Indoor Positioning Method with Sparse Reference Points

‍ ‍With the development of mobile internet， people's demand for indoor location service is increasing day by day. The indoor fingerprint database localization algorithm based on Wi-Fi has the advantages of low cost and good positioning accuracy， but the signal acquisition of fingerprint database takes a lot of time and manpower. In this paper， the methods of constructing efficient fingerprint database and high-precision indoor location under sparse reference points are studied deeply. This paper improves the Kalman filter to suppress the Wi-Fi signal noise and the missing points， designes the wireless access point selection strategy based on signal strength difference variance to filter out low information access point， and proposes kriging interpolation algorithm based on support vector regression （SVR-Kriging） to reconstruct the fingerprint database， finally uses the AP weighted and Weighted K-Nearest Neighbor （AWKNN） to get location. The method is applied to two-dimensional and three-dimensional location scenes. The experimental results show that the two-dimensional positioning average error is 1.01 m and the three-dimensional positioning average error is 0.92 m. This method solves the problem of signal acquisition difficulty and AP redundancy， and effectively reduces the positioning error.