This paper considers the problem of acquiring an unknown target location
(among a finite number of locations) via a sequence of measurements, where each
measurement consists of simultaneously probing a group of locations. The
resulting observation consists of a sum of an indicator of the target's
presence in the probed region, and a zero mean Gaussian noise term whose
variance is a function of the measurement vector. An equivalence between the
target acquisition problem and channel coding over a binary input additive
white Gaussian noise (BAWGN) channel with state and feedback is established.
Utilizing this information theoretic perspective, a two-stage adaptive target
search strategy based on the sorted Posterior Matching channel coding strategy
is proposed. Furthermore, using information theoretic converses, the
fundamental limits on the target acquisition rate for adaptive and non-adaptive
strategies are characterized. As a corollary to the non-asymptotic upper bound
of the expected number of measurements under the proposed two-stage strategy,
and to non-asymptotic lower bound of the expected number of measurements for
optimal non-adaptive search strategy, a lower bound on the adaptivity gain is
obtained. The adaptivity gain is further investigated in different asymptotic
regimes of interest.

本文研究了通过一系列测量获取未知目标位置（在有限个位置中）的问题，并建立了目标获取问题和二进制输入加性白噪声信道上的信道编码之间的等价性，提出了一种基于排序后的后验匹配信道编码策略的两阶段自适应目标搜索策略。通过信息论会话进一步研究了自适应和非自适应策略的基本限制，提出了适应性增益的下限。