Patent Application
20250076452
The present disclosure is directed in general to radar systems and associated methods of operation. In one aspect, the present disclosure relates to a radar system configured to mitigate radar signal interference using generative networks.
A radar system transmits an electromagnetic signal and receives back reflections of the transmitted signal. The time delay and/or time delay variation between the transmitted and received signals can be determined and used to calculate the distance and/or the speed of objects causing the reflections, respectively. For example, in automotive applications, automotive radar systems can be used to determine the distance and/or the speed of oncoming vehicles and other obstacles.
Automotive radar systems enable the implementation of advanced driver-assistance system (ADAS) functions that are likely to enable increasingly safe driving and, eventually, fully autonomous driving platforms. Such systems use radar systems as the primary sensor for ADAS operations.
In a particular environment, the accuracy of automotive radar systems can be affected by signals from other radars (also known as radar-to-radar interference), which can reduce the signal-to-noise ratio in received radar reflection signals.
A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following figures, wherein like reference numbers refer to similar elements throughout the figures.
The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter of the application and uses of such embodiments. As used herein, the words “exemplary” and “example” mean “serving as an example, instance, or illustration.” Any implementation or embodiment described herein as exemplary, or an example is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or the following detailed description.
Automotive radar systems are used to support advanced driver-assistance system (ADAS) functions like assisted cruise control, emergency braking, and blind spot monitoring and alerts. The efficacy of a radar system is at least partially determined by an amount of signal interference (e.g., signal noise) contained within the reflected radar signals received and processed by the radar system. As the level of interference increases, the signal-to-noise ratio (SNR) in the received reflected signals is reduced, reducing the accuracy of the radar system's determinations.
Interference in automotive radar system applications is generally a growing problem that may impede the development of autonomous vehicles or other vehicles that rely on ADAS that utilize radar systems. As more and more vehicles are equipped with radar systems, this interference problem is expected to increase, potentially exponentially.
There are some existing approaches for mitigating the interference problem in vehicle radar systems, however these tends to rely on manually crafted approaches, such as sparsity-based techniques, or implicitly learned assumptions (e.g., using convolutional neural networks (CNN)) about the interfering and victim radar parameters. Consequently, these approaches rely on certain presumptions that make them less effective in real-world applications in various scenarios.
An improved approach for mitigating signal interference is presented herein, in which generative networks are trained via explicit learning to accurately reconstruct interference signals, which can then be removed from an input signal to determine the desired portion of the input signal.
In various embodiments, the present disclosure provides an approach that provides an interference mitigation solution the utilizes a generative network trained on a wide spectrum of interfering and/or victim radar configurations to determine parameters associated with interference signals in a particular scenario. The approach can be robust, as the generative network can be trained on a large number of interference scenarios, which can include scenarios with different signal-to-interference ratios (SIR), signal-to-noise ratios (SNR), and the presence of an arbitrary number of interferer or target operational radar systems. Using trained generative networks, the approach can provide accurate interference reconstruction, which can assist a radar system in removing interference from received radar systems thereby improving the radar system's capability of detecting weak target objects, even in the presence of strong Gaussian noises.
To illustrate the design and operation of a radar system configured in accordance with the present disclosure, reference is now made to
Each radar device 10 includes one or more transmitting antenna elements TXi and receiving antenna elements RXj connected, respectively, to one or more radio frequency (RF) transmitter (TX) units 11 and receiver (RX) units 12. For example, each radar device (e.g., 10) is shown as including individual antenna elements (e.g., TX1,i, RX1,j) connected, respectively, to three transmitter modules (e.g., 11) and four receiver modules (e.g., 12), but these numbers are not limiting and other numbers are also possible, such as four transmitter modules 11 and six receiver modules 12, or a single transmitter module 11 and/or a single receiver modules 12.
Each radar device 10 also includes a chirp generator 112 that is configured and connected to supply a chirp input signal to the transmitter modules 11. To this end, the chirp generator 112 is connected to receive a separate and independent local oscillator (LO) signal 110 and a chirp start trigger signal 111, though delays are likely to be different due to the signal path differences and programmable digital delay elements in the signal paths. Chirp signals 113 are generated and transmitted to multiple transmitters 11, usually following a pre-defined transmission schedule, where they are filtered at the RF conditioning module 114 and amplified at the power amplifier 115 before being fed to the corresponding transmit antenna TX1,i and radiated. By sequentially using each transmit antenna TX1,i to transmit successive pulses in the chirp signal 113, each transmitter element 11 operates in a time-multiplexed fashion in relation to other transmitter elements because they are programmed to transmit identical waveforms on a temporally separated schedule.
The radar signal transmitted by the transmitter antenna unit TX1,i, TX2,i may be reflected by an object, and part of the reflected radar signal reaches the receiver antenna units RX1,i at the radar device 10. At each receiver module 12, the received (radio frequency) antenna signal is amplified by a low noise amplifier (LNA) 120 and then fed to a mixer 121 where it is mixed with the transmitted chirp signal generated by the RF conditioning module 114. Specifically, mixer 121 is configured to down-convert the RF signal to a baseband signal, meaning that during an acquisition window the target signals result into a superposition of constant-amplitude sinusoidal signals while the interference signal results in a broadband chirp-like signal (i.e., that is time-limited, after anti-aliasing filtering). The resulting intermediate frequency signal is fed to a first high-pass filter (HPF) 122. The resulting filtered signal is fed to a first variable gain amplifier 123 which amplifies the signal before feeding it to a first low pass filter (LPF) 124. This re-filtered signal is fed to an analog/digital converter (ADC) 125 and is output by each receiver module 12 as a digital signal D1. In various embodiments, the receiver module compresses target echoes of various delay signals into multiple sinusoidal tones whose frequencies correspond to the round-trip delay of the echo.
The radar system 100 also includes a radar controller processing unit 20 that is connected to supply input control signals to the radar device 10 and to receive therefrom digital output signals generated by the receiver modules 12.
In selected embodiments, the radar controller processing unit 20 may be embodied as a micro-controller unit (MCU) or other processing unit that is configured and arranged for signal processing tasks such as, but not limited to, target identification, computation of target distance, target velocity, and target direction, and generating control signals. The radar controller processing unit 20 may, for example, be configured to generate calibration signals, receive data signals, receive sensor signals, generate frequency spectrum shaping signals (such as ramp generation in the case of frequency-modulated continuous wave (FMCW) radar) and/or register programming or state machine signals for RF (radio frequency) circuit enablement sequences.
In addition, the radar controller processor 20 may be configured to program the modules 11 to operate in a time-division fashion by sequentially transmitting LFM chirps for coordinated communication between the transmit antennas TX1,i, RX1,j. The result of the digital processing at the radar controller processing unit 20 is that the digital domain signals D1 are first processed by interference cancellation block 19 to remove interference and noise from the signal D1 received from receiver module 12. That processed signal is then passed to fast-time range frequency Fourier transform (FFT) 21, slow-time FFT 22 for Doppler compression, constant false alarm rate (CFAR) target detection 23, spatial angle estimation 24, and target tracking processes 25, with the result being output 26 to other automotive computing or user interfacing devices for further process or display. When performing target detection, the radar controller processor 20 is configured to determine a range profile of the reflected radar signals, where peaks in the range profile are indicative of potential targets at known ranges.
During operation of automotive radar system 100, radar controller processor 20 transmits control signals to the various components of one or more radio frequency (RF) transmitter (TX) units 11 and receiver (RX) units 12 to control how the radar signals transmitted by automotive radar system 100 are the transmitted signals are shaped and how the received signals are processed. In a specific example, radar controller processor 20 is configured to modulate the power level of signals transmitted by the one or more radio frequency (RF) transmitter (TX) units 11 by transmitting control signal to power amplifier 115 of each of the one or more radio frequency (RF) transmitter (TX) units 11.
In accordance with the present disclosure, interference cancellation 19 as implemented by radar controller processor 20 is configured to provide improved interference mitigation. In a particular vehicle radar scenario, when other radar systems are present in the environment, the signals transmitted by those other radar systems can interfere with the signals being transmitted and/or received by a first radar system (e.g., automotive radar system 100 of
Specifically,
The remaining interfering signal after filtering is further processed and tends to corrupt a portion of the ADC samples generated by the signal processing chain (e.g., signal D1 of
The example radar system 100 described above and the various examples of interference are presented in conjunction with a radar system 100 configured as an FMCW radar system. It should be understood that embodiments of the present system may be utilized in conjunction with the signals being received and processed by other types of radar systems to remove interference therefrom and increase the accuracy of such radar systems. For example, the radar signals processed in conjunction with the present system may be received by radar systems configured to process signals encoded by bi-phase modulated continuous wave (PMCW), orthogonal frequency division multiplexing (OFDM), or orthogonal time frequency space (OTFS) encoded scheme or any other types of radar systems configured to processed received radar signals and process those signals to identify attributes of target objects in the vicinity (i.e., with a field of vision (FOV)) of the radar system based upon that processing.
The present disclosure provides an approach for mitigating interference in radar systems in which interference is modeled as a source separation optimization problem. The approach uses posterior estimation and/or maximization combined with deep generative modeling (e.g., via generative score networks, diffusion models, or normalizing flows), which determines statistical characteristics of interfering signals and/or desired signal (source) to retrieve an accurate reconstruction of the desired (source) signal. When implementing the present approach, the prior beliefs associated with the desired signal and interfering signal are learned independently (e.g., in an offline process) in the time domain or transformed domains (such as short-time Fourier transform (STFT), wavelet, etc.). Furthermore, priors can be learned for complex data.
In some embodiments, different optimization schemes can be employed to solve posterior maximizations (e.g., (Fast) ISTA, ADMM, or AMP). Although the approaches described herein may operate on individual chirp signals, enabling low latency operation, the approaches may also be extended to data from consecutive chirps and signals captured by multiple receive antennas. The approaches can also be applied to different radar systems using different signal encoding schemes, as described above.
In the present disclosure, the digital signal (y) output by the ADC of a vehicle radar system's signal processing chain (e.g., signal D1 generated by ADC 125 of automotive radar system 100 of
Additionally, n∈Rl denotes an additive Gaussian white noise contained within the signal y. Noise n generally has zero mean and standard deviation σn.
As described above, the presence of aggressive interference xi in the signal y can deleteriously affect the detection of the desired signal xs. The present disclosure provides approaches for determining or estimating the interference signal xi and the desired signal xs from y
Without prior knowledge of the signal structure and statistical data characterizing xs and xi, the determination of the signal xs can be a poorly-condition problem. As described herein, however, this can be addressed via posterior estimation or maximization. Such posterior estimation or maximization processes can incorporate or utilize statistical priors to ensure that such reconstructions are both physically feasible (i.e., data-consistent) and plausible (i.e., consistent with prior belief).
Specifically, presuming that xs and xi follow distinct data distributions (e.g., have a particular data distribution in the time domain or another transformed domain) that are generally independent from one another (which is generally the case for FMCW, PMCW, OFDM, and OTFS interference signals), xs and xi can be formulated as:
In equation (2), p(xs,xi|y) represents the joint conditional posterior distribution, p(y|xs, xi) is a measurement likelihood model, described in more detail herein, and pθ1(f1(xs)) and pθ2(f2(xi)) are the desired signal and interference priors that define the data distribution expected in both the desired and interference signals, which utilize explicit modeling, as described herein. The interference prior pθ2(f2(xi)) can be generated using deep generative networks.
Within equation (2), f1() and f2() indicate generic signal transformations that may be selected based upon attributes of the signals being processed. Typically, the transformations are selected such that functions f1() and f2() provide improved modeling of the signal of interest and/or better separation between the signal of interest and the interference signal. In general, the transformations f1() and f2 enable improved characterization of the desired and interference signals and are selected depending upon the signal type being processed. Example transformations are illustrated in Table 1, below.
According to Table 1, the data format of the transformed data f1(xs) and f2(xi) may vary or be selected based on application and depending on the transformation that was used.
In the present disclosure, a two-stage approach to solve Equation (2) to determine the interference signal xi and, consequently, the desired signal xs is presented. 32 As depicted in
The first signal input xs is provided to a generative modeling block 606 after being processed through an initial transformation function f1() Generative modeling block 606 is configured to retrieve a desired signal prior pθ1(f1(xs)) (generated via an offline modeling process 601 described in more detail herein) and generate an output equal to an estimated desired signal. That output is converted back into its original data format via the function f1−1() (i.e., the inverse of f1() and passed to input 610 of data consistency block 612.
Similarly, the second signal input xi0 is provided to generative modeling block 608 after being processed through an initial transformation function f2() Generative modeling block 608 is configured to retrieve an interference signal prior pθ2(f2(xi)) (generated via offline modeling process 601 described in more detail herein) and generate an output equal to an estimated interference signal. That output is converted back into its original data format via the function f2−1() (i.e., the inverse of f2() and passed to input 614 of data consistency block 612. Data consistency block 612 operates a data consistency operation configured to align the signal estimates outputted by data consistency block 612 after iteration with the observation signal y. This enables the estimate of the desired signal and the interference signal generated by system 600 to be guided into the right direction of the observed data after every new estimate/iteration. The modified outputs of data consistency block 612 {circumflex over (x)}sk and {circumflex over (x)}lk are fed back into generative modeling block 606 and generative modeling block 608, respectively, enabling the method to iterate thereby refining the estimated desired signal and interference signal outputs. At the same time, the outputs of data consistency block 612 {circumflex over (x)}sk and {circumflex over (x)}lk(which are being continuously refined) are generated as outputs of system 600 at outputs 616, 618, respectively. An example of the output signal {circumflex over (x)}sk, which is the estimate of the desired signal component, is illustrated by waveform 605. An example of the output signal {circumflex over (x)}lk, which is the estimate of the interference signal component, is illustrated by waveform 607. The feedback loop illustrated in
In various embodiments, the functional components of system 600 may be implemented by any suitably configured device processor, such as radar controller processor 20 of automotive radar system 100 of
Although not shown in
Once determined, the recovered interference signal (i.e., {circumflex over (x)}lk) can be subtracted from the noisy observations of signal y to determine the desired signal {circumflex over (x)}sk to identify even weak target object signals within the FOV of the radar system that may be buried under the noise floor that may be recovered via downstream signal aggregation. That is:
Consequently, the problem can therefore be interpreted as the accurate reconstruction of the interference from noise-contaminated observations, potentially containing the desired signal of various strengths. In one case, the direct estimate {circumflex over (x)}i could be taken as the output of system 600.
In some embodiments of the present system, a solution to Equation (2) is provided that is based upon the structured nature of the interference signal in a magnitude time-frequency domain signal. To summarize, initially, a transformation (e.g., an STFT) is applied to the observed time-series signal (e.g., signal D1 of
In equation (4), f1 denotes a Fourier transformation and f2 represents a STFT.
When implementing the present approach, in an initial step a preliminary offline stage is implemented wherein a generative score network is used to determine interference signal prior pθ2. Generative score networks are a class of generative networks that rely on modeling the gradient of the log-probability distribution of samples ∇xlogpθ(x) (i.e., the score function) rather than directly modeling priors pθ. This can allow for greater flexibility in the network architecture design. In some embodiments, the generative score networks can be generated using a U-NET based network architecture in which the input dimension of the network matches the output dimension of the network. One common approach to train score-based networks is through denoising score matching, as follows:
Equation (5) is a general loss function that can be used to optimize the score networks. Below, we explain that in this specific implementation, it is done separately for the targets-only signals and the interference-only signals, possibly each in a transformed domain.
This involves perturbing the interference samples (e.g., the targets-only or interference- only signals) with additive Gaussian noise having a range of standard deviations. The generative network is then trained to estimate the gradients of these perturbed samples by implicitly learning how to map back to clean data samples (e.g., data samples that have not been perturbed by the Gaussian noise function). Drawing new samples from the resulting learned probability distribution is done in various ways, for instance, via iterative gradient-ascent-based algorithms.
When operating the generative score network to determine priors pθ2, the generative network can be trained using sample data generated, for example, by putting the radar receiver into a listening-only mode and enabling the radar system to record signals received via the RX receiver unit (e.g., RX unit 12 of
With the interference signal prior pθ2 determined, in the second, inference, stage a posterior maximization process is used to estimate the interfering and desired signal. At each step or iteration of that process, a data consistency step is executed so that the estimated signals adhere to the inputs (observations) of the received signal y. At the same time, the values in the estimated interference and desired signals are iteratively nudged towards the target data distributions, as indicated by the determined priors pθ2. As this step is iterated, the estimates of the interference signal and the desired signal become more accurate until satisfactory (and in some cases, optimal) estimates are generated, as described below. In a specific embodiment, this involves solving equation (4) using an iterative shrinkage-thresholding algorithms (ISTA) optimizer. In such an optimization process involves alternating between the gradient ascent and data consistency steps of the ISTA optimizer, starting with initializing both the desired signal and interference signal as the observation in the time domain. The learned score function sθ(|f2(xi)|) is then used for approximating ∇|f2(x2)|logpθ
In Equation (6), the ratio λl/σik determines the regularization level during the k-th iteration. Subsequently, data consistency is performed on the estimated interference again in the magnitude time-frequency domain:
In Equation (7), f1−1(.) denotes the inverse Fourier transformation (i.e., the opposite function of f1() and μ1 tunes the data consistency level, determining how much the estimated interference should adhere to the observation in the magnitude time-frequency domain. The estimated interference in the magnitude time-frequency domain is transformed back to the time domain via:
In Equation (8), f2−() denotes the inverse of function f2() (i.e., the inverse STFT), and ∠Ĉik−1 is the phase component of the time-frequency representation of the estimated interference from the last iteration.
The estimated desired signal is then estimated by a proximal step that includes soft thresholding in the Fourier domain. That estimated signal is then transformed back to the time domain using f1−1. A data consistency step is then performed in the time domain:
In Equation (9), Γλ
In equation (10), the parameter μ3 tunes the data consistency level, determining how much the individually estimated interference and estimated target signal should adhere to the observation in the time domain.
After the algorithm converges, it is possible to obtain the estimated time series interference signal {circumflex over (x)}i. The algorithm may be determined to have converged after a fixed number of total iterations. In that case, the fixed number of total iterations can be at least partially determined by model hyperparameter tuning. Alternatively, as the algorithm runs until a particular stopping condition is satisfied, at which time the output of the model is considered the final output. An example stopping criteria may be that the magnitude of the change in outputs of the model from one iteration to the next fall below some predetermined threshold. At that time is can be determined that the algorithm has converged, and the estimated interference signal can then be subtracted from the observation signal y in the same domain to obtain the estimated desired signal with the potential environmental noises, typically Gaussian, accompanied.
In one implementation of this approach for interference mitigation, a system was trained using a simulated ADC (time-series) dataset, which was randomly split into a training set of 40,000 samples, a validation set of 6,000 samples, and a test set of 500 samples. The key parameters used to generate each sample are listed in Table 2, below.
In Table 2, the first column represents key parameters for the simulated dataset generalization. The symbol * denotes a value that is randomly selected from a uniform distribution.
Using the training data, score-based networks are first pre-trained on magnitude time-frequency data. The data can be obtained by applying STFT to the time-series training samples in the original training data set. During the pre-training (i.e., offline) process, the network parameters are optimized using denoising scoring approaches, such as those illustrated by equation (5), above. This aims to minimize the empirical average of the Euclidean distance between the score of the training samples and the gradients of the added zero mean Gaussian noises with varying noise levels.
Following pre-training, the learned generative score prior is integrated into the ISTA algorithm, as described above. Hyperparameters, including regularization terms, data consistency terms, and the number of total iterations, are then fine-tuned based on a subset of time-series validation samples. Bayesian optimization can be used to fine-tune the hyperparameters, with the objective of minimizing the difference between the reconstructed and ground-truth interference in both the time-frequency and time domains.
This implementation is then compared against other approaches of interference calculation, which includes conventional zeroing methodologies in which interference is first detected and then canceled out through a conventional thresholding technique. Gaps caused by cancellations can then be filled with zeros. Comparison against two such zeroing methods including time-domain zeroing (TDZ) and time-frequency domain zeroing (TFDZ) is provided. Another interference mitigation approach, referred to as the ‘sparse prior’ approach, operates under the assumption that the desired signal and interference signal are sparse (e.g., containing greater than 80% zero values or a greater percentage of zero values) in alternative domains. These other approaches use an optimizer based on the Alternating Direction Method of Multipliers (ADMM).
The present algorithm evaluated the baselines on the test samples within the range spectrum, utilizing evaluation metrics as the ratio of the target peaks to the strongest sidelobe in the decibel (dB) scale. Additionally, various Gaussian noises with zero mean and standard deviation σn were introduced to assess the robustness of the present approach against environmental noises. The quantitative results of the ratio of the target peaks to the strongest sidelobe are shown in Table 3, below.
In Table 3, the first column represents a noise level on in the original signal samples being processed. The second column represents the ratio of the target peaks (e.g., the signal-to-noise ration) for the processed signal using the TDZ interference mitigation approach. The third column shows the same data for the TFDZ interference mitigation. The fourth column shows the same data for the sparse prior approach to interference mitigation, while the fifth column shows the results for the present method.
As illustrated by Table 3, across all noise levels, the ratio of target peaks, or the SNR, is greatest (indicating better performance) with the present method by a significant margin.
In another embodiment, equation (2) is solved using posterior sampling with generative score-based diffusion models. This approach expands upon the approach described above, in which the input data is perturbed with a discrete or finite number of noise levels (e.g., determined by injecting Gaussian noise with different discrete standard deviations) to an approach that uses an infinite number of noise levels. Specifically, the approach defines a diffusion process, denoted as {xt}t=0T, which is indexed by a continuous time variable t∈[0, 1]. This process starts from a clean sample x0 drawn from a distribution p(x0) at t=0. This is subsequently formulated using stochastic differential equations (SDE). Consequently, the data perturbation process leveraging the forward SDE can therefore be written as follows:
In equation (11), w is the standard wiener process, and f(t) is the drift coefficient, representing the deterministic evolution of xt at each time t, while g(t) denotes the diffusion coefficient, quantifying the level of stochasticity introduced at each time t. This process assists in mapping the data distribution p(x0) to a pre-defined base distribution p(xT), from which samples can be generated efficiently. As a result, it is feasible to generate samples from the data distribution p(x0) by reversing the perturbation (diffusion) process. This can be achieved through the use of a reverse-time SDE. The formulation is as follows:
In equation (12), the score, representing the gradient of the log probability density of target data samples, is important in the reverse diffusion process. Functioning as a vector field, the score points at the high likelihood regions within the data distribution, thus guiding the evolution of xt, from, for example, random noises to novel data samples.
The score function can be learned utilizing generative score-based networks via denoising score matching, as in equation (5), described above. Unlike the discrete generative score-based network in the first embodiment, here a time-dependent generative score-based network can be denoted as sθ(x,t)≈∇x
In this approach, leveraging posterior sampling to sample desired signals from the joint conditional posterior distribution of equation (2) by integrating the core concepts of diffusion process can be an effective strategy for interference mitigation. To sample from the posterior p(xst, xit|y), the joint conditional reverse-time diffusion SDE is defined as follows:
The joint conditional reverse-time SDE of equation (13), expanded from equation (12), can be efficiently calculated using gradients of the posteriors. However, to accomplish this, it is necessary to factorize the expression equation 13 into two individual score models while both models remain connected via the data consistency term. The gradient of each posterior can be obtained from the following mathematical expression:
In equation (14), sθ
In the first of the two steps, for radar signal applications, the specific desired signal score function sθ
Gaussian noise added throughout the forward diffusion process, as defined in (11), can equal xt=αt x0+βt w at every timestep tαtβt and corresponds to scalars defined by the selected SDE process. Therefore, the summation of two random variables results in the convolution of the two independent distributions, namely the Laplace distribution p(αtx0) and the (complex) Normal distribution p(βtw): p(αtx0)*p(βtw). It can be analytically derived that, under the assumption of Gaussian noise, the proximal operator for the maximum a posteriori probability (MAP) estimator of a signal that follows sθ
Utilizing the continuous generative score-based network sθ
Typically, unlike p(y|x0i,x0s), the likelihood term p(y|xti,xts) is intractable. Hence, it is possible to adopt the approach proposed in equation (11) to formulate the perturbed data likelihood term as follows:
In equation (15), μt=xs0|t+xi0|t and Σ=(rt)2I+(qt)2I, where rt and qt define the standard deviation at timestep t. Moreover, the term xi0|t (the same process for xs0|t) can be formulated employing Tweedie's formula:
It is then possible to derive the score function of equation (15) and perform the data consistency steps as follows:
Referring to
With the interference and desired signal priors determined, at step 740, the processor is configured to execute an iterative procedure using the desired signal prior pθ1 and the interference signal prior pθ2 to determine an estimate of the interference signal component xi and an estimate of the desired signal component xs. Each iteration of the iterative procedure executes a data consistency operation to increase a data consistency attribute between the estimate of the interference signal component xi and the estimate of the desired signal component xs. Then, at step 750, a desired signal is determined using the estimate of desired signal component xs and at step 760, the desired signal is processed to determine an attribute (e.g., a range, distance, velocity, angle) of a target object in a field of vision of an automotive radar system.
In some aspects, the techniques described herein relate to an automotive radar system, including: at least one transmitter and at least one receiver, wherein the at least one transmitter and the at least one receiver are configured to transmit and receive radar signals, wherein the at least one transmitter and the at least one receiver are coupled to a vehicle; and an automotive radar processor, configured to: transmit, at a first time, a first radar signal, receive, using the at least one receiver, a received signal y, wherein the received signal y is of the form y=xs+xi+n, wherein xs represents a desired signal component, xi represents an interference signal component, and n represents a noise component of the received signal y, determine a desired signal prior pθ1, wherein the desired signal prior pθ1 is determined using generative modeling, determine an interference signal prior pθ2, wherein the interference signal prior pθ2 is determined using generative modeling, execute an iterative procedure using the desired signal prior pθ1 and the interference signal prior pθ2 and to determine an estimate of the interference signal component xi and an estimate of the desired signal component xs, wherein each iteration of the iterative procedure a data consistency operation to increase a data consistency attribute between the received signal y, the estimate of the interference signal component xi and the estimate of the desired signal component xs, and determine a desired signal using the estimate of desired signal component xs, and process the desired signal to determine an attribute of a target object in a field of vision of the automotive radar system.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the automotive radar processor is configured to determine the desired signal by subtracting the estimate of the interference signal component xi from the received signal y to determine the desired signal.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the interference signal prior pθ2 is determined by performing denoising score matching on a training data set, wherein the training data set includes simulated time-domain interference signals.
In some aspects, the techniques described herein relate to an automotive radar system, wherein values in the training data set are modified by adding Gaussian noise values to the values in the training data set to generate a modified training data set, wherein Gaussian noise values added to the values in the training data set have a range of standard deviations.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the generative modeling is configured to estimate gradients of the values in the modified training data set by mapping the values in the modified training data set to corresponding values in the training data set.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the Gaussian noise values are determined by a finite number of noise levels.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the Gaussian noise values are determined according to a diffusion process, wherein the diffusion process is defined using a stochastic differential equation.
In some aspects, the techniques described herein relate to an automotive radar system, wherein the received signal includes a signal encoded as a frequency-modulated continuous wave radar signal.
In some aspects, the techniques described herein relate to a system, including: a transmitter configured to transmit a radar signal towards a target object; a receiver configured to receive a received signal in response to the transmitted radar signal; and a processor, configured to: receive, using the receiver, a received signal, execute an iterative procedure using a desired signal prior and an interference signal prior and to determine an estimate of an interference signal component of the received signal and an estimate of the desired signal component of the received signal, wherein the interference signal prior is determined using generative modeling and each iteration of the iterative procedure executes a data consistency operation, use the estimate of the interference signal component and the received signal to determine a desired signal, and processing the desired signal to determine an attribute of the target object.
In some aspects, the techniques described herein relate to a system, wherein the desired signal prior is a sparse prior.
In some aspects, the techniques described herein relate to a system, wherein the interference signal prior is determined by performing denoising score matching on a training data set, wherein the training data set includes at least one of simulated time-domain interference signals and measured time-domain interference signals.
In some aspects, the techniques described herein relate to a system, wherein values in the training data set are modified by adding Gaussian noise values to the values in the training data set to generate a modified training data set, wherein Gaussian noise values added to the values in the training data set have a range of standard deviations.
In some aspects, the techniques described herein relate to a system, wherein the generative modeling is configured to estimate gradients of the values in the modified training data set by mapping the values in the modified training data set to corresponding values in the training data set.
In some aspects, the techniques described herein relate to a system, wherein the Gaussian noise values are determined by a finite number of noise levels.
In some aspects, the techniques described herein relate to a system, wherein the Gaussian noise values are determined according to a diffusion process, wherein the diffusion process is defined using a stochastic differential equation.
In some aspects, the techniques described herein relate to a system, wherein the received signal includes a signal encoded as a frequency-modulated continuous wave radar signal.
In some aspects, the techniques described herein relate to a method, including: receiving a received signal y, wherein the received signal y is of the form y=xs+xi+n, wherein xs represents a desired signal component, xi represents an interference signal component, and n represents a noise component of the received signal y; determining a desired signal prior pθ1, wherein the desired signal prior pθ1 is determined using a generative network; determining an interference signal prior pθ2, wherein the interference signal prior pθ2 is determined using generative modeling, executing an iterative procedure using the desired signal prior pθ1 and the interference signal prior pθ2 and to determine an estimate of the interference signal component xi and an estimate of the desired signal component xs, wherein each iteration of the iterative procedure executes a data consistency operation to increase a data consistency attribute between the estimate of the interference signal component xi and the estimate of the desired signal component xs, determining a desired signal using the estimate of desired signal component xs, and processing the desired signal to determine an attribute of a target object in a field of vision of an automotive radar system.
In some aspects, the techniques described herein relate to a method, wherein determining the desired signal further includes subtracting the estimate of the interference signal component xi from the received signal y to determine the desired signal.
In some aspects, the techniques described herein relate to a method, further including determining the interference signal prior pθ2 by performing denoising score matching on a training data set, wherein the training data set includes at least one of simulated time-domain interference signals and measured time-domain interference signals.
In some aspects, the techniques described herein relate to a method, further including modifying values in the training data set by adding Gaussian noise values to the values in the training data set to generate a modified training data set.
Although the examples have been described with reference to automotive radar systems, the systems and methods described herein may be implemented in conjunction with other types of radar systems.
The preceding detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments.
As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or detailed description.
The connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the subject matter. In addition, certain terminology may also be used herein for the purpose of reference only, and thus are not intended to be limiting, and the terms “first,” “second” and other such numerical terms referring to structures do not imply a sequence or order unless clearly indicated by the context.
As used herein, a “node” means any internal or external reference point, connection point, junction, signal line, conductive element, or the like, at which a given signal, logic level, voltage, data pattern, current, or quantity is present. Furthermore, two or more nodes may be realized by one physical element (and two or more signals can be multiplexed, modulated, or otherwise distinguished even though received or output at a common node).
The foregoing description refers to elements or nodes or features being “connected” or “coupled” together. As used herein, unless expressly stated otherwise, “connected” means that one element is directly joined to (or directly communicates with) another element, and not necessarily mechanically. Likewise, unless expressly stated otherwise, “coupled” means that one element is directly or indirectly joined to (or directly or indirectly communicates with, electrically or otherwise) another element, and not necessarily mechanically. Thus, although the schematic shown in the figures depict one exemplary arrangement of elements, additional intervening elements, devices, features, or components may be present in an embodiment of the depicted subject matter.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.
