Compressive sensing based indoor localization fingerprint collection and construction

Localization based on fingerprint has been viewed as a popular indoor localization technique, which uses the signal strength of different positions as the location fingerprint. The localization model can thus be constructed by analyzing the relationship between the location fingerprint and the target location. However, this method requires the manual acquisition of fingerprint signal data in an offline phase, which has become a bottleneck for practical application, especially in large-scale fields. Therefore, how to reduce the workload in fingerprint collection has become a significant issue. This paper invokes a compressive sensing-based method to reduce fingerprint construction complexity. First, the k-singular value decomposition algorithm based on an overcomplete dictionary is employed to sparse the fingerprint signal. Then, considering the uncertainty of the signal sparsity in the indoor environment, an adaptive fingerprint signal reconstruction algorithm based on error weight is proposed to construct signals with variable sparsity. We test the proposed fingerprint reconstruction on both actual RSSI and geomagnetic fingerprints. Experiments show that the fingerprint database of 132 reference positions can be reconstructed with only 50 compressed samples, which reduces the workload of offline collection by 62 % .