various techniques of the present disclosure can be implemented. [Diagram 2] FIG. 1 is a simplified block diagram of an example radar system in which various techniques of the present disclosure can be implemented. [Figure 3A] FIG. 1 is an example of a range-Doppler image obtained from multiple chirps. [Figure 3B] FIG. 1 depicts synchronous interference of range-Doppler images. [Figure 4A] FIG. 1 is an example of a range-Doppler image obtained from multiple chirps. [Figure 4B] FIG. 1 illustrates asynchronous interference of range-Doppler images. [Diagram 5] FIG. 1 is a conceptual diagram illustrating two vehicles and a target with corresponding radar transceiver units. [Figure 6] FIG. 13 is an example of a range-FFT of a single chirp, where the range-FFT depicts the baseline signal of a real target and the signal corrupted with an additional false target due to synchronous interference. [Figure 7] 1 is an example of a graph depicting phase in range bins across multiple chirps. [Figure 8] 1 is a graph depicting the short-time Fourier transform (STFT) of a chirp corrupted by asynchronous interference. [Figure 9] 13 is a graph depicting the STFT of a chirp in a fourth frequency bin corrupted by asynchronous interference. [Figure 10] 1 is a graph illustrating a time domain mask for one frame. [Figure 11A] 1 is a graph illustrating an example of a frame without asynchronous interference. [Figure 11B] 1 is a graph illustrating an example of a frame without asynchronous interference. [Figure 12A] 1 is a graph illustrating an example of a frame corrupted by asynchronous interference. [Figure 12B] 1 is a graph illustrating an example of a frame corrupted by asynchronous interference. [Figure 13A] 12B is a graph illustrating the asynchronous interference corrupted frame of FIG. 12A with a mask applied; [Figure 13B] 12C is a graph illustrating the frame corrupted by the asynchronous interference of FIG. 12B with a mask applied; [Figure 14A] 12B is a graph illustrating the asynchronous interference corrupted frame of FIG. 12A after correction using 2D FFT techniques. [Figure 14B] 12C is a graph illustrating the asynchronous interference corrupted frame of FIG. 12B after correction using 2D FFT techniques. [Figure 15A] 12B is a graph illustrating the asynchronous interference corrupted frame of FIG. 12A after correction using 1D FFT techniques. [Figure 15B] 12C is a graph illustrating the asynchronous interference corrupted frame of FIG. 12B after correction using 1D FFT techniques. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] An autonomous vehicle (also called a "self-driving car" or "driverless car") is a vehicle that can use on-board sensors and / or radar systems to sense and react to its environment, thereby enabling the vehicle to respond to the environment without human involvement. A radar system mounted on a vehicle can emit a transmitted signal and receive a reflected back echo signal from one or more targets that the radar system attempts to detect, such as a vehicle in front of the vehicle on which the radar system is mounted, in the same lane.

[0010] However, as the number of radar-equipped vehicles increases, there is an increasing possibility of interference between radar systems. The receiver of a radar system may detect both the echo signal and any transmissions from the radar systems of other vehicles, such as vehicles moving toward the vehicle equipped with a radar system in another lane. These transmissions from other radar systems may interfere with and corrupt the received echo signal, which may mask the target that the radar system is attempting to detect.

[0011] There are two types of automotive radar interference: synchronous interference and asynchronous interference. In synchronous interference, the host transceiver unit and the interferer transceiver unit have corresponding transmission parameters, e.g., nearly identical transmission parameters, i.e., corresponding bandwidths and slow / fast axis times, etc. In asynchronous interference, the host transceiver unit and the interferer transceiver unit have non-identical transmission parameters, i.e., different bandwidths and slow / fast axis times, etc.

[0012] The present inventors have recognized a need for improved detection and mitigation of synchronous and asynchronous interference in radar signals. Using various techniques of the present disclosure, corrupted received signals can be processed to reduce the effects of interference while preserving existing targets. Various techniques of the present disclosure are based on a two-step approach: (1) detect and mask corrupted samples, and (2) recover samples hidden by the mask. The recovery step enforces sparsity of existing targets and prevents them from being blurred, which is a common problem after interference mitigation. The sparsity-enforced recovery step can successfully remove interference while preserving small targets. The techniques of the present disclosure do not require any prior knowledge of the parameters of the interfering radar.

[0013] 1 is a conceptual diagram of an example vehicle 100 including a radar system 102 capable of implementing various techniques of the present disclosure. The radar system 102 may include two or more radar transceiver units 104 that may be disposed on or within the vehicle 100. Each of the radar transceiver units 104 may transmit a signal and receive an echo signal in response to the transmitted signal. By using various techniques described below, the radar system 102 may mitigate interference in the radar signal by repairing the received signal so as to accurately detect targets, such as stationary or moving vehicles, buildings, trees, etc., even in the presence of interference.

[0014] 2 is a simplified block diagram of an example of a radar system capable of implementing various techniques of the present disclosure. The radar system 102 may include two or more radar transceiver units 202A-202N. In some examples, the radar transceiver units 202A-202N may implement a frequency modulated continuous wave (FMCW) radar technique. At least two radar transceiver units may be used to determine two-dimensional (2D) motion parameters. At least three radar transceiver units may be used to determine three-dimensional (3D) motion parameters.

[0015] The radar transceiver unit 202A can include a signal generator 204A that can be used to generate an electromagnetic signal for transmission. The signal generator 204A can include, for example, a frequency synthesizer, a waveform generator, and a master oscillator. In some examples, the signal generator 204A can generate the signal as one or more chirps, where a chirp is a sinusoidal signal having a frequency that increases over time. The signal generator 204A can generate a signal that can be transmitted by a transmit antenna TX1 toward the environment. The radar transceiver unit 202A can include one or more receive antennas RX1 for receiving echo signals in response to the transmitted signal.

[0016] The transmitted signal and the received echo signal may be applied to corresponding inputs of a mixer 206A to generate an intermediate frequency (IF) signal. The IF signal may be applied to a filter 208A, such as a low-pass filter, and the filtered signal may be applied to an analog-to-digital converter (ADC) 210A. The antenna RX1, the mixer 206A, the filter 208A, and the ADC 210A may form a receive channel 211A.

[0017] 2, the radar system 102 can include a second radar transceiver unit 202B. In some examples, the radar system 102 can include more than two radar transceiver units, such as for determining 3D motion parameters. The radar transceiver units 202B-202N can include components similar to those of the radar transceiver unit 202A.

[0018] The digital outputs of ADCs 210A-210N may be applied to a computer system 212. Computer system 212 may include a processor 214, which may include a digital signal processor (DSP), and a memory device 216 coupled to processor 214, which may store instructions 218 for processor 214 to execute that specify actions to be taken by computer system 212.

[0019] In some examples, the radar system 102 may include a sensor system 220 that may provide sensor data to the computer system 212. The sensor system 220 may include, for example, one or more of an inertial measurement unit (IMU) 222, a global positioning system (GPS) 224, and / or a camera 226.

[0020] Using various techniques of this disclosure, as described in more detail below, processor 214 can process received corrupted radar samples to reduce the effects of interference without knowledge of the interfering radar parameters. For example, processor 214 can detect time intervals in which interference is present and then mask out the corrupted samples (interference detection). Processor 214 can then recover the masked out samples to reduce distortion of existing targets. Processor 214 can recover the samples, for example, by enforcing sparsity in the Fourier domain and solving an optimization problem that preserves existing targets.

[0021] The synchronous interference detection techniques of this disclosure are described first, followed by a description of asynchronous interference detection and mitigation techniques. In synchronous interference, the interference is not localized in time. The techniques of this disclosure only perform detection of synchronous interference.

[0022] As mentioned above, in synchronous interference, the host transceiver unit and the interferer transceiver unit may have corresponding transmission parameters, e.g., nearly identical transmission parameters, i.e., corresponding bandwidths and corresponding slow / fast axis times, etc. However, each time the host transceiver unit transmits, the starting phase of the transmitted chirp is different. The techniques of this disclosure can exploit that phase difference to distinguish between the host radar system and the interfering radar system.

[0023] 3A and 3B are examples of range-Doppler images obtained from multiple chirps, with 3B depicting synchronous interference. A range-Doppler image is a two-dimensional (2D) fast Fourier transform (FFT) performed on data from one or more of ADCs 210A-210N (of FIG. 2), where peaks in the frequency spectrum correspond directly to the range of some object or target. In FIG. 3A and 3B, the x-axis represents velocity in meters per second (m / s) and the y-axis represents distance in meters (m).

[0024] Image 300 in Figure 3A represents a "clean" signal received by a radar transceiver unit of a host radar system, such as radar transceiver unit 202A in Figure 2, where the clean signal includes return signals from targets in response to the transmitted signal, and non-interfering signals from any interfering radar transceiver units. In the absence of interference, such as in Figure 3A, clear targets can be seen, such as shown at 302.

[0025] Image 304 in FIG. 3B represents a “corrupted” signal with synchronous interference received by a radar transceiver unit of a host radar system, such as radar transceiver unit 202A of FIG. 2, where the corrupted signal includes a return signal from a target in response to the transmitted signal and interference signals from one or more interfering radar transceiver units.

[0026] When interference is present, such as in FIG. 3B, clear targets, such as shown at 306, can be much more difficult to distinguish from noise in image 304. Differences in phase noise of the host radar system's radar transceiver unit and the interfering radar transceiver unit may result in one or more line artifacts, such as line artifact 308. These artifacts can be detected but may be difficult to remove. Various techniques of this disclosure may use the phase noise of the transceiver units to determine if synchronous interference is present.

[0027] Figures 4A and 4B are examples of range-Doppler images obtained from multiple chirps, with Figure 4B depicting asynchronous interference, where the x-axis represents velocity in meters per second (m / s) and the y-axis represents distance in meters (m).

[0028] Image 400 in Figure 4A represents a "clean" signal received by a radar transceiver unit of a host radar system, such as radar transceiver unit 202A in Figure 2, where the clean signal includes return signals from targets in response to the transmitted signal, and non-interfering signals from any interfering radar transceiver units. In the absence of interference, such as in Figure 4A, clear targets can be seen, such as shown at 402.

[0029] Image 404 in Figure 4B represents a "corrupted" signal with asynchronous interference received by a radar transceiver unit of a host radar system, such as radar transceiver unit 202A of Figure 2, where the corrupted signal includes a return signal from a target in response to the transmitted signal and interference signals from one or more interfering radar transceiver units. When there is interference, such as in Figure 4B, a distinct target, such as shown at 406, can become much more difficult to discern from the noise in image 404.

[0030] Figure 5 is a conceptual diagram illustrating two vehicles and a target including corresponding radar transceiver units. In Figure 5, a host vehicle 500 may include a radar system, such as the radar system of Figure 2. The radar system may use various techniques of the present disclosure to mitigate interference caused by signals transmitted by a second transceiver unit of another radar system, such as synchronous and / or asynchronous interference.

[0031] The host vehicle 500 is fitted with a radar system that may include a first transceiver unit, such as the radar transceiver unit 202A of Figure 2, to transmit a first signal 502 towards a target 504, e.g., a second vehicle, and receive an interference corrupted and combined signal including an echo signal 506 from the target 504 in response to the transmitted first signal 502 and a second signal 508 transmitted by a second transceiver unit, such as mounted on a third vehicle 510. The interference corrupted and combined signal may survive for multiple chirps.

[0032] Using various techniques, as described in more detail below, to detect synchronous interference in the combined signals corrupted by interference, a processor such as processor 214 of FIG. 2 can determine a frequency domain representation, such as an FFT, of the combined signals corrupted by interference, determine a variation, such as standard deviation, variance, interquartile range, etc., of the phase characteristics of the representation that correspond to designated range bins, and assign the designated range bins as exhibiting synchronous interference based on the variation.

[0033] Figure 6 is an example of the range-FFT of a single chirp, which depicts the baseline signal of the real target and the signal corrupted with an additional false target due to synchronous interference. The x-axis represents the range bin number and the y-axis represents the magnitude of the range-FFT.

[0034] In the absence of synchronous interference, only the baseline signal 600 (the true target) is present. However, in the presence of synchronous interference, additional false targets will still appear in the corrupted signal 602. A processor, such as the processor 214 of FIG. 2, can determine a frequency domain representation of the interference corrupted signal 602. In some examples, the frequency domain representation can include a discrete Fourier transform, such as obtained by an FFT. FIG. 6 graphically illustrates an example of a frequency domain representation of the interference corrupted and combined signals.

[0035] To distinguish between real and false targets, the synchronous interference techniques of the present disclosure can take advantage of how phase varies between different chirps.

[0036] Figure 7 is an example of a graph depicting phase in range bins across multiple chirps. The x-axis represents chirp number and the y-axis represents phase in radians. In Figure 7, each range bin of the baseline signal 600 (real target) of Figure 6 and the corrupted signal 602 (false target) of Figure 6 has been phase unwrapped. The graph can be generated by a processor such as processor 214 of Figure 2 by taking the range-FFT of all the chirps and looking at a particular range bin for each of the range-FFTs.

[0037] As can be seen in Figure 7, the phase remains relatively constant between chirps for a real target 700, but varies between chirps for a false target 702. For a stationary target, the derivative between chirps is zero, and for a moving target, the derivative between chirps is constant.

[0038] The processor may determine the variance, such as the variance, standard deviation, interquartile range, etc., of the phase characteristic, such as the derivative of the expression corresponding to the specified range bin. For example, the processor 214 of FIG. 2 may determine the standard deviation of the derivative of the phase at each range bin.

[0039] A large standard deviation of the derivative of a specified range bin may indicate a false target and thus can be used to detect synchronous interference. Thus, if the standard deviation of the derivative of a specified range bin meets some criteria, the processor can assign the specified range bin as exhibiting synchronous interference. For example, the processor can compare the variance, such as the standard deviation, to a threshold value.

[0040] In some examples, the processor may determine multiple metrics such as variability of phase characteristics, such as derivatives of expressions corresponding to specified range bins, and central tendency, e.g., mean, median, mode, etc. Using multiple metrics and using corresponding criteria to evaluate each one and compare it to such thresholds can help improve reliability.

[0041] In some examples, the processor may determine multiple variations in the phase characteristic of the representation corresponding to multiple range bins.

[0042] In addition to detecting synchronous interference as noted above, this disclosure describes techniques for detecting asynchronous interference between a host transceiver unit and an interferer transceiver unit, such as when a first transceiver unit and a second transceiver unit have non-identical transmission parameters. A radar system, such as the radar system 102 of FIG. 2, can detect synchronous interference and / or implement techniques for detecting and mitigating asynchronous interference.

[0043] To detect asynchronous interference in the interference corrupted and combined signals, as described in more detail below, a processor such as processor 214 of Figure 2 can determine whether interference is present in a time-domain representation of the interference corrupted and combined signals, suppress samples corresponding to the interference to create a masked signal from a mask (M), and use the time-domain representation of the interference corrupted and combined signals (Y), and construct a corrected frequency-domain representation of the interference corrupted and combined signals using the mask (M). Thus, the asynchronous detection and mitigation technique can be considered a two-step process: (1) interference detection and masking of corrupted samples, and (2) recovery of the masked samples.

[0044] To detect asynchronous interference caused by a signal transmitted by a second transceiver unit of another radar system, a processor of the first radar system, such as processor 214 of Figure 2, can first determine whether interference is present in the received signal. For example, the processor can assign time intervals as exhibiting asynchronous interference based on the amplitude of a frequency domain representation of the combined signals corrupted by the interference, such as a short-time Fourier transform (STFT) of a chirp corrupted by the asynchronous interference, as described below with respect to Figures 8 and 9.

[0045] Figure 8 is a graph depicting the short-time Fourier transform (STFT) of a chirp corrupted by asynchronous interference. The x-axis represents time in microseconds (μs) and the frequency of the chirp in megahertz (MHz). Interference 800 from an interfering radar, such as a radar system located on the approaching vehicle 510 of Figure 5, has a V-shaped characteristic, as shown in Figure 8. Time interval 802 depicts where the interfering radar system corrupts some target, such as vehicle 504 of Figure 5.

[0046] To detect asynchronous interference caused by a signal transmitted by a second transceiver unit of another radar system, a processor of the first radar system, such as processor 214 of Figure 2, can determine the STFT of each chirp for any receive channel of the radar system. The processor can then detect the time interval where the interference corrupts the target, as shown in Figure 9.

[0047] Assume that the target is of interest at a location up to about 50 m from the host transceiver unit. The processor can use the fourth frequency range in Figure 9 to determine the time interval when the interfering radar will damage the target of interest.

[0048] 9 is a graph depicting the STFT of a chirp in a fourth frequency range corrupted by asynchronous interference. The x-axis represents time in microseconds. The magnitude of the STFT in the fourth frequency range is shown at 900. The detected mask based on thresholding the STFT values ​​is shown at 902, where a value of 1 represents no interference and a value of 0 represents detection of interference.

[0049] As discussed above, the processor can determine the time interval during which the interfering radar corrupted the target. For example, the processor can compare the amplitude of the STFT of the corrupted signal 900 to a reference. By way of example, the processor can compare the amplitude of the frequencies of the corrupted signal 900 to a central tendency, such as a multiple of the central tendency, e.g., twice the median amplitude.

[0050] 9, the amplitude of the corrupted signal 900 spikes above 100, which meets a criterion, such as meeting or exceeding twice the median amplitude, at approximately 60 μs. Based on the amplitude meeting the criterion, the processor can automatically detect the mask 902 at approximately 60 μs.

[0051] The graphs in Figures 8 and 9 represent one chirp. The processor can repeat the detection process for every chirp in the frame and then determine the mask, as shown in Figure 10.

[0052] Figure 10 is a graph depicting the time domain mask for one frame. The x-axis represents time in microseconds and the y-axis represents chirp number. As can be seen in Figure 10, the processor has determined a mask (M) 1000 for the chirps in the frame. The processor can then suppress samples corresponding to the interference 800 in Figure 8 and create a masked signal from the mask (M) in Figure 9.

[0053] Figures 11A and 11B are graphs depicting example frames without asynchronous interference (a "clean" signal). In Figure 11A, the x-axis represents speed in meters per second (m / s) and the y-axis represents distance in meters. In Figure 11B, the x-axis represents angle in degrees and the y-axis represents distance in meters.

[0054] In the example shown in Figures 11A and 11B, the signal-to-noise ratio (SNR) is 18.27 decibels (dB). Target 1100 is clearly shown in Figures 11A and 11B.

[0055] 2, radar system 102 may include two or more radar transceiver units 202A-202N, each having a receive channel, such as receive channel 211A of transceiver unit 202A. A processor may generate a range-velocity image for each receive channel (each channel having a different viewing angle) and then combine them to generate a range-angle image. In this manner, a processor may generate a cube of 3D radar images, where each slice is a range-velocity image.

[0056] 12A and 12B are graphs illustrating an example of a frame corrupted by asynchronous interference. In FIG. 12A, the x-axis represents the velocity in meters per second (m / s) and the y-axis represents the distance in meters. In FIG. 12B, the x-axis represents the angle in degrees and the y-axis represents the distance in meters. In the example shown in FIG. 12A and 12B, the SNR is 10.49 dB and the signal-to-interference ratio (SIR) is 8.233 dB. Some targets 1200 are unclear in FIG. 12A and 12B. In particular, the distance-velocity image in FIG. 12A is severely corrupted and the targets 1200 are barely visible beyond a distance of about 30 m below the interference floor.

[0057] 13A and 13B are graphs depicting frames corrupted by the asynchronous interference of FIG. 12A and FIG. 12B with a mask applied. In FIG. 13A, the x-axis represents speed in meters per second (m / s) and the y-axis represents distance in meters. In FIG. 13B, the x-axis represents angle in degrees and the y-axis represents distance in meters. In the example shown in FIG. 13A and FIG. 13B, the SNR is 9.216 dB and the signal-to-interference ratio (SIR) is 7.866 dB.

[0058] The masked signal shown in Figures 13A and 13B can be generated by a processor applying a mask, such as the mask 1000 of Figure 10, to a corrupted signal, such as in Figures 12A and 12B. For example, the processor can assign zero values ​​or weighted values ​​(e.g., reduced values, such as by a scaling factor) in the time domain to samples corresponding to interference. In this manner, the processor can suppress samples corresponding to interference to create a masked signal from the mask (M). However, as seen in Figures 13A and 13B, applying the mask can distort or blur, particularly the target 1300 in the range-angle image of Figure 13B. The SNR was reduced after applying the mask. The masking process can reduce the range resolution of the host radar system.

[0059] In a second step, once a particular time interval of the detected signal is masked, a processor can recover the missing or masked samples to restore the detected signal, thereby improving the range resolution. To recover the masked samples, a processor such as processor 214 of FIG. 2 can execute instructions to solve an optimization problem. There are two implementations: (1) a range-Doppler image-based implementation, and (2) a range-FFT implementation.

[0060] In a range-Doppler image based implementation, the processor may solve Equation 1 below:

[0061]

number

[0062] In the above formula, X *represents the recovered range-Doppler image (corrected frequency domain representation), M represents the two-dimensional (2D) mask, X represents the recovered range-Doppler image (corrected frequency domain representation of the masked candidate of the interference corrupted and combined signal), Y represents the interference corrupted and corrected time domain representation of the combined signal, and the last term, λ||X|| 1 is a sparsity regularizer term that enforces sparsity in the range-Doppler image; a sparse signal has many zero or near-zero values.

[0063] Using Equation 1, the processor can use the time domain representation of the combined signal corrupted by interference (Y) and the mask (M) to construct a corrected frequency domain representation of the combined signal corrupted by interference (X*).

[0064] To solve the optimization problem of Equation 1, the processor may use the detection steps described above to obtain the 2D mask M. The processor may then perform initialization.

[0065]

number

[0066] The processor can then perform the following iterations to recover X: for n = 1:N outer

[0067]

number

[0068] for m = 1:N inner

[0069]

number

[0070] β = β × 10 An example of the minimum value is N outer =2, N inner =2 is an example.

[0071] Below is an intuitive explanation of the equations that describe how the processor solves the optimization problem in Equation 1.

[0072] The first guess or initialization of the algorithm is the range-Doppler image, where corrupted time-domain samples are masked out (set to zero or otherwise de-weighted, etc.) and X 0 refers to the initial guess, M is a mask operating on the time-domain samples, and X* is the recovered range-Doppler image. The objective function in the optimization problem has two terms: 1. A data consistency term to ensure that when the processor performs the inverse 2D FFT of the recovered range-Doppler image (X), the uncorrupted samples must be similar to the measured time-domain samples (Y). The selection of the uncorrupted samples is done using a mask M. This term retains the uncorrupted samples. 2. A regularization term λ||X|| to ensure that the recovered range-Doppler image (X) is sparse. 1 This term aids in the recovery of corrupted samples that have been masked.

[0073] By solving Equation 1 to estimate the samples hidden by the mask, a balance is found between the sparsity of X* and its similarity to Y. This is controlled by the parameter λ, which can be kept constant across the dataset.

[0074] The technique for solving optimization problems is called "variable decomposition," which results in two variables, "Z" and "X," that can be solved iteratively. First, Z=X 0Z may be a sparse approximation of X obtained by setting small values ​​in X to zero and uniformly decreasing larger values. This process is called soft thresholding. The processor performs thresholding on the masked data X 0 We can compute the variable X by a weighted sum of Z and the current sparse approximation Z. As the processor performs further iterations, it becomes more confident in its estimate of Z, and the processor reverts to our initial guess, X. 0 A larger weighting β can be applied to Z than to

[0075] Since the sparsity is in the 2D FFT domain and the masking is in the time domain, the processor moves back and forth between the two domains. There is an inner loop and an outer loop. Once the processor has completed a few iterations of the inner loop, the processor can increase β in the outer loop. The elements of matrix Q can be determined by the current value of β and matrix M. Matrix M can be composed of zeros or deweighted values ​​(representing corrupted samples) and ones (representing uncorrupted samples). The variable Q adjusts the weighting coefficients applied to the time domain samples depending on whether they are zeroed or not by M.

[0076] The formulation of Equation 1 allows all range-Doppler images (alternatively called range-velocity images) to be solved at once. With this formulation, the processor must wait for all chirp returns to be measured before the process can recover an entire frame. The optimization problem can be solved iteratively, as shown above. However, a 2D FFT can be computationally intensive.

[0077] As noted above, in addition to the range-Doppler image-based implementation described above, a range-FFT implementation can be used. In a range-FFT implementation, a processor can solve Equation 2 below.

[0078]

number

[0079] In the above formula, x * represents the recovered distance-FFT signal (corrected frequency domain representation), m represents the one-dimensional (1D) mask, x represents the recovered distance-FFT signal (candidate corrected frequency domain representation of the interference corrupted and combined signals), y represents the time domain representation of the interference corrupted and combined signals, and the last term λ||x|| 1 is a sparsity regularizer term that enforces sparsity in the range-Doppler image; a sparse signal has many zero or near-zero values.

[0080] The formulation of Equation 2 allows each chirp to be solved independently. Therefore, the radar system does not need to wait for the entire data from a particular frame to be measured before performing processing. This formulation only requires a one-dimensional FFT. The sparsity assumption is for the range-FFT of each chirp. The processor can solve the optimization problem as in the range-Doppler image-based implementation described above, except that in Equation 2, the processor can now solve vectors instead of matrices and the 2D FFT is replaced with a 1D FFT.

[0081] With respect to computational complexity, in some examples, asynchronous detection and masking may utilize one STFT per chirp for any one receive channel. For interference mitigation, the computational complexity may depend on whether the radar system uses a range-Doppler image-based implementation or a range-FFT implementation.

[0082] In a range-Doppler image-based implementation (using Equation 1), the processor may use eight 2D FFTs per frame per receive channel. In contrast, a range-FFT implementation allows independent recovery of the chirp, does not need to process uncorrupted chirps, and has lower computational complexity (a 1D FFT is used).

[0083] As mentioned above, the processor can generate a range-velocity image for each receive channel (each channel having a different viewing angle) and then combine them to generate a range-angle image. To reduce the number of iterations required to solve the optimization problem of either Equation 1 or Equation 2, a processor of a radar system, such as processor 214 of FIG. 2, can initialize some of the receive channels using information from one or more of the other receive channels. In other words, the processor can use the solution to either the optimization problem of either Equation 1 or Equation 2 in one receive channel as an initial guess for one or more of the other receive channels, to which they correlate. That is, the processor can use the signal from the second receive channel to initialize a corrected frequency domain representation (X) of a candidate signal corrupted and combined by the interference of the first receive channel. In this manner, the solutions for those other receive channels can converge faster, thereby reducing the number of FFTs that the processor needs to perform.

[0084] Figures 14A and 14B are graphs depicting the frames corrupted by the asynchronous interference of Figures 12A and 12B after correction using 2D FFT techniques. In Figure 14A, the x-axis represents speed in meters per second (m / s) and the y-axis represents distance in meters. In Figure 14B, the x-axis represents angle in degrees and the y-axis represents distance in meters.

[0085] Using the technique described above with respect to Equation 1 and using a 2D FFT, the processor can recover the missing or masked samples to restore the detected signal, thereby improving the range resolution. As can be seen in Figures 14A and 14B, the resolution of the target 1400 is improved.

[0086] Figures 15A and 15B are graphs depicting the frames corrupted by the asynchronous interference of Figures 12A and 12B after correction using 1D FFT techniques. In Figure 15A, the x-axis represents speed in meters per second (m / s) and the y-axis represents distance in meters. In Figure 15B, the x-axis represents angle in degrees and the y-axis represents distance in meters.

[0087] Using the technique described above with respect to Equation 2 and a 1D FFT, the processor can recover the missing or masked samples to restore the detected signal, thereby improving the range resolution. As can be seen in Figures 15A and 15B, the resolution of the target 1500 is improved.

[0088] Various notes Each of the non-limiting aspects or examples described herein can be effective on its own or can be combined with one or more of the other examples in various permutations or combinations.

[0089] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of example, specific embodiments in which the invention may be practiced. These embodiments are also referred to herein as "examples." Such examples may include elements in addition to those shown or described. However, the inventors also contemplate examples in which only those elements shown or described are provided. Furthermore, the inventors also contemplate examples that use any combination or permutation of those elements shown or described (or one or more aspects thereof) with respect to a particular example (or one or more aspects thereof) or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0090] In the event of a conflict of usage between this document and any document incorporated by reference, the usage in this document takes precedence.

[0091] In this document, the terms "a" or "an" are used to include one or more than one, as is customary in patent documents, independently of any other instance or the use of "at least one" or "one or more." In this document, the term "or" is used to refer to a non-exclusive or, such that, unless otherwise specified, "A or B" includes "A but not B," "B but not A," and "A and B." In this document, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising," "including," and "wherein." Also, in the following claims, the terms "including" and "comprising" are open-ended, i.e., a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such terms in a claim will still be considered within the scope of that claim. Moreover, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels and are not intended to impose numerical requirements on their objects.

[0092] Examples of the methods described herein may be at least partially machine or computer implemented. Some examples may include computer-readable or machine-readable media encoded with instructions operable to configure an electronic device to perform the methods as described in the examples above. Implementations of such methods may include code, such as microcode, assembly language code, high-level language code, and the like. Such code may include computer-readable instructions for performing various methods. The code may form part of a computer program product. Further, in examples, the code may be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memory (RAM), read-only memory (ROM), and the like.

[0093] The above description is intended to be illustrative, not limiting. For example, the above examples (or one or more aspects thereof) can be used in combination with each other. Other embodiments can be used by those of ordinary skill in the art upon review of the above description. An Abstract is provided to comply with 37 CFR § 1.72(b) to allow the reader to quickly ascertain the nature of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to simplify the disclosure. This should not be construed as intending that an unclaimed disclosed feature is essential to any claim. Rather, the subject matter of the invention may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as an example or embodiment, with each claim standing on its own as a separate embodiment, and it is intended that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. [Explanation of symbols]

[0094] 100 vehicles 102 Radar System 104 Radar Transmitter / Receiver Unit 202A Radar Transmitter / Receiver Unit 202B Second radar transceiver unit 202N Radar Transmitter / Receiver Unit 204A Signal Generator 204B Signal Generator 204N Signal Generator 206A Mixer 208A Filter 208B Filter 208N Filter 210A Analog to Digital Converter, ADC 210N ADC 211A Receive Channel 212 Computer Systems 214 processor 216 Memory, memory devices 218 Command 220 Sensor System 222 Inertial Measurement Unit, IMU 224 Global Positioning System, GPS 226 Camera 300 images 302 Goal 304 images 306 Goal 308 Linear Artifacts 400 images 402 Goal 404 images 406 Goal 500 host vehicles 502 First Signal 504 Target, Vehicle 506 Echo Signal 508 Second Signal 510 Third Car 600 Baseline Signal 602 Broken Signal 700 True Goal 702 False Target 800 Interference, boundary surface 802 Time Interval 900 Broken Signal 902 Mask 1000 Mask, M 1100 goals 1200 goals 1300 goals 1400 goals 1500 goal

Claims

1. 1. A radar system for mitigating interference caused by a signal transmitted by a second transceiver unit of another radar system, the radar system having a first transceiver unit, the first transceiver unit comprising: Transmitting a first signal toward the target; receiving a combined signal corrupted by interference, the combined signal including an echo signal from the target in response to the transmitted first signal and a second signal transmitted by the second transceiver unit, the first transceiver unit and the second transceiver unit having corresponding transmission parameters; a first transceiver unit for a processor for detecting synchronous interference in the interference corrupted combined signal, determining a frequency domain representation of the combined, corrupted signals; determining a variance in a phase characteristic of said representation corresponding to a specified range bin; assigning the designated range bins as exhibiting synchronous interference based on the variance. With processor for A radar system comprising:

2. to assign the designated range bin as exhibiting synchronous interference based on the variance, the processor further comprising: The radar system of claim 1 , further comprising: comparing the variability to a threshold value.

3. The processor further comprises: The radar system of claim 1 , further comprising: determining a plurality of metrics of the phase characteristics of the representation corresponding to designated range bins.

4. The processor further comprises:

10. The radar system of claim 1, further comprising: determining a plurality of variations in the phase characteristic of the representation corresponding to a plurality of range bins.

5. The radar system of claim 1 , wherein the variability comprises a standard deviation.

6. 10. The radar system of claim 1, wherein the frequency domain representation comprises a discrete Fourier transform representation (FFT).

7. 1. A radar system for mitigating interference caused by a signal transmitted by a second transceiver unit of another radar system, the radar system having a first transceiver unit, the first transceiver unit comprising: Transmitting a first signal toward the target; receiving a combined signal corrupted by interference, the combined signal including an echo signal from the target in response to the transmitted first signal and a second signal transmitted by the second transceiver unit; a first transceiver unit for a processor for detecting synchronous interference in the combined signal, determining a frequency domain representation of the combined signal; determining a variance in a phase characteristic of said representation corresponding to a specified range bin; assigning the designated range bins as exhibiting synchronous interference based on the variance. It is a processor for The processor for mitigating asynchronous interference in the interference corrupted combined signal, determining whether interference is present in a time domain representation of the combined signal corrupted by the interference; suppressing samples corresponding to said interference to create a masked signal from a mask (M); using said time domain representation (Y) of said interference corrupted and combined signals and constructing a corrected frequency domain representation (X*) of said interference corrupted and combined signals using said mask (M); The processor for A radar system comprising:

8. to assign the designated range bin as exhibiting synchronous interference based on the variance, the processor further comprising: The radar system of claim 7 , further comprising: comparing the variability to a threshold value.

9. The radar system of claim 7 , wherein the variability comprises a standard deviation.

10. To determine whether the interference is present in the time domain representation of the combined signal, the processor further comprises:

8. The radar system of claim 7, further comprising: assigning time intervals as exhibiting asynchronous interference based on an amplitude of the frequency domain representation of the combined signal.

11. To suppress samples corresponding to the interference to create a masked signal, the processor further comprises:

8. The radar system of claim 7, further comprising: assigning the samples corresponding to the interference a zero value or a reduced weighting value in the time domain.

12. using the time domain representation (Y) of the combined signals corrupted by interference and using the mask (M) to construct a corrected frequency domain representation (X*) of the combined signals corrupted by interference, the processor further comprising:

8. The radar system of claim 7, further comprising: applying a sparsity regularizer to minimize a difference between a corrected frequency domain representation (X) of the masked candidate of the interference corrupted combined signal and a corrected time domain representation (Y) of the masked interference corrupted combined signal.

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