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.

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).

Defect detection holds significant importance in improving the overall quality of fabric manufacturing. To improve the effectiveness and accuracy of fabric defect detection, we propose the PRC-Light YOLO model for fabric defect detection and establish a detection system. Firstly, we have improved YOLOv7 by integrating new convolution operators into the Extended-Efficient Layer Aggregation Network for optimized feature extraction, reducing computations while capturing spatial features effectively. Secondly, to enhance the performance of the feature fusion network, we use Receptive Field Block as the feature pyramid of YOLOv7 and introduce Content-Aware ReAssembly of FEatures as upsampling operators for PRC-Light YOLO. By generating real-time adaptive convolution kernels, this module extends the receptive field, thereby gathering vital information from contexts with richer content. To further optimize the efficiency of model training, we apply the HardSwish activation function. Additionally, the bounding box loss function adopts the Wise-IOU v3, which incorporates a dynamic non-monotonic focusing mechanism that mitigates adverse gradients from low-quality instances. Finally, in order to enhance the PRC-Light YOLO model’s generalization ability, we apply data augmentation techniques to the fabric dataset. In comparison to the YOLOv7 model, multiple experiments indicate that our proposed fabric defect detection model exhibits a decrease of 18.03% in model parameters and 20.53% in computational load. At the same time, it has a notable 7.6% improvement in mAP.

The optimal resource sharing is key to delivering the promise of radar-communication systems, since the spectrum is shared while competed to perform radar and communication tasks. In this paper, a joint target and user assignment as well as dwell time and spectrum allocation strategy is proposed for the distributed radar-communication coexistence (RCC) network. The optimization model is formulated as minimizing the sum of weighted position Bayesian Cramér-Rao lower bounds (BCRLBs) under the dwell time and spectrum budgets while meeting the communication downlink requirements. The optimization model is shown to be a mixed integer programming problem, where the binary assignment variables and continuous resource allocation variables are coupled in both objective function and constraints. A three-stage alternate optimization method (TSAOM) is developed for problem-solving. The target assignment and dwell time allocation (TADTA) problem and user assignment and spectrum allocation (UASA) problem are separately relaxed as convex ones. Then, a cyclical minimizer framework is applied for the suboptimal solution. Simulations confirm the tracking performance improvement of the proposed strategy, compared with two baseline allocation strategies. The results also show the effectiveness and efficiency of the developed TSAOM in comparison with the baseline algorithm.

The increasingly congested frequency spectrum necessitates efficient resource allocation in radar-communication coexistence (RCC) networks. This paper proposes a power-saving-based joint beam-target assignment (BTA), communication-user association (CUA), and power allocation strategy to minimize total power consumption while meeting radar tracking accuracy and communication quality requirements. The posterior Cramér-Rao lower bound (PCRLB) and communication data ratio (CDR) are employed to quantify radar estimation performance and communication efficiency, respectively. To address the NP-hard mixed-integer programming problem, we develop a two-stage cyclically iterative expansion optimization method. This method iteratively expands feasible BTA and CUA domains via convex relaxation while solving power allocation through convex optimization, yielding a suboptimal solution with reduced complexity. Simulations demonstrate that the proposed strategy achieves over 17% lower power consumption compared to state-of-the-art benchmarks while ensuring PCRLB and CDR thresholds. In addition, the framework dynamically prioritizes targets and users with optimal channel conditions, enhancing resource utilization efficiency. The results validate the method's superiority in balancing computational efficiency and performance for extensive simulations.

Existing direction of arrival (DOA) estimation methods in multiple-input multiple-out (MIMO) radar systems will encounter the performance degradation in the cases of few snapshots, low signal-to-noise ratio (SNR), closely spaced targets, or strongly correlated sources. To improve it, this paper develops a new sparse representation-based DOA estimation method. The main contributions are as follows: i) we construct a new real-valued double weighted $l_{2,1}$-norm minimisation model; ii) we derive an improved reduced-dimension technique to enhance estimation accuracy; and iii) we design optimal and sparse weights carefully to improve the corresponding estimation accuracy. Finally, the effectiveness and theoretical analysis of the presented approach are verified by extensive numerical simulations, which proves that the new algorithm performs well at low SNR and with a small number of snapshots as well as at the coherent source case.