基于可见光通信的指纹定位,提出一种低复杂度、稀疏度自适应的压缩感知算法。首先,利用位置指纹的稀疏性,将定位问题转换为稀疏矩阵的重构问题。其次,根据重构的残差值,自适应地计算近邻值。最后,详细分析指纹采样间距、信噪比、调制带宽及发射功率对定位误差的影响,详细分析所提定位算法的时间复杂度、最优近邻值的分布、发光二极管个数及最大近邻指纹数对定位误差的影响。仿真结果表明,所提定位算法的平均计算时间低、定位误差小,当信噪比为10 dB,指纹点之间的间距为40 cm时,所提定位算法的平均定位误差为1.56 cm,显著低于现有的同类算法。

In this paper, a low-complexity, sparsity adaptive compressed sensing algorithm is proposed based on fingerprint localization of visible light communication. First, the localization problem is transformed into a sparse matrix reconstruction problem based on the sparsity of location fingerprints. Second, the nearest neighbor value is adaptively calculated based on the reconstructed residual value. Finally, the impact of fingerprint sampling interval, signal-to-noise ratio, modulation bandwidth, and transmission power on positioning errors are analyzed in detail. Moreover, the time complexity, distribution of the optimal nearest neighbor values, number of the light-emitting diodes, and maximum number of nearest neighbor fingerprints of the proposed positioning algorithm on positioning errors are also analyzed. The simulation results show that the proposed positioning algorithm has comparatively low average calculation time and small positioning error. When the signal-to-noise ratio and the distance between the fingerprints are 10 dB and 40 cm, respectively, the average positioning error of the proposed positioning algorithm is 1.56 cm, which is significantly lower than those of existing algorithms.