Radar moving target multi-frame joint detection method based on graph space-time network

Abstract

The invention discloses a radar moving target multi-frame joint detection method based on a graph space-time network. The method mainly solves the problem that in the prior art, the false alarm rate of moving target detection of a single-channel system is high. According to the scheme, the method comprises the steps of obtaining a sub-aperture distance Doppler spectrum; constructing a sub-residualnetwork and a sub-graph space-time network, and forming a neural network model for moving target detection by using the sub-residual network and the sub-graph space-time network; performing regionaltarget detection by using the sub residual network, outputting a preliminary detection probability graph, and calculating cross entropy loss; performing spatial-temporal feature extraction and fusionby using a sub-graph spatial-temporal network, outputting a final detection probability graph of an intermediate frame moving target, and calculating a mean square error; and taking the sum of the cross entropy loss and the mean square error as a total cost function, training the neural network until the total cost function is converged to obtain a trained neural network, inputting test data intothe trained neural network, judging an output threshold value of the trained neural network, and suppressing a non-maximum value to obtain a moving target detection result of an intermediate frame. According to the invention, the false alarm rate is reduced, and reliable moving target detection can be realized.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, in particular to a multi-frame joint detection method for a moving target, which can be used in a high frame rate radar system. Background technique [0002] Moving target detection is the basic task of airborne maritime radar to realize battlefield environment perception and potential target surveillance. In Doppler processing, moving targets can be detected by extracting their Doppler shift. However, the harsh sea conditions and the complex motion characteristics of the target often lead to a low signal-to-noise ratio of the echo of the moving target. Achieving reliable moving object detection under low signal-to-noise ratio is a research hotspot at present. Most target detection algorithms use clutter suppression to improve the signal-to-noise ratio, so effective suppression of strong sea clutter is a key technology for maritime radar moving target detection. In a single-cha...

AI Technical Summary

Problems solved by technology

However, due to the feature extraction of the entire image, this method is only suitable for tasks such as extracting motion information in videos and video classification, and cannot extract target position information, so it is not suitable for target detection tasks.

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