Controlled Sensing for Sequential Multihypothesis Testing with Controlled Markovian Observations and Non-Uniform Control Cost

TL;DR

This paper introduces a new controlled sensing model with complex memory and flexible cost structures for multihypothesis testing, proposing asymptotically optimal and risk-constrained tests with self-tuning control policies.

Contribution

It develops a novel controlled Markovian observation model with general cost functions and proposes asymptotically optimal and risk-constrained testing procedures.

Findings

Proposed an asymptotically optimal test for the new model.

Identified a self-tuning optimal control policy maximizing an inferential reward.

Designed a risk-constrained test that is asymptotically optimal.

Abstract

A new model for controlled sensing for multihypothesis testing is proposed and studied in the sequential setting. This new model, termed {\em controlled Markovian observation} model, exhibits a more complicated memory structure in the controlled observations than existing models. In addition, instead of penalizing just the delay until the final decision time as in standard sequential hypothesis testing problems, a much more general cost structure is considered which entails accumulating the total control cost with respect to an arbitrary control cost function. An asymptotically optimal test is proposed for this new model and is shown to satisfy an {\em optimality} condition formulated in terms of decision making risk. It is shown that the optimal causal control policy for the controlled sensing problem is self-tuning, in the sense of maximizing an inherent "inferential" reward simultaneously under every hypothesis, with the maximal value being the best possible corresponding to the case where the true hypothesis is known at the outset. Another test is also proposed to meet {\em distinctly predefined} constraints on the various decision risks {\em non-asymptotically,} while retaining asymptotic optimality.