Recently, nested and coprime arrays have attracted considerable interest due to their capability of providing increased array aperture, enhanced degrees of freedom (DOFs), and reduced mutual coupling effect compared to uniform linear arrays (ULAs). These features are critical to improving the performance of DOA estimation and adaptive beamforming. In this paper, a new sparse array configuration based on the maximum inter-element spacing constraint (MISC) is proposed. The MISC array configuration generally consists of three sparse ULAs plus two separate sensors that are appropriately placed. The MISC array configurations are designed based on the inter-element spacing set, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive closed-form expressions for the number of uniform DOFs of the MISC arrays with any number of sensors. Compared with the existing sparse arrays, the MISC array enjoys two important advantages, namely, providing a higher number of DOFs and reducing the mutual coupling effects. Numerical simulations are conducted to verify the superiority of the MISC array over other sparse arrays.

Multiple-input multiple-output (MIMO) radar enjoys the advantage of increased degrees-of-freedom and spatial diversity gain, but it cannot effectively resolves the targets closely spaced in the same angle cell (but different range cells). Frequency diverse array (FDA)-MIMO radar can handle this problem by exploiting its range-dependent beampattern. FDA-MIMO radar was, thus, suggested for range-angle estimation of targets. Nevertheless, it is necessary to provide theoretical performance analysis for such a relatively new radar technique. Since multiple signal classification (MUSIC) algorithm is widely adopted in most of the FDA-MIMO literature, this paper derives the Cramér-Rao lower bound and mean square error expressions in MUSIC-based range-angle estimation algorithms for a general FDA-MIMO radar. Furthermore, the corresponding range and angle resolution thresholds in target detection and localization are also derived. Numerical results verify that the FDA-MIMO indeed outperforms conventional MIMO radar in both range-angle estimation and resolution threshold performance.

Different from phased array providing only angle‐dependent transmit beampattern, frequency diverse array (FDA) employs a small frequency increment across its array elements to provide range–angle‐dependent transmit beampattern. This enables the array beam to scan without the need of phase shifters or mechanical steering. Since FDA has received much attention in antenna and radar signal processing societies, it is necessary to make an overview on this interesting topic. This study introduces what FDA is and why it could be exploited for radar and navigation applications from a top‐level system description and appeal to the radar signal processing and system engineering communities for more investigations on this promising array technique. The status of FDA studies is overviewed and the most recent advances of FDA radar are discussed. The basic FDA system architectures are introduced, along with performance compared to a conventional phased‐array. Next, guidelines for choosing good system parameters and typical implementation schemes are provided. Finally, potential applications in range and angle estimation of targets, cognitive FDA radar and low probability of identification FDA radar are discussed, along with several technical challenges.

Different from conventional phased-array providing only angle-dependent beampattern, frequency diverse array (FDA) produces angle-range-dependent and time-variant transmit beampattern. Existing investigations show that FDA offers improved performance in interference suppression and target localization, but the time-variant beampattern will bring interferences to subsequent matched filtering. More seriously, the range-dependent signal phase may be canceled out in the filtering process. In fact, traditional single-channel receiver does not fully exploit the multicarrier feature in FDA signals. In this paper, we propose a multichannel matched filtering structure with considering the time-variance property for receiving pulsed-FDA signals. A coherent pulsed-FDA radar signal model to deal with the angle-range-dependent and time-variance problem is devised under additive colored Gaussian noise scenarios, followed by the corresponding waveform design principle. Moreover, closed-form expressions of the output signal-to-interference-plus-noise ratio and Cramér-Rao bounds for angle and range are derived. The proposed receiver design approach and corresponding theoretical performance derivations are verified by extensive numerical results.

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.

In this letter, we devise an efficient approach for estimating the directions-of-arrival (DOAs) of coherent signals using coprime arrays. Specifically, we firstly derive an augmented uniform linear array (ULA) via coprime array interpolation. Subsequently, we define a Toeplitz matrix that is formed using the correlation information of the interpolated ULA theoretical outputs, and recover the Toeplitz matrix by solving a nuclear norm minimization problem. After the low-rank Toeplitz matrix is recovered, the coherent signals are well resolved by the MUSIC algorithm. Simulation results demonstrate the advantages of the proposed