融合压缩感知的指纹信息密度凝聚定位算法OA北大核心CSTPCD

Aiming at the problems of bulky fingerprint database,redundant information and monotonous data processing mechanism in traditional fingerprint positioning methods,a fingerprint information density aggregation positioning algorithm based on compressed sens-ing is proposed,which realizes the cross mapping of compressed sampling,signal recovery to fingerprint database construction and online positioning.The complementary gain of the two effectively improves the positioning ability of the system.The region fuzzy partition is completed by the spatial feature correction clustering algorithm,and the scene feature is adaptive and the regional edge mismatch RP is included.Comprehensive evaluation and selection of regional optimal AP subsets from effectiveness,discrimination and measurability are carried out to aggregate information density.For location matching,sparse Bayesian Learning algorithm is selected to weaken the in-fluence of fingerprint correlation,and information sequence is introduced to enhance the nearest neighbor RP weight.The experimental results show that the proposed scheme has a good effect on streamlining fingerprint information and can effectively condense the value information of fingerprint database.The positioning accuracy is significantly better than algorithms in this field.Compared with the tradi-tional positioning algorithm,it has certain advantages and has high potential and application value.