Radar Maneuvering Target Signal Integration Algorithm Based On Segmentation Processing

The development and progress of military science and technology has made more and more mobile targets appear in the competition of military equipment.This kind of competition makes the national defense field urgently need radar to effectively detect mobile targets.Radar usually improves the signal-to-noise ratio by accumulating signals over a long period of time.However,the distance walking characteristics and Doppler walking characteristics of maneuvering targets lead to the failure of traditional accumulation methods.Although the algorithm for perfect matching and accumulation of distance walk and Doppler walk has good accumulation effect,the calculation amount is more redundant.Therefore,this paper studies two radar maneuvering target echo accumulation algorithms based on segmented processing.Aiming at the shortcomings of these two algorithms,two improved segmented accumulation algorithms are proposed.The specific content is as follows:(1)The echo model of the pulse compression radar after the detection of the maneuvering target is studied,and the echo is processed by pulse compression;the characteristics of the range movement and Doppler movement of the radar maneuvering target echo after pulse compression are analyzed;Finally,several classic long-term coherent accumulation algorithms(MTD,RFT,GRFT)are introduced.Experiments show that the traditional MTD algorithm fails to detect maneuvering targets.The RFT calculation method has good detection performance but cannot effectively accumulate the echoes of maneuvering targets with uniform acceleration.GRFT can detect maneuvering targets with acceleration,but the amount of calculation is relatively large.(2)Analyze the MTD-GRT-based radar maneuvering target echo segment accumulation algorithm,and analyze the error of the algorithm,and propose an improved MTD-GRT algorithm(MTD-MGRT)accumulation algorithm.The algorithm first segments the echo,and then performs MTD processing on the echo within the segment,and uses MGRT to correct the error of the MTD-GRT algorithm for peak accumulation between segments.Through simulation experiments,a comparison experiment was made on the calculation amount and accumulation performance of MTD-GRT algorithm and MTD-MGRT.Experiments show that MTD-MGRT is a good compromise between the amount of calculation and accumulation performance.(3)A maneuvering target echo accumulation algorithm(SHI)based on improved subspace segmentation processing is studied.This method can effectively accumulate maneuvering targets with large motion parameters,but the algorithm has large errors.Subsequently,an echo maneuvering target accumulation algorithm based on improved subspace segmentation echo processing(I-SHI)is proposed.The algorithm first uses subspace division to divide the full parameter space into several parameter subspaces,and then performs subspace The moving operation aligns the center of the parameter space with the zero point of the original full parameter space to form multiple new parameter subspaces,which can effectively reduce the parameter size.Finally,perform MTD-GRT processing in the subspace to accumulate energy.Finally,the energy accumulation,calculation amount and performance of I-SHI are analyzed and verified,which proves that the algorithm effectively overcomes the limitation of the larger motion parameters on the space aperture,and the energy accumulated by the SHI algorithm is greatly increased,and at the same time,it is effectively reduced compared to the GRFT algorithm.calculation amount.