One-Bit Recursive Least-Squares Algorithm With Application to Distributed Target Localization

Adaptation and learning over low-cost wireless networks, meanwhile keeping an acceptable performance, are well motivated. This paper focuses on online parameter estimation over binary networks, which consist of noisy low-resolution sensors, each only giving coarsely one-bit quantized output observations and transmitting them to a fusion center. We develop a class of recursive least-squares (RLS) algorithms based on an expectation-maximization framework, which realizes adaptive parameter estimation from one-bit observations of the noisy output stream. The developed algorithms are, respectively, derived with and without prior knowledge of the noise variances, and their performances are theoretically and experimentally evaluated. Moreover, it is shown that, although the information contained in the one-bit observations is very limited, the proposed algorithms are comparable to the classical RLS algorithm using the original (nonquantized) observations. In addition, as a practical application, the proposed algorithm combined with array signal processing techniques is applied to bearing-only target localization over wireless sensor array networks, and its effectiveness is verified through simulation experiments.