Sequential Joint Detection and Estimation: Optimum Tests and Applications

TL;DR

This paper develops a joint sequential detection and estimation framework that optimally combines both tasks to minimize sample size while maximizing performance, outperforming traditional separate methods.

Contribution

It introduces a general Bayesian sequential approach for joint detection and estimation, deriving optimal coupled procedures and demonstrating their superiority over separate strategies.

Findings

Optimal joint detection and estimation significantly reduce sample size.

Coupled procedures outperform separate detection and estimation methods.

Numerical results confirm improved performance in practical models.

Abstract

We treat the statistical inference problems in which one needs to detect and estimate simultaneously using as small number of samples as possible. Conventional methods treat the detection and estimation subproblems separately, ignoring the intrinsic coupling between them. However, a joint detection and estimation problem should be solved to maximize the overall performance. We address the sample size concern through a sequential and Bayesian setup. Specifically, we seek the optimum triplet of stopping time, detector, and estimator(s) that minimizes the number of samples subject to a constraint on the combined detection and estimation cost. A general framework for optimum sequential joint detection and estimation is developed. The resulting optimum detector and estimator(s) are strongly coupled with each other, proving that the separate treatment is strictly sub-optimum. The theoretical results derived for a quite general model are then applied to several problems with linear quadratic Gaussian (LQG) models, including dynamic spectrum access in cognitive radio, and state estimation in smart grid with topological uncertainty. Numerical results corroborate the superior overall detection and estimation performance of the proposed schemes over the conventional methods that handle the subproblems separately.