A fast efficient power allocation algorithm for target localization in cognitive distributed multiple radar systems

• Published in: Signal processing 2016-10, Vol.127, p.100-116
• Main Authors: Feng, Han-Zhe, Liu, Hong-Wei, Yan, Jun-Kun, Dai, Feng-Zhou, Fang, Ming
• Format: Article
• Language: English
• Subjects: Algorithms; Allocations; Alternating global search algorithm; Budgeting; Cognitive distributed multiple radar systems; Convergence; Cramér–Rao low lound; Domain decomposition methods; Localization; Optimization; Pareto optimal set; Pareto optimality; Power allocation algorithm; Radar
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2015.12.022

It is well-known that the power allocation can enhance the power utilization of the distributed radar systems. We first analyze two interesting non-increasing properties of Cramér–Rao low bound (CRLB) for target location via distributed multiple radar systems. On the basis of the classical power allocation methods [15], this paper proposes a fast efficient power allocation algorithm applied to cognitive distributed multiple radar systems, which depends greatly on an alternating global search algorithm(AGSA). In this paper, our aim is directly to minimize the non-convex CRLB [15] of target location estimation. The convergence of the proposed algorithm is theoretically analyzed by LaSalle invariance principle. We analyze the computational complexity of the two closely-related algorithms. The famous Pareto optimal set associated with power allocation is obtained by the proposed algorithm, and it can indirectly derive the solution to problem for minimizing total power budget. Experimental results demonstrate that our algorithm has quick convergence and good performance.