With the increasing of commercial communication applications, the scarcity of spectrum resources is becoming obvious, and the joint radar and communication (JRC) systems have been attracted much attention. The resource allocation technique is crucial for mitigating mutual interference and enhancing radar sensing and communication performance in JRC systems. A strategy for joint target and user assignment, power allocation, and subchannel allocation (JCAPASA) in an unmanned aerial vehicle (UAV)-based radar and communication coexistence (RCC) network is proposed. The predicted conditional Cramér-Rao lower bound (PC-CRLB) incorporating uncertainty of sensor location (USL) is derived and utilized as the optimization metric. The optimization model aims to minimize the sum of weighted PC-CRLBs for multiple targets while adhering to constraints, such as communication data ratio (CDR) requirements, beam assignment, power budget, and subchannel nonoverlap. The optimization model is nonconvex and falls into the NP-hard class. An iterative descent-based optimization method (IDBOM) is proposed for its solution. The simulation results confirm the efficiency and effectiveness of the proposed strategy compared to state-of-the-art methods. The results also imply that the proposed strategy comprehensively considers spatial diversity and power budget to maximize tracking performance while meeting the CDR requirements.

A method of motion status estimation of airborne synthetic aperture radar (SAR) platform in short subapertures via parametric sparse representation is proposed for high-resolution SAR image autofocusing. The SAR echo is formulated as a jointly sparse signal through a parametric dictionary matrix, which converts the problem of SAR motion status estimation into a problem of dynamic representation of jointly sparse signals. A full synthetic aperture is decomposed into several subapertures to estimate the dynamic motion parameters of a platform, and SAR motion compensation is achieved by refining the estimation of the equivalent platform motion parameters, i.e., the azimuth velocity and the radial acceleration of the radar platform, at each subaperture in an iterative fashion. Experimental results based on both simulated and real data demonstrate that: 1) the proposed algorithm outperforms the map-drift algorithm and the phase gradient autofocus algorithm in terms of the imaging quality and 2) compared to the iterative minimum-entropy autofocus, the proposed algorithm produces the comparative imaging quality with less computational complexity in complex motion environment.

The micro‐Doppler (m‐D) features induced by targets with micro‐motions provide important information for automatic radar target recognition. In this study, the m‐D effect induced by rotational micro‐motion in wideband radar is analysed, and an algorithm for the extraction of these m‐D features is proposed. By making use of the amplitude and phase information of ‘range‐slow‐time image’, a dictionary with m‐D signal atoms is constructed in the complex image space. The orthogonal matching pursuit algorithm in vector space is then extended to the complex image space to decompose the range‐slow‐time image and to extract the m‐D features of the target. The proposed algorithm can extract the m‐D features in the presence of migration through range cells of micro‐motional scatterers, and can also work well when the sampling rate in slow‐time domain is lower than the Nyquist sampling rate. Simulations are given to validate the effectiveness and robustness of the proposed method.

Sparse representation (SR) based SAR imaging approaches have shown their superior performance and great potential in compressed sensing (CS) SAR imaging field. However, for many existing approaches, the reconstruction accuracy may be affected by inexact observation and low radar sampling ratio. Inspired by sparse representation works in image restoration, we proposed a novel centralized sparse representation (CSR) based SAR imaging approach. We assume that the imaging result is the degraded (noisy or inaccurate reconstructed) version of the true scattered field. So, the aim of reconstruction is to make the sparse coding coefficients of imaging result as close as possible to the coefficients of the true scattered field. Under this assumption, a centralized sparsity constraint is added into the reconstruction framework to shrink the difference between the coefficients of imaging result and true scattered field and hence make the sparse coding more accurate. To solve the joint optimization problem between updating sparse coding vectors and SAR imaging, a joint optimization method using Lq (0<q≤1) regularization is proposed. Experimental results have proven to demonstrate the validity of the proposed approach.

Uncrewed aerial vehicle (UAV)-assisted joint radar and communication (JRC) systems are widely adopted in Internet of Things applications. This is due to their convenience, affordability, and space and resource-saving. A joint power, bandwidth, and subchannel allocation (JPBSA) strategy is proposed for a UAV-assisted dual-function radar and communication (DFRC) network. The predicted posterior Cramér-Rao lower bound (PCRLB) is utilized to measure the target tracking accuracy. The optimization model is established as minimizing the sum of weighted predicted PCRLBs while meeting the communication data ratio (CDR) requirements, total power and bandwidth budget. It is shown that the JPBSA problem involves a mixed-integer programming (MIP) problem. Even worse, the bandwidth and subchannel are coherent. A three-stage alternating optimization method (TSAOM) is constructed for the solution. By integrating the radar and communication power allocation as a whole, the alternating ascent-descent method (AADM) is employed to solve the power allocation. Then, two propositions are proposed to provide the upper bounds for bandwidth allocation. Finally, the subchannel allocation is solved using a greedy search-based method. Simulation results confirm the effectiveness and efficiency of the proposed method, compared with the state-of-the-art methods. It also shows that using the PCRLB as the optimization metric is better than the signal-to-interference-plus-noise ratio (SINR) and mutual information (MI).

Compressed sensing (CS) reconstruction of nonsparse scenes is one of the difficulties in synthetic aperture radar (SAR) imaging technology. Although the conventional CS method with sparse representation has proven applicable for nonsparse SAR reconstruction, its disadvantages are unsatisfactory imaging quality and high computational complexity under downsampling. In this letter, a novel deep learning approach for nonsparse SAR scene reconstruction is proposed based on sparse representation and the iterative shrinkage threshold algorithm (ISTA). Specifically, we first develop a sparse-representation-based imaging model associated with the $\ell _{1}$ sparse regularizer in nonlinear transform domains. Then, the advantages of the recurrent neural network (RNN) and convolutional neural network (CNN) are incorporated into an ISTA-inspired deep unfolded network (DUN) called SR-ISTA-Net, in which all the parameters are layer-varied rather than handcrafted. The experiments verify that the proposed SR-ISTA-Net can provide high-quality reconstruction results under nonsparse scenes while substantially reducing the imaging time.