Search

The integrated dual-function design is addressed in a Frequency Diverse Array-Phased Multiple-Input Multiple-Output (FDA-PMIMO) radar. Firstly, the transmit array is partitioned into multiple subarrays, where a tiny frequency shift is imported across different subarrays, while an identical carrier frequency is adopted within each subarray. At the design stage, the system parameters at the transmit side are optimized to accomplish joint range-angle estimation and mainlobe deceptive jammer suppression. Specifically, the subarray number and transmit weight matrix are devised to minimize the Cramér-Rao Lower Bounds (CRLBs) for estimation. In addition, the subarray number is optimized to maximize the Signal-to-Jammer-plus-Noise Ratio (SJNR) for jammer suppression. Subsequently, with the optimized architectures, the joint range-angle estimation is achieved based on the Maximum Likelihood (ML) criterion, and the jammers are suppressed via joint transmit-receive beamforming. At the analysis stage, the Root Mean Square Error (RMSE) as well as the output SJNR are analyzed to evaluate the performances of estimation and jammer suppression, respectively. Numerical results corroborate the effectiveness of the integrated dual-function design with the developed methods.

The problem of robust mainlobe deceptive jammer suppression in an element-pulse coding-multiple-input–multiple-output radar is explored utilizing data-dependent beamforming. In general, by introducing a tunable coding coefficient in both transmit elements and pulses, the response of mainlobe deceptive jammers can be identified as signals coming from different range ambiguity regions in the joint transmit–receive domain, and further mitigated through minimum variance distortionless response criterion. In specific, to address the problems of insufficient snapshots and polluted samples enrolled in data-dependent beamforming, a robust beamforming approach utilizing the two-dimensional atomic-norm minimization is proposed to estimate the steering vector corresponding to the signal of interest and to reconstruct the interference-plus-noise covariance matrix. In this regard, to reduce the computational burden, the alternating direction method of multipliers is utilized to obtain both the true target's angle error and the jammer-plus-noise subspace. In addition, a series of theoretical proofs including equivalent semidefinite program transformation, and the optimality conditions are provided. The validity of the proposed mainlobe deceptive jammer mitigation approach is confirmed through numerical results.

Space-time adaptive processing (STAP) is significant for detecting moving targets with phased array (PA) and frequency diverse array (FDA). However, the parameter estimation will be affected by the residual clutter. To mitigate this issue, this article puts forward an enhanced moving target parameter estimation method for STAP-based PA-FDA dual-mode radar, where dual-mode radar contains several advantages: it has simultaneously narrow-beam tracking and wide-beam searching capabilities and it can resolve range ambiguity problem with FDA mode and improve target detection performance with PA mode. With the dual-mode cooperative design, an accurate clutter covariance matrix is reconstructed to calculate weights for the maximum likelihood (ML) parameters estimator, where range-ambiguous clutter is considered. The Taylor series expansion of the ML estimator is developed to mitigate the problem of biased parameter estimation caused by residual clutter after suppression. Moreover, enhanced unambiguous range estimation is proposed with PA-FDA. Overall, the proposed method with PA-FDA enhances the parameters estimation performance of moving target. Simulation experiments are employed to demonstrate the validity of the proposed approach.

With the rapid development of remote sensing (RS) technology, high-resolution RS image change detection (CD) has been widely used in many applications. Pixel-based CD techniques are maneuverable and widely used, but vulnerable to noise interference. Object-based CD techniques can effectively utilize the abundant spectrum, texture, shape, and spatial information but easy-to-ignore details of RS images. How to combine the advantages of pixel-based methods and object-based methods remains a challenging problem. Besides, although supervised methods have the capability to learn from data, the true labels representing changed information of RS images are often hard to obtain. To address these issues, this article proposes a novel semisupervised CD framework for high-resolution RS images, which employs small amounts of true labeled data and a lot of unlabeled data to train the CD network. A bihierarchical feature aggregation and extraction network (BFAEN) is designed to achieve the pixelwise together with objectwise feature concatenation feature representation for the comprehensive utilization of the two-level features. In order to alleviate the coarseness and insufficiency of labeled samples, a confident learning algorithm is used to eliminate noisy labels and a novel loss function is designed for training the model using true- and pseudo-labels in a semisupervised fashion. Experimental results on real datasets demonstrate the effectiveness and superiority of the proposed method.

This paper is focused on the problem of resolving the range ambiguity in a fixed phase coding-phased array (FPC-PA) radar. At the modeling stage, the FPC-PA radar is configured by a fixed phase coding in both transmit elements and pulses, where the transmit beam is modulated with a coding phase in each pulse. Then, after some appropriate signal processing procedures, the degrees-of-freedom (DoFs) in the transmit domain is obtained, and the range region numbers, namely the index of the range ambiguity region, are discriminated in the transmit spatial frequency domain. Particularly, echoes from different range ambiguity regions correspond to different pointing directions of the equivalent transmit beampattern. Moreover, in order to obtain the range ambiguity index and angle of the target, a three-stage algorithm is developed to enhance the performance against the effect of amplitude and phase errors. At the analysis stage, the parameter estimation performance is assessed in comparison with the Cramér-Rao lower bound (CRLB). Experimental and numerical results are provided to demonstrate the effectiveness of resolving range ambiguity and parameter estimation with the proposed method.