基于线性数据融合的双椭圆定位模型优化算法

为了充分利用分离式台声源声纳定位系统的冗余测量信息,提高系统的定位精度,提出了基于线性数据融合的双椭圆定位模型优化算法。并通过数值仿真,对不同条件下的定位误差几何分布曲线图(GDOP)进行了研究。仿真结果表明,大部分探测范围内,两个声源之间距离的变化和时间测量误差对定位性能影响较小,而角度测量误差对该算法的定位精度影响较大。与仅利用距离信息进行定位的方法相比较,采用数据融合的优化手段,可以充分利用定位系统的冗余信息,有效地降低了系统的定位误差,探测盲区的定位性能也得到明显改善。仿真结果和性能分析为解决远距离探潜定位优化问题提供了理论依据。

Research on the Optimized Algorithm of Dual-Ellipse Localization Model

There will be the problem of redundant information when the separate high-power sources sonar positioning system works. To take full advantage of the redundant measurement information so that the positioning accuracy can be improved, the optimized algorithm of dual-ellipse localization model based on o linear data fusion is presented. And the localization precision formulas under the different conditions are deduced. Through the simulation, the graph of the Geometrical Dilution of Precision (GDOP) is given. The result shows that, the baseline length and the time measure error has a little effect on the positioning capability of the entire region. Besides, the angle measure error has a great influence on the localization accuracy, and the localization error will increase when either of two receivers has a larger error. Compared with the algorithm based on the distance measurement, the data redundancy can be better utilized by data fusion, and the localization accuracy can be effectively improved. The result provides the theoretical basis to solve the optimization problem of long-range antisubmarine localization.