EP-4737938-A1 - RADAR DETECTION AND CLASSIFICATION USING AN AUTO REGRESSIVE SPECTRAL ESTIMATOR WITH MACHINE LEARNING

Abstract

A processing circuitry-based method of detecting radar targets, the method comprising a) receiving data derivative of a series of radar pulse measurements; b) generating an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model; c) utilizing data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least a. an indication of presence or absence of a target, and b. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity.

Inventors

• AMARA, ARIE

Assignees

• Elta Systems Ltd.

Dates

Publication Date

20260506

Application Date

20251031

Claims (13)

1. A processing circuitry-based method of detecting radar targets, the method comprising: a) receiving data derivative of a series of radar pulse measurements; b) generating an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model; c) utilizing data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least: a. an indication of presence or absence of a target, and b. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity.
2. The method of claim 1, wherein the utilizing the data as input to the trained machine learning models further results in, responsive to presence of the target: data indicative of a target identification.
3. The method of any one of claims 1-2, wherein the data derivative of the series of radar pulse measurements comprises in-phase and quadrature (I/Q) data.
4. The method of any one of claims 1-3, wherein the given order is four.
5. The method of any of claims 1-4, wherein the data derivative of the complex coefficients is data informative of pole coordinates.
6. The method of claim 5, wherein the pole coordinates are based on roots of: 1 − ∑ k = 1 p a k z k where p is the given order of the estimated model, a k are the complex coefficients of the estimated model, and z k are the radar pulse measurements.
7. The method of claim 5, wherein the data derivative of the complex coefficients is polar map image data based on pole coordinates, the pole coordinates being based on the complex coefficients of the estimated model.
8. The method of any one of claims 1-7, wherein the performing complex autoregressive spectral estimation comprises at least one of: a) least squares estimation, b) Yule-Walker equation computation, c) Levinson-Durbin algorithm, d) Burg's method, e) maximum likelihood estimation, f) parametric estimation with Kalman filtering, and g) predictive error minimization.
9. The method of claim 4, the method additionally comprising, prior to step b): evaluating presence of a target, based on applying signal processing techniques to an AR spectral estimation of order two of data derivative of the series of radar pulses; and wherein the generating is responsive to successful evaluating of the presence of the target.
10. The method of claim 9, the method additionally comprising, subsequent to c): verifying the distance, energy, and velocity of the target, based on at least one of: i. utilizing a constant false alarm rate (CFAR) method in conjunction with a range-Doppler map based on data derivative of the series of radar pulses; and ii. applying signal processing techniques to an AR spectral estimation of order two of data derivative of the series of radar pulses.
11. The method of any one of claims 1-10, wherein at least one of the one or more machine learning models was trained by a method comprising: a. receiving data that is derivative of AR spectral estimation coefficients associated with a given radar target; b. receiving ground truth data associated with the given radar target, the ground truth data comprising at least one of: i. a distance, ii. an energy, iii. a velocity, and iv. an identification associated with the given radar target; c. training the machine learning model based on the received data derivative of the AR spectral estimation coefficients and the received ground truth data.
12. A system of detecting radar targets, the system comprising a processing circuitry configured to: a) receive data derivative of a series of radar pulse measurements; b) generate an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model; c) utilize data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least: a. an indication of presence or absence of a target, and b. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity.
13. A computer program product comprising a computer readable non-transitory storage medium containing program instructions, which program instructions when read by a processor, cause the processing circuitry to perform a method of detecting radar targets, the method comprising: a) receiving data derivative of a series of radar pulse measurements; b) generating an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model; c) utilizing data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least: a. an indication of presence or absence of a target, and b. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity.

Description

TECHNICAL FIELD The presently disclosed subject matter relates to radar detection, and in particular to machine learning/artificial intelligence-based enhancements to detection and identification of radar targets BACKGROUND Problems of detection in radar systems have been recognized in the conventional art and various techniques have been developed to provide solutions. GENERAL DESCRIPTION According to one aspect of the presently disclosed subject matter there is provided a method of detecting radar targets, the method comprising: a) receiving data derivative of a series of radar pulse measurements;b) generating an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model;c) utilizing data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least: a. an indication of presence or absence of a target, andb. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity. In addition to the above features, the system according to this aspect of the presently disclosed subject matter can comprise one or more of features (i) to (x) listed below, in any desired combination or permutation which is technically possible: (i) the utilizing the data as input to the trained machine learning models further results in, responsive to presence of the target: data indicative of a target identification.(ii) the data derivative of the series of radar pulse measurements comprises in-phase and quadrature (I/Q) data.(iii)the given order is four.(iv)the data derivative of the complex coefficients is data informative of pole coordinates.(v) the pole coordinates are based on roots of: 1−∑k=1pakzk where p is the given order of the estimated model, ak are the complex coefficients of the estimated model, and zk are the radar pulse measurements.(vi) the data derivative of the complex coefficients is polar map image data based on pole coordinates, the pole coordinates being based on the complex coefficients of the estimated model.(vii) the performing complex autoregressive spectral estimation comprises at least one of: a) least squares estimation,b) Yule-Walker equation computation,c) Levinson-Durbin algorithm,d) Burg's method,e) maximum likelihood estimation,f) parametric estimation with Kalman filtering, andg) predictive error minimization.(viii) the method additionally comprising, prior to step b): evaluating presence of a target, based on applying signal processing techniques to an AR spectral estimation of order two of data derivative of the series of radar pulses;and wherein the generating is responsive to successful evaluating of the presence of the target.(ix)the method additionally comprising, subsequent to c): verifying the distance, energy, and velocity of the target, based on at least one of: i. utilizing a constant false alarm rate (CFAR) method in conjunction with a range-Doppler map based on data derivative of the series of radar pulses; andii. applying signal processing techniques to an AR spectral estimation of order two of data derivative of the series of radar pulses.(x) at least one of the one or more machine learning models was trained by a method comprising: a. receiving data that is derivative of AR spectral estimation coefficients associated with a given radar target;b. receiving ground truth data associated with the given radar target, the ground truth data comprising at least one of: i. a distance,ii. an energy,iii. a velocity, andiv. an identification associated with the given radar target;c. training the machine learning model based on the received data derivative of the AR spectral estimation coefficients and the received ground truth data. According to another aspect of the presently disclosed subject matter there is provided a system of detecting radar targets, the system comprising a processing circuitry configured to: a) receive data derivative of a series of radar pulse measurements;b) generate an autoregressive (AR) spectral estimation, of a given order, from the received data, thereby resulting in two or more complex coefficients of an estimated AR model;c) utilize data derivative of the two or more complex coefficients as input to one or more trained machine learning models, thereby resulting in, at least: a. an indication of presence or absence of a target, andb. responsive to presence of the target: a distance, an energy, and a velocity of the target, the one or more models having been trained to receive data derivative of complex coefficients and output data informative of target depth, energy, and velocity. This aspect of the disclosed subject matter can further optionally comprise one or more of features (i) to (x) listed above wi