Radar object positioning method based on machine learning

Abstract

The invention proposes a radar object positioning method based on machine learning. According to one embodiment of the invention, the method comprises the steps: carrying out the range-direction pulse compression of a target echo signal received by an along-track two-channel SAR (synthetic aperture radar), obtaining an interference phase matrix, sequentially inputting the interference phase vectors corresponding to all distance units in the interference phase matrix into a neural network, and obtaining a result of judgment whether each distance unit has a moving object or not and the orientation position x0 of the moving object. According to the embodiment of the invention, the method avoids problems of complex orientation pulse compression, radial speed estimation and faced phase fuzziness of a conventional ATI (along-track interference) method, thereby solving a problem that the positioning precision of the moving object is decreased because of error accumulation in the above process. The method is higher in positioning precision of the moving object, is lower in time consumption, and can meet the demands of high instantaneity and high positioning precision.

Description

technical field [0001] The present application relates to the field of signal processing technology, specifically to the field of radar target detection and recognition, and in particular to a machine learning-based radar target positioning method, which can be used for radar moving target detection, moving target positioning and tracking, etc. Background technique [0002] Existing radar systems are based on relatively strict theoretical models and traditional signal processing methods, which mainly have the following problems: (1) It is difficult to model complex environments, system errors accumulate, and high robustness is difficult to achieve; (2) Linear periodic processing methods (such as Fourier transform, etc.), deblurring (range ambiguity, Doppler ambiguity, phase ambiguity, velocity ambiguity, etc.) requires complex calculations and expensive hardware costs; (3) The radar system is complex, poor in versatility, and weak in portability , The upgrade cycle is long. ...

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Problems solved by technology

[0002] Existing radar systems are based on relatively strict theoretical models and traditional signal processing methods, which mainly have the following problems: (1) It is difficult to model complex environments, system errors accumulate, and high robustness is difficult to achieve; (2) Linear periodic processing methods (such as Fourier transform, etc.), deblurring (range ambiguity, Doppler ambiguity, phase ambiguity, velocity ambiguity, etc.) requires complex calculations and expensive hardware costs; (3) The radar system is complex, poor in versatility, and weak in portability , long upgrade cycle

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