For direction-of-arrival estimation problems, deep learning (DL) has shown excellent performance recently owing to the effectiveness and robustness to complicated cases. However, DL is always requiring massive data and lacks explainable theory, which limits its practical application. Fortunately, deep unfolding is able to overcome the disadvantages of DL and empirically achieves fast convergence. Inspired by that, we construct a deep unfolded network according to the famous Sparse Learning via Iterative Minimization (SLIM), yielding a method called learned-SLIM (LSLIM). LSLIM is able to converge efficiently and inherits the advantages of SLIM, such as low computational complexity, excellent sparsity performance. In addition, nested array is further adopted in LSLIM for high estimation accuracy. Extensive simulations are presented to illustrate the superior of the proposed LSLIM beyond other state-of-the-art algorithms.
Scattering centers are important features of targets at high frequency regions and the geometric theory of diffraction (GTD) scattering center model is the typical one to describe scattering centers. Therefore, it is quite vital to estimate parameters of GTD model accurately. The classical multiple signal classification (MUSIC) algorithm can effectively estimate these parameters at high signal-to-noise ratios (SNR) but it suffers from poor parameter estimation performance at low SNR scenarios. To solve this problem, we propose a modified MUSIC algorithm which enhances the noise robustness. The modified MUSIC algorithm construct a new total covariance matrix R by averaging the auto-correlation matrix of the original back-scattered data and the auto-correlation matrix of its conjugate data. Then, we take even powers of R ,which can broaden the differences between the eigenvalues of noises and signals and avoid overlapping spectral peaks. The theoretical computational complexities of the main modified step are discussed in this paper. Simulation results verify that the proposed algorithms achieve superior accuracy in parameter estimation of the GTD model and obtain better noise robustness. What is more, a target recognition method based on the GTD model and the artificial intelligence algorithm are proposed in this paper. Simulation results validate the effectiveness of this method.
For high-speed and high-maneuvering target detection, a detection method based on TRT and special GRFT is proposed in this paper. Firstly, the range migration and Doppler migration caused by target velocity and jerk are corrected by TRT. Secondly, estimation of target acceleration can be realized by special GRFT. Then, the estimated acceleration is used to construct a compensation function to correct the range migration and Doppler migration caused by acceleration. Finally, we can achieve coherent accumulation simulation results demonstrate the effectiveness of the proposed method.
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