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EP-4737941-A1 - MULTIPATH RADAR SIGNAL REFLECTION DETECTORS

EP4737941A1EP 4737941 A1EP4737941 A1EP 4737941A1EP-4737941-A1

Abstract

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 a second hypothesis model and a maximized likelihood function of a 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 a determination that the set of angles is available, updating a set of data associated with a set of objects. The method includes, in response to a determination 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
  • AGRAWAL, PIYUSH
  • ROGERS, STUART

Assignees

  • Aptiv Technologies AG

Dates

Publication Date
20260506
Application Date
20251020

Claims (15)

  1. A method comprising: receiving a set of radar signals incident on a set of objects; determining a first hypothesis model that represents a monostatic signal model of the set of radar signals; determining a second hypothesis model that represents a bistatic signal model of the set of radar signals; selecting, from a set of generalized likelihood ratio test (GLRT) detectors, a selected GLRT detector based on a set of signal criteria; based on the selected GLRT detector, determining a ratio between a maximized likelihood function of the second hypothesis model and a maximized likelihood function of the first hypothesis model; determining whether the ratio is greater than a threshold; determining whether a set of angles associated with the set of radar signals is available; in response to a determination that the set of angles is available, updating a set of data associated with a set of objects; and in response to a determination 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. The method of claim 1 further comprising: in response to a determination that the ratio is greater than a threshold, determining that the second hypothesis model is accurate; and in response to a determination that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate.
  3. The method of claim 1 or claim 2, further comprising autonomously controlling a vehicle to avoid the set of objects.
  4. The method of any one of the preceding claims, wherein selecting the angle estimation method includes: in response to a determination that the ratio is greater than the threshold, selecting a first angle estimation method; and in response to a determination that the ratio is less than the threshold, selecting a second angle estimation method.
  5. The method of any one of the preceding claims, wherein the set of GLRT detectors is derived from: L = max θ , S , η p 1 Y θ , S , η max θ , S , η p 0 Y θ , S , η H 1 ≷ H 0 γ .
  6. The method of claim 5 wherein the set of GLRT detectors includes: a first GLRT detector defined as: L = min θ P A ⊥ θ Y F 2 min θ P B ⊥ θ Y F 2 , a second GLRT detector defined as: L ′ = max θ P B θ Y F 2 − max θ P A θ Y F 2 , a third GLRT detector defined as: L " = P B θ Y F 2 − P A θ Y F 2 , and a fourth GLRT detector defined as: L ‴ = P A ⊥ θ Y F 2 P B ⊥ θ Y F 2 .
  7. The method of claim 6 wherein: the selected angle estimation method is applicable to direct-path reflections and multi-path reflections, and the selected GLRT detector is the third GLRT detector or the fourth GLRT detector.
  8. The method of claim 6 or claim 7, wherein: the first hypothesis model ( ) is defined as: Y = A θ S + W , the second hypothesis model ( ) is defined as: Y = B θ S + W , Y ∈ ℂ N × M is a set of array observations, A θ ∈ ℂ N × K is a first spatial matrix of reflection paths, B θ ∈ ℂ N × K is a second spatial matrix of reflection paths, S ∈ ℂ K × M is a set of transmitted signals, W ∈ ℂ N × M is a set of noise data, θ is a set of angle data that includes K quantity of elements, N is a quantity of radar elements, K is a quantity of reflection paths, M is a quantity of observations, η is a power level associated with the set of noise data, γ is a threshold value, p 1 is a likelihood function under the second hypothesis model, p 0 is a likelihood function under the first hypothesis model, ∥·∥ F represents a Frobenius norm, P A is a first projection matrix that maps vectors into their projections on to a subspace formed by A, where P A ( θ ) = A ( θ )( A ( θ ) H A ( θ )) -1 A ( θ ) H , P B is a second projection matrix that maps vectors into their projections on to a subspace formed by B, where P B ( θ ) = B ( θ )( B ( θ ) H B ( θ )) -1 B ( θ ) H , P B ⊥ θ is defined as P B ⊥ θ = I − P B θ , and P A ⊥ θ is defined as P A ⊥ θ = I − P A θ , where I is an identity matrix.
  9. The method of claim 8 further comprising: determining a probability that the second hypothesis model is correct, wherein the probability is defined as: λ B = max θ P B θ Y F 2 max θ P B θ Y F 2 + max θ P A θ Y F 2 , or, determining a probability that the first hypothesis model is correct, wherein the probability is defined as: λ A = max θ P A θ Y F 2 max θ P B θ Y F 2 + max θ P A θ Y F 2 .
  10. The method of claim 8 or claim 9, wherein the first hypothesis model is based on: a quantity of elements in a radar array that receives the set of radar signals, a first quantity of reflection paths, a first quantity of observations, the set of array observations, the first spatial matrix of reflection paths, the set of transmitted signals, and the set of noise data.
  11. The method of claim 10, wherein the second hypothesis model is based on: the quantity of elements in a radar array that receives the set of radar signals, a second quantity of reflection paths, a second quantity of observations, the set of array observations, the second spatial matrix of reflection paths, the set of transmitted signals, and the set of noise data.
  12. The method of any one of the preceding claims, wherein the set of signal criteria includes: a first criterion that is met when a power level associated with a set of noise data is known, and a second criterion that is met when a set of angles associated with a spatial matrix of reflection paths is known.
  13. The method of any one of the preceding claims, wherein the set of angles includes a direction of departure and a direction of arrival.
  14. The method of any one of the preceding claims, further comprising: before determining whether the ratio is greater than the threshold, estimating the set of angles; and based on the ratio, identify a set of objects associated with bistatic reflections.
  15. A system comprising: memory hardware configured to store instructions; processor hardware configured to execute the instructions, wherein the instructions, when executed, are arranged to cause the method of any one of claims 1 to 14 to be performed.

Description

FIELD The present disclosure relates to radar signal processing and more particularly to detecting bistatic reflections and estimating angles based on processing of received radar signals (USPC Class 342). BACKGROUND In automotive applications, radar is frequently employed to detect obstacles such as other vehicles or other hazards. In some scenarios and environments, the transmitted radar signals reflect back directly to the host radar (direct-path signals) after hitting a first object and in others, the signal returns to the host radar after reflecting a first object and then a second object (multi-path signals). Since the environment illuminated by automotive radar is often crowded, multi-path reflections could be the majority in some cases, and the most challenging type is called a "bistatic" reflection which affects how to estimate the angles after range-Doppler processing. Conventional angle estimation approaches assume that the reflections are direct-path, which creates large angle estimation errors when the model mismatches. Special angle estimators may be required when multi-path reflections occur. Bistatic reflections also affect how object tracking systems deal with range-velocity-angle detections. Tracking algorithms frequently assume that reflections are direct-path, and the detections with bistatic reflections are usually discarded. Therefore, determining whether a range-Doppler detection contains multi-path energy becomes critical to the whole system. By definition, the direction of arrival (DOA) is not equal to the direction of departure (DOD) for a bistatic signal, therefore the detection of bistatic reflection must be done in the spatial (or angle) domain after range-Doppler processing. Some bistatic detectors apply linear prediction (LP) theory to a synthetic uniform linear array (ULA). The LP error is close to the noise power when there are no bistatic reflections, and the LP error becomes larger when bistatic reflections exist. Comparing the LP error with a threshold indicates whether a signal is multi-path. Other methods use angle unfolding schemes (such as direct-matching and cross-matching) to detect bistatic reflections by testing the angle unfolding matching errors between the DOA and DOD. When using such schemes, a bistatic reflection exists if the direct-matching error is large and the cross-matching error is less than the direct-matching error. A third method uses a joint DOD-DOA estimation approach. Multi-path detection is conducted for each reflection path by comparing the angle unfolding matching error between the associated DOD and DOA. The background description provided here 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. SUMMARY A method includes receiving a set of radar signals incident on a set of objects. The method includes determining a first hypothesis model that represents a monostatic signal model of the set of radar signals. The method includes determining a second hypothesis model that represents a bistatic signal model of the set of radar signals. The method includes selecting, from a set of generalized likelihood ratio test (GLRT) detectors, a selected GLRT detector based on a set of signal criteria. The method includes, based on the selected GLRT detector, 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 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 a determination that the set of angles is available, updating a set of data associated with a set of objects. The method includes, in response to a determination 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 the 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 a determination 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 a determination that the ratio is less than or equal to the threshold, determining that the first hypothesis model is accurate. In other features, the method includes autonomously controlling a vehicle to avoid the set of objects. In other features, selecting the angle estimation method includes, in response to a determination that the ratio is greater than the threshold, se