Distributed Estimation Over Sensor Networks Based on Distributed Conjugate Gradient Strategies

This paper presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional conjugate gradient (CCG) and modified conjugate gradient (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least–mean square (LMS)–based algorithms and a performance that is close to recursive least–squares (RLS) algorithms. In comparison with existing centralized or distributed estimation strategies, key features of the proposed algorithms are: 1) more accurate estimates and faster convergence speed can be obtained and 2) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications.