Optimality analysis of sensor-target localization geometries
Optimality analysis of sensor-target localization geometries
复制标题
DOI：
10.1016/j.automatica.2009.12.003
复制
发表时间：
2010-03-01
期刊：
影响因子：
6.4
通讯作者：
Pathirana, Pubudu N.
中科院分区：
文献类型：
--
作者：
Bishop, Adrian N.;Fidan, Baris;Pathirana, Pubudu N.
目标定位问题涉及到从多个噪声传感器测量中估计目标的位置。众所周知，传感器-目标的相对几何形状会显著影响任何特定定位算法的性能。定位性能可以通过某些度量来明确表征，例如，通过估计量方差的Cramer-Rao下界（等于逆Fisher信息矩阵）。此外，通常使用Cramer-Rao下界来生成所谓的不确定性椭圆，该椭圆表征有效估计的空间方差分布，即达到下界的估计。这项工作的目的是确定那些相对的传感器-目标几何形状，导致测量的不确定椭圆被最小化。考虑到这些传感器目标几何形状相对于所选择的测量是最优的，本文确定并研究了仅距离定位、基于到达时间定位和仅方位定位的最优传感器目标几何形状。对任意数量传感器的最优几何形状进行了识别，并表明最优传感器-目标结构通常不是唯一的。通过形式分析结果和一些说明性示例，强调了理解传感器-目标几何形状对潜在定位性能的影响的重要性。2009爱思唯尔有限公司版权所有。
The problem of target localization involves estimating the position of a target from multiple noisy sensor measurements. It is well known that the relative sensor-target geometry can significantly affect the performance of any particular localization algorithm. The localization performance can be explicitly characterized by certain measures, for example, by the Cramer-Rao lower bound (which is equal to the inverse Fisher information matrix) on the estimator variance. In addition, the Cramer-Rao lower bound is commonly used to generate a so-called uncertainty ellipse which characterizes the spatial variance distribution of an efficient estimate, i.e. an estimate which achieves the lower bound. The aim of this work is to identify those relative sensor-target geometries which result in a measure of the uncertainty ellipse being minimized. Deeming such sensor-target geometries to be optimal with respect to the chosen measure, the optimal sensor-target geometries for range-only, time-of-arrival-based and bearing-only localization are identified and studied in this work. The optimal geometries for an arbitrary number of sensors are identified and it is shown that an optimal sensor-target configuration is not, in general, unique. The importance of understanding the influence of the sensor-target geometry on the potential localization performance is highlighted via formal analytical results and a number of illustrative examples. (C) 2009 Elsevier Ltd. All rights reserved.