基于数据融合的压缩感知多目标定位算法

文中提出一种基于数据融合的压缩感知多目标定位算法,该算法能够同时处理多种不同类型的定位数据。与传统算法相比,该算法以目标个数的稀疏性为基础,通过压缩感知技术来重构目标位置向量,从而大大减少了传感器的数目。算法分为数据预处理和数据融合定位两个阶段。在数据预处理阶段,将不同类型的数据转换到同一个数量级,使得各类型数据能被充分用于提高目标定位性能；在数据融合定位阶段,提出一种基于多测量向量的压缩感知重构算法来估计目标位置向量。仿真证明,相比于现有的压缩感知定位算法,所提算法具有更高的定位精度和更强的鲁棒性。

Compressive Sensing Multi-target Localization Algorithm Based on Data Fusion

This paper proposd a new compressive sensing localization algorithm based on data fusion,which can utilize all kinds of the localization data at the same time.The proposed theory is based on the sparsity of the target number and compressive sensing theory,and it can greatly reduce the quantity of sampling compared with the traditional localization algorithms.The new algorithm consists of data pre-processing and data fusion based localization.At the first step,different kinds of measurements are transferred into the form which has the same level.Then the technique of multiple measurement vectors is used to recover the target vector.Compared with other algorithms,the proposed algorithm holds better performance in localization accuracy and robustness.