Radar sensor network resource allocation for fused target tracking: A brief review

The design of irregular subarrayed planar phased arrays composed of modular tiles is a research area of increasing interest. In this article, a heuristic iterative convex relaxation programming (H-ICRP) framework is proposed. By formulating the tiling of irregular subarray as a binary programming problem, this framework is suitable for arbitrary array aperture and subarray structure. To achieve exact aperture cover, the nonconvex binary constraint is relaxed to a convex approximation and solved through multiple iterations. Furthermore, for better performance of the scanning radiation pattern, the multitask Bayesian compressive sensing (MT-BCS) solver is adopted to find a heuristic initial iteration point instead of randomizing, such that the robustness and effectiveness can be improved remarkably. Good performance and potentialities of the proposed method are verified by comparing with existing approaches in various numerical examples.

Irregular Subarray Tiling via Heuristic Iterative Convex Relaxation Programming

The low-cost sensor array, an effective way to provide sufficient degrees of freedom (DoFs) and affordable costs, is one of the most important foundation devices for large-scale applications of the Internet of Things (IoT) and sixth-generation (6G) spaceborne communication systems. In this article, we propose an irregular subarray-based low-cost sensor array design strategy, which decomposes the practical application problem into two subproblems according to the scene scale. For the small-size cases, a user-defined DoF control strategy is presented, which is flexibly defined and establishes an effective tradeoff between the sensor array radiation performance and manufacturing cost. Moreover, for large-size cases, a hierarchical subarray design strategy is presented. A module panel structure composed of subarray tiles is proposed to avoid both the convergence problem in high-dimensional optimization and engineering complexity. Both design strategies are able to ensure good sensor radiation performance while maintaining engineering convenience. The effectiveness and potential of the proposed method are verified by various numerical examples.

Low-Cost Subarrayed Sensor Array Design Strategy for IoT and Future 6G Applications

The synthesis of irregular phased arrays subject to constraint on directivity is fulfilled by convex optimization. In particular, based on the membership between the antenna elements and subarrays, the dictionary matrix, the resulting sparse excitation vector, and the sparse binary vector are introduced to describe the array pattern and exact tiling of the aperture. Therefore, the synthesis of irregular phased arrays without overlaps and holes satisfying given maximum sidelobe level (SLL) and minimum directivity can be summarized as a mixed integer nonconvex programming problem, where both the subarray tiling configurations and the associated complex excitations are optimized simultaneously. To avoid the nonconvexities, some mathematical transformations in terms of directivity are implemented and the binary sparse vector is relaxed to be a real sparse vector by the minimization of the weighted $l_{1}$ -norm. Consequently, the nonconvex problem is reduced to iterative convex optimization problems, where several convex optimization problems are included and can be efficiently solved using convex optimization solver. The excellent performances of the proposed method are verified by comparing with previously reported methods through several numerical examples.

Synthesis of Irregular Phased Arrays Subject to Constraint on Directivity via Convex Optimization

A high-directivity optimization technique for irregular arrays combined with maximum entropy model is presented. First, the major cause of the reduction in directivities of irregular arrays has been analyzed, namely, the so-called relative wave path difference. The larger size of subarrays and the wider scanning angle will inevitably enlarge relative wave path differences, thus resulting in the reduction of directivity. Aimed at this challenging issue, a mixed integer linear programming model is proposed to achieve high directivity and low sidelobe simultaneously for tetromino-shaped irregular arrays. An embedded structure algorithm has been applied to efficiently optimize a large-scale array with 2-D 60° maximum scanning angles. Moreover, representative numerical examples based on Vivaldi finite arrays are presented to demonstrate the effectiveness of the proposed approach. Numerical results show that the directivity of an optimized array by the proposed method outperforms those of the reported state-of-the-art methods by about 1 dB for the scan to 45°.

High-Directivity Optimization Technique for Irregular Arrays Combined With Maximum Entropy Model

Subarray design technique is widely employed in the design of planar phased arrays. However, it is hard to obtain full coverage and good radiation performance with the given shape of subarrays and array aperture. In this letter, the subarray design problem is formulated as an iterative convex relaxation model and solved using some steps subtly. Besides, the scanning sidelobe level is adopted to retrieve the optimal configuration among the found solutions. Numerical experiments are carried out to assess the performance of the proposed method as well as the inherent superiority in adaptive beamforming.

Modular Subarrayed Phased-Array Design by Means of Iterative Convex Relaxation Optimization

For a dwell time limited radar network, the achievable tracking performance can be further enhanced by adaptively tuning in the dwell time allocation strategy to accommodate the future operation scenario. In this paper, considering this dynamical resource allocation problem, a cone programming-based dwell time allocation scheme is proposed for achieving the optimal tracking performance. The basic mechanism is to exploit the feedback of its operating environment based on the interrogation of received echoes for informing the decision of future optimal dwell time allocation. To achieve this purpose, the predicted conditional Cramer-Rao lower bound (PC-CRLB) of target state estimates is developed for evaluating the tracking performance of each candidate allocation strategy conditioned on the cognitive knowledge firstly. And, its trace is chosen as a scalar allocation strategy evaluation metric. On this basis, the closed-form expression of the scalar evaluation metric is derived, which enables us to transform the dwell time allocation problem into the second-order cone program (SOCP). Extensive simulations demonstrate the efficiency and superiority of the proposed allocation scheme.

Cognitive Dwell Time Allocation for Distributed Radar Sensor Networks Tracking via Cone Programming

In this letter, a general framework of cognitive resource allocation for target tracking in radar networks is proposed. Firstly, an allocation strategy evaluation metric is established in this framework, i.e., the predicted conditional Cramer-Rao lower bound (PC-CRLB), for evaluating the future response of each candidate allocation strategy. Then, the optimal resource allocation strategy can be found by solving a constrained optimization problem. To specify the framework implementation details, a dwell time allocation problem is considered. For this given problem, the analytical expressions of PC-CRLB and its derived scalar strategy evaluation metric are derived, enabling us to convert the allocation problem to a second-order cone program (SOCP). Numerical results demonstrate the effectiveness of the proposed framework.

Cognitive Resource Allocation for Target Tracking in Location-Aware Radar Networks

In this paper, the reliability of irregular subarrayed phased array antenna is analyzed. In order to access the performance of this kind of array structure, the failure rate of subarray tiles is simulated. Moreover, the low sidelobe tapering beam pattern degradation case is also proposed. Numerical results are presented to validate the effectiveness.

Luo-Sheng-Bin Wang

Papers

Irregular Subarray Tiling via Heuristic Iterative Convex Relaxation Programming

Modular Subarrayed Phased-Array Design by Means of Iterative Convex Relaxation Optimization

Cognitive Dwell Time Allocation for Distributed Radar Sensor Networks Tracking via Cone Programming

Cognitive Resource Allocation for Target Tracking in Location-Aware Radar Networks

Array Reliability Analysis for Irregular Subarrayed Phased Array Antenna