Power Allocation Strategies for Target Localization in Distributed Multiple-Radar Architectures

• Published in: IEEE transactions on signal processing Vol. 59; no. 7; pp. 3226 - 3240
• Main Authors: Godrich, H, Petropulu, A P, Poor, H V
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Allocations; Applied sciences; Architecture; Budgeting; Cramer-Rao bound (CRB); Detection, estimation, filtering, equalization, prediction; Estimation; Exact sciences and technology; Information, signal and communications theory; Localization; multiple-input multiple-output (MIMO) radar; multistatic radar; nonconvex optimization; Optimization; Parameter estimation; Position (location); Radar; Radar antennas; Radar cross section; Resource allocation; Resource management; Signal and communications theory; Signal, noise; Studies; target localization; Telecommunications and information theory; Performance evaluation; Parameter estimation; Non convex programming; Resource allocation; Decomposition method; nonconvex optimization; Power allocation; Optimization; Mean square error; Relaxation; Distributed processing; target localization; Localization; Target detection; Antenna; Cramer Rao inequality; MIMO system; multiple-input multiple-output (MIMO) radar; Cramer-Rao bound (CRB); Multistatic radar; Algorithm; Signal processing; Metric; Resource management; Domain decomposition
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2011.2144976

Widely distributed multiple radar architectures offer parameter estimation improvement for target localization. For a large number of radars, the achievable localization minimum estimation mean-square error (MSE), with full resource allocation, may extend beyond the predetermined system performance goals. In this paper, performance driven resource allocation schemes for multiple radar systems are proposed. All available antennas are used in the localization process. For a predefined estimation MSE threshold, the total transmitted energy is minimized such that the performance objective is met, while keeping the transmitted power at each station within an acceptable range. For a given total power budget, the attainable localization MSE is minimized by optimizing power allocation among the transmit radars. The Cramer-Rao bound (CRB) is used as an optimization metric for the estimation MSE. The resulting nonconvex optimization problems are solved through relaxation and domain decomposition methods, supporting both central processing at the fusion center and distributed processing. It is shown that uniform or equal power allocation is not necessarily optimal and that the proposed power allocation algorithms result in local optima that provide either better localization MSE for the same power budget, or require less power to establish the same performance in terms of estimation MSE. A physical interpretation of these conclusions is offered.