基于LANDMARC与压缩感知的双段式室内定位算法

鉴于已有室内定位算法定位精度与运算效率之间的矛盾，该文提出一种将LANDMARC区域定位与基于模拟退火优化正则化正交匹配追踪(SROMP)的压缩感知位置估计相结合的双段式定位算法(LANDMARC- SROMP CS)。首先，利用LANDMARC定位算法快速锁定目标所在区域范围；在锁定的区域内，再引入压缩感知理论实现目标位置估计。此部分，首先根据锁定区域范围建立虚拟参考标签；然后由新型组合核函数相关向量机算法训练得到室内传播损耗模型，计算获得虚拟标签处接收信号强度值，构建测量矩阵；最后利用SROMP压缩感知重构算法求解出目标的位置索引矩阵，对索引矩阵中的位置相关点加权平均得到目标的位置信息。实验结果表明，所提定位算法平均定位误差为0.6445 m，算法运算效率相对较高，可以较好地满足室内定位的要求。

Double Stage Indoor Localization Algorithm Based on LANDMARC and Compressive Sensing

In consideration of the contradiction between the positioning accuracy and computational efficiency of the previous indoor positioning algorithm, a double stage positioning algorithm (LANDMARC- SROMP CS) using LANDMARC combined with Compressive Sensing based on the Regularized Orthogonal Matching Pursuit optimized by the Simulated annealing algorithm (SROMP) is put forward. First of all, LANDMARC location algorithm is used to lock the target area quickly; then in the locked area, Compressive Sensing (CS) theory is introduced to realize the target position estimation. In this part, firstly, the virtual reference tags are constructed according to the scale of the locked area; then, the measurement matrix is constructed by the received signal strength data of the virtual reference tags, and the signal strength data are calculated by the indoor propagation loss model which is trained by a new relevance vector machine algorithm based on mixed kernel functions. At last, the SROMP compressive sensing reconstruction algorithm is used to get the position index matrix, and the position information of the target also can be obtained through a simple weighted average calculation. The experimental results show that the average positioning error of the proposed algorithm is only 0.6445 m, and the computation efficiency of the proposed algorithm is relatively high, which can meet the indoor positioning requirements well.