Range cell migration and Doppler frequency migration induced by the target maneuverability are two difficulties of target signal enhancement and radar detection performance. In order to resolve them, a novel subaperture joint coherent integration (SJCI) algorithm is proposed in this article, which consists of three stages. Firstly, it divides the target signal into several subapertures, in which the Doppler frequency dispersions can be neglected. Afterward, coherent integration within each subaperture is implemented via scaled Fourier transform. Finally, correcting the Doppler frequency shifts and phase differences via axis rotation and phase compensation technology, the joint coherent integration among the subapertures can be achieved effectively. Based on the SJCI algorithm, an upgrade algorithm named subspace SJCI (SSJCI) is presented. Through acceleration space division and subspace translation, the SSJCI algorithm extends the subaperture time and optimizes the computation complexity significantly. Theoretical analyses and performance comparisons demonstrate that the SSJCI algorithm can accomplish a good trade-off among signal-to-noise ratio gain, detection capability, resolution, and computation complexity. In addition, the results of the numerical experiments further verify the effectiveness of the proposed algorithm.

A mainlobe maintenance method based on shrinkage estimator is presented here to promote the adaptive digital beamforming performance when there exists mainlobe jamming (MLJ). First, block matrix preprocessing (BMP) method is applied to suppress the MLJ. Then, the linear combination of estimated covariance matrix and identity matrix is optimised to generate more accurate estimation of the covariance matrix. After that, the improved covariance matrix is utilised to generate the adaptive weights to suppress the sidelobe jamming. Finally, the simulation shows that the proposed method is capable of eliminating peak offset of mainlobe and high sidelobes introduced by BMP and provides robustness against finite data samples effects. Accordingly, it outperforms noise whitening with error compensation, diagonal loading, and robust covariance matrix reconstruction in output SINR.

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Due to the target motion, range cell migration (RCM) and Doppler frequency migration (DFM) always occur. That is harmful to the signal enhancement and detection. In order to solve the problem, a novel three-dimensional (3-D) coherent integration (TDCI) based algorithm is proposed in this paper which consists of three stages. Firstly, a 3-D space is generated by the autocorrelation function. After that, TDCI algorithm is realized and TDCI domain is obtained in which the motion parameters can be accurately estimated. Finally, compensating off the RCM and DFM by the estimates, the target signal can be accumulated and detected in range-Doppler frequency domain. Theoretical analyses and simulation experiments are given to demonstrate that the proposed algorithm is able to deal with the problems of velocity ambiguity, shadow effect, and cross-term with superior resolution. Comparisons with several representative algorithms lead us to the conclusion that the proposed algorithm can strike a good balance between computation cost and anti-noise performance. In the end, real measured data processing and result analysis are carried out, which further verify the effectiveness of the proposed algorithm.

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