传感网无源目标跟踪与传感器位置联合校正研究

项目名称： 传感网无源目标跟踪与传感器位置联合校正研究

项目编号： No.61304264

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 杨乐

作者单位： 江南大学

项目金额： 24万元

中文摘要： 传感器网络无源目标跟踪是许多民用和军用系统的基本要素。在实际应用中，传感器位置信息往往含有误差，导致无源目标跟踪性能恶化；因此研究无源目标跟踪和传感器位置联合校正对改善目标跟踪精度和传感器位置信息具有重要意义。本项目针对时差频差无源跟踪系统，对如下问题开展研究：（1）从理论上分析传感器位置误差对时差频差无源跟踪精度的影响，分析其与跟踪体制、传感器布局、目标运动轨迹和观测量精度等因素的关系，从理论上论证目标无源跟踪和传感器位置联合校正的必要性；（2）针对仅使用时差和同时使用时差频差的系统设计有闭式解、复杂度低、收敛快的无源目标跟踪和传感器位置联合校新方法；（3）从理论上证明新算法的统计有效性，通过计算机和半实物仿真验证理论研究的正确性。本项目属于应用基础研究，将为无源目标跟踪和传感器位置联合校正的实际工程运用提供理论指导和有效算法，并为贝叶斯和卡尔曼滤波等状态估计理论的发展提供新的思路。

中文关键词： 时差频差；克莱默-劳下界；无源定位跟踪；传感器位置误差；低复杂度算法

英文摘要： Sensor network-based passive source tracking is essential for a variety of civil and military applications. In practice,the known sensor positions are almost inevitably subject to noises, which would significantly degrade the source tracking performance. Therefore, the investigation on the joint source tracking and sensor position calibration is of great importance. This proposal focuses on systems that utilize TDOA and FDOA measurements for passive source tracking. In particular, we shall investigate the following three aspects. First, we shall theoretically analyze the impact of sensor position errors on the source tracking performance. Its dependence on the positioning parameters,sensor array configuration and source motion trajectory will be established. We shall also show that ignoring the presence of sensor position errors would lead to severely degraded source tracking accuracy to motivate the study on the joint source tracking and sensor position calibration. Second,we shall develop novel joint source tracking and sensor position calibration methods that are in closed form, have low complexity and possess faster convergence to optimal performance than e.g., EKF. Third, we shall establish analytically the estimation error performance of the proposed algorithms and compare the results with the performance

英文关键词： TDOA and FDOA；Cramer-Rao lower bound；Passive localization and tracking；Sensor position uncertainty；Low-complexity algorithms