A probability-based indoor positioning method and device

Abstract

The invention discloses a probability-based indoor location method. The method comprises the steps of establishing a fingerprint database on the basis of a received signal strength indication (RSSI) sequence and probability distribution of M reference points relative to N access points (AP), and location coordinates of M reference points; acquiring the RSSI sequence and probability distribution of at least one to-be-tested point relative to N AP, wherein Laplace calibration is performed on the probability of at least one to-be-tested point relative to each AP; on the basis of the Bayes theorem, matching the RSSI sequence and probability distribution of the j to-be-tested point of at least one to-be-tested point with the fingerprint database, so as to acquire the probability of the j to-be-tested point on each of the M reference points; and ranking the M probabilities from big to small so as to form a probability list, taking the reference points corresponding to the first k probabilities in the probability list, and computing the oriented coordinate of the j to-be-tested point. Through adoption of the method, the indoor location accuracy can be improved.

Description

technical field [0001] The present invention relates to the technical field of indoor positioning, in particular to a probability-based indoor positioning method and device. Background technique [0002] With the rise of the concept of the Internet of Things and the rapid development of positioning technology, indoor positioning technology based on ZigBee (Zigbee protocol) has become a research hotspot, and finding a high-precision, low-complexity algorithm has become a common goal of researchers. At present, many researchers have adopted various methods to reduce the workload and improve the positioning accuracy. Although some algorithms can achieve better positioning results in the simulation environment, there are still large errors in the actual environment. One is that the actual environment has a great influence on the value of RSSI (Received Signal Strength Indication, received signal strength indication), such as wall blocking, reflection and diffraction, people walk...

AI Technical Summary

Problems solved by technology

At present, many researchers have adopted various methods to reduce the workload and improve the positioning accuracy. Although some algorithms can achieve better positioning results in the simulation environment, there are still large errors in the actual environment. One is that the actual environment has a great influence on the value of RSSI (Received Signal Strength Indication, received signal strength indication), such as wall blocking, reflection and diffraction, people walking, time, weather, etc. These factors will cause RSSI fluctuations, while Environmental factors are unavoidable, making it difficult to improve positioning accuracy

Probabilistic-based indoor positioning algorithms that have emerged have improved accuracy compared to deterministic algorithms, but there are "zero probability" situations in probabilistic algorithms (such as Bayesian probability method), once zero probability occurs, the positioning accuracy will decrease

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