Target Location Method Based on Compressed Sensing in Hidden Semi Markov Model

A compressive sensing-based target localization method based on Hidden semi-Markov Model (HsMM) is proposed to address problems like unpredictable data and the multipath effect of the Receive Signal Strength (RSS) in indoor localization. The method can achieve both coarse and precise positioning by combining HsMM and the compressive sensing algorithm. Firstly, the hidden semi-Markov model is introduced to complete the coarse positioning of the target, and a parameter training method is proposed; secondly, the Davies-Bouldin Index and the Calinski-Harabasz Index based on the Euclidean distance and on the proposed connection distance herein are introduced; then, on the basis of coarse positioning, a precise positioning method based on compressive sensing is proposed; in the compressive sensing method, Gaussian matrix is introduced and a selection method of two screening matrices of the deterministic matrix is proposed; finally, the performance of coarse positioning is verified by experimental data for Hidden Markov Model (HMM) and HsMM, respectively, and the performance of the compressive sensing algorithm based on the two screening matrices of Gaussian matrix and deterministic matrix is respectively verified; the effectiveness of the proposed algorithm is experimentally verified.

Automated summary

This paper proposes a target localization method based on compressive sensing and Hidden semi-Markov Model (HsMM), which can solve some problems in indoor localization. The method achieves both coarse and precise positioning by combining compressive sensing and HsMM, and introduces some parameter training methods and indices to improve the localization performance.