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High‐speed target often induces severe across range unit problem in the long‐time coherent integration (LTCI). This paper designates the bottom‐right corner of the 2‐D compressed signal as the coordinate system origin. It establishes a novel angular inverse rotation transform that accurately corrects target's trajectory to the final location and achieves signal energy accumulation. Numerical simulation experiments and real‐world measured data confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages. [ABSTRACT FROM AUTHOR]

Since the distributed MIMO radar (DMR) has widely spread transmitters and receivers, it can provide higher target detection probability, as well as superior target tracking and localization performance than the monostatic/bistatic radar systems. An effective radar resource allocation scheme can optimise the DMR system parameter and obtain better system performance. In this paper, a critical but limited system resource is optimised, that is, select a subset of the active radar antennas. In this scenario, the convolutional neural network of the antenna subset selection for target localization (LCNN‐ASS) algorithm in the DMR is proposed based on two free switching policies. The proposed algorithm is immune to the failure of a single policy and selects antenna subsets from the entire sets with a remarkable computational speed. Therefore, the proposed algorithm increases the flexibility of resource scheduling over traditional algorithms. Simulation experiments and performance analysis demonstrate the localization performance and flexibility of the LCNN‐ASS algorithm. [ABSTRACT FROM AUTHOR]

Researchers have recently shown an increased interest in estimating the direction‐of‐arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace‐based methods. An off‐grid sparse recovery‐based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal‐to‐noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer–Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

Narrow-band interference (NBI) is a critical issue for synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based signal separation in time–frequency domain is proposed. The RPCA algorithm is introduced for signal separation in the time–frequency domain for the first time. The fundamental assumption of RPCA is that a matrix can be modeled as a combination of a low-rank matrix and a sparse counterpart. In terms of the SAR echo, the short time Fourier transformation (STFT) matrix of mixed signals (i.e., useful SAR signals and NBIs) well fits the assumption of RPCA. Based on this property, radar echoes are first transformed into the time–frequency domain by STFT to form an STFT matrix. Then, the RPCA algorithm is used to decompose the STFT matrix into a low-rank matrix (i.e., NBIs) and a sparse matrix (i.e., useful signals). Finally, the NBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible. [ABSTRACT FROM PUBLISHER]