基于TDOAs与GROAs的多信号源被动定位

Passive Multiple Disjoint Sources Localization Using TDOAs and GROAs

为提高感知节点位置模糊条件下多目标被动定位结果精度,提出基于TDOAs与GROAs的混合定位代数闭式解算法,该算法联合估计未知信号源位置与带误差感知节点位置,利用TDOAs与GROAs所包含的相同感知节点位置误差信息提升定位精度,并推导得到基于TDOAs与GROAs多目标混合定位的克拉美罗下界(CRLB),仿真结果表明,所提算法能较好的达到CRLB,并且GROAs信息的引入给多目标定位精度带来明显性能提升.

We proposed a hybrid closed-form solution algorithm based on TDOAs and GROAs to improve multiple disjoint sources localization accuracy with erroneous sensor positions. The algorithm jointly estimates the unknown sources and sensor posi- tions,and then take the advantage that the TDOAs and GROAs from different sources have the same sensor position displacements to enhance the position accuracy. We also derived the Cramtr-Rao lower bound （CRLB） of multiple source location estimate using both TDOAs and GROAs. Simulations show that the proposed solution is able to reach the CRLB accuracy very well, and the local- ization accuracy improvements contributed by GROA measurements are significant.