CN-121995363-A - Bistatic radar signal reflection detector

Abstract

Bistatic radar signal reflection detectors. A method includes receiving a set of radar signals incident on a set of objects. The method includes selecting a GLRT detector. The method includes determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model. The method includes determining whether a set of angles associated with the set of radar signals is available. The method includes, in response to determining that the set of angles is available, updating a set of data associated with a set of objects. The method includes, in response to determining that the set of angles is not available, selecting an angle estimation method, estimating the set of angles, and tracking the set of objects.

Inventors

• ZHANG XIN
• LI ZHENGZHENG
• P. Agraval
• S. ROGERS

Assignees

• APTIV技术股份公司

Dates

Publication Date

20260508

Application Date

20251031

Priority Date

20241101

Claims (20)

1. 1. A method, the method comprising: Receiving a set of radar signals incident on a set of objects; determining a first hypothesis model representing a single base signal model of the set of radar signals; Determining a second hypothesis model representing a bistatic signal model for the set of radar signals; selecting a selected GLRT detector from a set of generalized likelihood ratio test GLRT detectors based on a set of signal criteria; determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model based on the selected GLRT detector; determining whether the ratio is greater than a threshold; Determining whether a set of angles associated with the set of radar signals is available; updating a set of data associated with a set of objects in response to determining that the set of angles is available, and In response to determining that the set of angles is not available: selecting an angle estimation method based on the ratio; estimating the set of angles using the selected angle estimation method, and Tracking the set of objects based on the set of angles.
2. 2. The method of claim 1, the method further comprising: determining that the second hypothesis model is accurate in response to determining that the ratio is greater than a threshold, and In response to determining that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate.
3. 3. The method of claim 1, further comprising autonomously controlling the vehicle to avoid the set of objects.
4. 4. The method of claim 1, wherein selecting the angle estimation method comprises: selecting a first angle estimation method in response to determining that the ratio is greater than the threshold, and In response to determining that the ratio is less than the threshold, a second angle estimation method is selected.
5. 5. The method of claim 1, wherein the set of GLRT detectors is derived from the following formula: 。
6. 6. the method of claim 5, wherein the set of GLRT detectors comprises: the first GLRT detector is used to detect, the first GLRT detector is defined as: , a second GLRT detector is provided which is configured to detect, the second GLRT detector is defined as: , a third GLRT detector is provided which is configured to detect, the third GLRT detector is defined as: A kind of electronic device A fourth GLRT detector is provided which is configured to detect, the fourth GLRT detector is defined as: 。
7. 7. the method according to claim 6, wherein: the selected angle estimation method is applicable to direct path reflection and multipath reflection, and The selected GLRT detector is either the third GLRT detector or the fourth GLRT detector.
8. 8. The method according to claim 6, wherein: The first hypothesis model The definition is as follows: , The second hypothesis model The definition is as follows: , Is a set of array observations of the array, Is a first spatial matrix of reflection paths, Is a second spatial matrix of reflection paths, Is a set of the transmitted signals that are transmitted, Is a set of noise data that is to be processed, Θ is a set of angle data containing K elements, N is the number of radar elements, K is the number of reflection paths and, M is the number of observations that are made, Η is the power level associated with the set of noise data, Gamma is the threshold value and, P 1 is the likelihood function under the second hypothesis model, P 0 is the likelihood function under the first hypothesis model, Representing the Frobenius norm, P A is the first projection matrix that maps the vectors to projections on the subspace formed by a, where, , P B is a second projection matrix that maps the vectors to projections on the subspace formed by B, wherein, , Is defined as A kind of electronic device Is defined as Wherein I is an identity matrix.
9. 9. The method of claim 8, the method further comprising: Determining a probability that the second hypothesis model is correct, Wherein the probability is defined as: 。
10. 10. the method of claim 8, the method further comprising: Determining a probability that the first hypothesis model is correct, Wherein the probability is defined as: 。
11. 11. the method of claim 8, wherein the first hypothesis model is based on: the number of elements in the radar array that receive the set of radar signals, A first number of the reflection paths, A first number of observations is made, The set of array observations is made, A first spatial matrix of the reflection paths, The set of transmitted signals, and The set of noise data.
12. 12. The method of claim 11, wherein the second hypothesis model is based on: the number of elements in the radar array that receive the set of radar signals, A second number of the reflection paths, A second number of observations is made, The set of array observations is made, A second spatial matrix of the reflection paths, The set of transmitted signals, and The set of noise data.
13. 13. The method of claim 1, wherein the set of signal criteria comprises: A first criterion that is met when a power level associated with a set of noise data is known, an A second criterion is satisfied when a set of angles associated with the spatial matrix of reflection paths is known.
14. 14. The method of claim 1, wherein the set of angles includes a direction of emission and a direction of arrival.
15. 15. The method of claim 1, the method further comprising: estimating the set of angles before determining whether the ratio is greater than the threshold, and Based on the ratio, a set of objects associated with the bistatic reflection is identified.
16. 16. A system, the system comprising: memory hardware configured to store instructions; Processor hardware configured to execute the instructions, wherein the instructions comprise: Receiving a set of radar signals incident on a set of objects; determining a first hypothesis model representing a single base signal model of the set of radar signals; Determining a second hypothesis model representing a bistatic signal model for the set of radar signals; selecting a selected GLRT detector from a set of generalized likelihood ratio test GLRT detectors based on a set of signal criteria; determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model based on the selected GLRT detector; determining whether the ratio is greater than a threshold; Determining whether a set of angles associated with the set of radar signals is available; updating a set of data associated with a set of objects in response to determining that the set of angles is available, and In response to determining that the set of angles is not available: selecting an angle estimation method based on the ratio; estimating the set of angles using the selected angle estimation method, and Tracking the set of objects based on the set of angles.
17. 17. The system of claim 16, wherein the instructions comprise: determining that the second hypothesis model is accurate in response to determining that the ratio is greater than a threshold, and In response to determining that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate.
18. 18. The system of claim 16, wherein selecting the angle estimation method comprises: selecting a first angle estimation method in response to determining that the ratio is greater than the threshold, and In response to determining that the ratio is less than the threshold, a second angle estimation method is selected.
19. 19. The system of claim 16, wherein: the set of GLRT detectors is derived from the following formula A kind of electronic device The set of GLRT detectors comprises: the first GLRT detector is used to detect, the first GLRT detector is defined as: , a second GLRT detector is provided which is configured to detect, the second GLRT detector is defined as: , a third GLRT detector is provided which is configured to detect, the third GLRT detector is defined as: A kind of electronic device A fourth GLRT detector is provided which is configured to detect, the fourth GLRT detector is defined as: 。
20. 20. The system of claim 19, wherein: The first hypothesis model The definition is as follows: , The second hypothesis model The definition is as follows: , Is a set of array observations of the array, Is a first spatial matrix of reflection paths, Is a second spatial matrix of reflection paths, Is a set of the transmitted signals that are transmitted, Is a set of noise data that is to be processed, Θ is a set of angle data containing K elements, N is the number of radar elements, K is the number of reflection paths and, M is the number of observations that are made, Η is the power level associated with the set of noise data, Gamma is the threshold value and, P 1 is the likelihood function under the second hypothesis model, P 0 is the likelihood function under the first hypothesis model, Representing the Frobenius norm, P A is the first projection matrix that maps the vectors to projections on the subspace formed by a, where, , P B is a second projection matrix that maps the vectors to projections on the subspace formed by B, wherein, , Is defined as A kind of electronic device Is defined as Wherein I is an identity matrix.

Description

Bistatic radar signal reflection detector Technical Field The present disclosure relates to radar signal processing, and more particularly to detecting bistatic reflection and estimating angles based on processing of received radar signals (U.S. patent classification 342). Background In automotive applications, radar is often used to detect obstacles, such as other vehicles or other hazards. In some scenarios and environments, the transmitted radar signal returns directly to the local radar (direct path signal) after hitting a first object, while in other cases the signal returns to the local radar (multipath signal) after reflecting the first object and then the second object. Since the environment of automotive radar illumination is often crowded, multipath reflections may dominate in some cases, and the most challenging type is called "bistatic" reflection, which affects how the angle is estimated after range-doppler processing. Conventional angle estimation methods assume that the reflection is direct path, which can produce large angle estimation errors when the models do not match. A special angle estimator may be required when multipath reflections occur. Bistatic reflection also affects how the target tracking system handles distance-speed-angle detection. Tracking algorithms generally assume that the reflection is a direct path and that the detection with bistatic reflection is generally discarded. Thus, determining whether range-doppler detection includes multipath energy becomes critical to the overall system. By definition, for a bistatic signal, the direction of arrival (DOA) is not equal to the direction of emission (DOD), so detection of bistatic reflection must be performed in the spatial (or angular) domain after range-Doppler processing. Some bistatic detectors apply Linear Prediction (LP) theory to a synthetic Uniform Linear Array (ULA). When there is no bistatic reflection, the LP error approaches the noise power, and when there is a bistatic reflection, the LP error becomes large. Comparing the LP error to a threshold may indicate whether the signal is multipath. Other methods use angle expansion schemes (e.g., direct and cross-match) to detect bistatic reflection by testing for angle expansion match errors between the DOA and DOD. When using such schemes, there is a bistatic reflection if the direct match error is large and the cross match error is less than the direct match error. The third method uses a joint DOD-DOA estimation method. Multipath detection is performed for each reflected path by comparing the angle spread match error between the relevant DOD and the DOA. The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure. Disclosure of Invention A method includes receiving a set of radar signals incident on a set of objects. The method includes determining a first hypothesis model representing a single base signal model of the set of radar signals. The method includes determining a second hypothesis model representing a bistatic signal model for the set of radar signals. The method includes selecting a selected Generalized Likelihood Ratio Test (GLRT) detector from a set of GLRT detectors based on a set of signal criteria. The method includes determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model based on the selected GLRT detector. The method includes determining whether the ratio is greater than a threshold. The method includes determining whether a set of angles associated with the set of radar signals is available. The method includes, in response to determining that the set of angles is available, updating a set of data associated with a set of objects. The method includes, in response to determining that the set of angles is not available, selecting an angle estimation method based on the ratio. The method includes estimating the set of angles using a selected angle estimation method. The method includes tracking the set of objects based on the set of angles. In other features, the method includes, in response to determining that the ratio is greater than a threshold, determining that the second hypothesis model is accurate. In other features, the method includes, in response to determining that the ratio is less than or equal to a threshold, determining that the first hypothesis model is accurate. In other features, the method includes autonomously controlling the vehicle to avoid the set of objects. In other features, selecting the angle estimation method includes, in response to determining that the ratio is greater than a th