Joint Multiple Disjoint Sources and Sensors Localization Based on TDOAs and FDOAs

HAO Ben-jian; ZHU Jian-feng; LI Zan; XIAO Song; GUAN Lei; WAN Peng-wu

doi:10.3969/j.issn.0372-2112.2015.10.002

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Joint Multiple Disjoint Sources and Sensors Localization Based on TDOAs and FDOAs

更新时间：2025-07-16

- Joint Multiple Disjoint Sources and Sensors Localization Based on TDOAs and FDOAs
- Acta Electronica Sinica Vol. 43, Issue 10, Pages: 1888-1897(2015)
- 作者机构：
  
  1. 西安电子科技大学ISN国家重点实验室,陕西,西安,710071
  2. 通信信息控制和安全技术重点实验室,浙江,嘉兴,314033
  3. 西安电子科技大学ISN国家重点实验室,陕西,西安,710071
  4. 通信信息控制和安全技术重点实验室,浙江,嘉兴,314033
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61401323, No.61301179);China Postdoctoral Science Foundation (No.2014M550479);Fundamental Research Funds for Xidian University (No.7214605403, No.7215605401)
- DOI：10.3969/j.issn.0372-2112.2015.10.002
  CLC： TN911.23
- Published：2015
- 稿件说明：
移动端阅览
HAO Ben-jian, ZHU Jian-feng, LI Zan, et al. Joint Multiple Disjoint Sources and Sensors Localization Based on TDOAs and FDOAs[J]. Acta Electronica Sinica, 2015, 43(10): 1888-1897.
DOI：

HAO Ben-jian, ZHU Jian-feng, LI Zan, et al. Joint Multiple Disjoint Sources and Sensors Localization Based on TDOAs and FDOAs[J]. Acta Electronica Sinica, 2015, 43(10): 1888-1897. DOI： 10.3969/j.issn.0372-2112.2015.10.002.

摘要

感知节点存在位置误差与速度误差的情况下

本文采用信号到达时间差(TDOAs)与到达频率差(FDOAs)信息

针对多信号源被动定位与感知节点进行位置及速度的同步优化问题进行了研究.在Sun和Ho前期工作中

只对多个不相关信号源进行了定位

并没有同时给出感知节点位置及速度的优化解

在很多实际应用中

对多个信号源进行被动定位的同时需要对感知节点的位置及速度信息进行优化;本文所提出的算法对前期算法进行了提升

被定位信号源与感知节点的位置及速度可同时较好地达到克拉美罗下界(CRLB);计算机仿真对本文理论推导进行了验证.

Abstract

In the presence of sensor position and velocity errors

this paper considers the problem of simultaneously locating multiple disjoint sources and refining erroneous of sensor positions and velocities using time differences of arrival (TDOA) and frequency differences of arrival (FDOA).The previous work by Sun and Ho to solve this problem provided an efficient estimator for multiple disjoint sources

but it cannot provide optimum accuracy for the sensor positions and sensor velocities.In many practical applications

it's necessary and helpful to refine sensor locations and velocities while localizing multiple sources.The proposed method improves the previous method so that both the source and the sensor position and velocity estimates can achieve the Cramr-Rao lower bound (CRLB) accuracy very well over small noise region.The theoretical derivation is corroborated by simulations.

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