We study the problem of simultaneous search for multiple targets over a multidimensional unit cube and derive the fundamental resolution limit of non-adaptive querying procedures using the 20 questions estimation framework. The performance criterion that we consider is the achievable resolution, which is defined as the maximal $L_\infty$ norm between the location vector and its estimated version where the maximization is over the possible location vectors of all targets. The fundamental resolution limit is then defined as the minimal achievable resolution of any non-adaptive query procedure. We drive non-asymptotic and second-order asymptotic bounds on the minimal achievable resolution by relating the current problem to a data transmission problem over a multiple access channel, using the information spectrum method by Han and borrowing results from finite blocklength information theory for random access channel coding. Our results extend the purely first-order asymptotic analyses of Kaspi \emph{et al.} (ISIT 2015) for the one-dimensional case. Specifically, we consider more general channels, derive the non-asymptotic and second-order asymptotic results and establish a phase transition phenomenon.
翻译:我们用20个问题估计框架研究在多维单元立方体上同时搜索多个目标的问题,并得出非适应性查询程序的基本解析限度。我们所考虑的性能标准是可实现的解析度,即定位矢量与其估计版本之间的最大值$L ⁇ infty$标准,即所有目标的可能位置矢量的最大值最大化。基本解析限度随后被定义为任何非适应性查询程序的最低限度可实现解析度。我们驱动非被动和二阶无序自定界限,将当前问题与多访问频道的数据传输问题联系起来,使用汉的信息频谱法,并借用随机访问通道编码的有限区段信息理论的结果。我们的结果扩大了对一维案例的纯一阶单线作为抽取分析的范围。具体地说,我们考虑更一般的渠道,得出非补救性和二阶值,并确立一个阶段过渡现象。