Convergence of a Maximum-Likelihood Parameter-Estimation Algorithm for DS/SS Systems in Time- Varying Channels With Strong Interference

An unbiased, maximum-likelihood (ML), channel parameter-estimation algorithm for direct-sequence spread-spectrum systems with strong interference is discussed in this paper. The algorithm includes correcting terms to the extended Kalman filter (EKF) based on the gradient of the negative log-likelihood function of the output of a conventional matched filter. By an asymptotic analysis, the algorithm is shown to determine the actual parameters. A complete implementation of the algorithm is given, and its transient behavior is examined by computer simulations. Results show that ML algorithm, albeit optimal in the sense of unbiased parameter estimation, is less robust than the modified EKF described in our first reference.