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Indoor tracking algorithm based on simplified Sage-Husa adaptive filter

In indoor tracking, Kalman filtering is a commonly used method. However, its tracking effect is not good when the target motion state is uncertain. To solve this problem, the researchers proposed the concept of adaptive filtering, Sage-Husa adaptive filtering is one of them. Sage-Husa adaptive Kalman filter algorithm can use the time-varying noise statistic estimator to estimate and correct the process noise and measurement of the system in real time while using the observation data for recursive filtering, so as to achieve the purpose of reducing model error, suppressing filter divergence and improving filtering accuracy. Therefore, this paper proposes a tracking algorithm based on simplified Sage-Husa adaptive filtering, and completes the target trajectory tracking based on the positioning results. The algorithm uses the positioning error of the positioning system as a fixed observation noise, and uses the time-varying noise statistical estimator to continuously correct the mean and variance of the system noise, so that the system equation is more in line with the actual motion state. Finally, the accuracy and performance of the tracking algorithm are verified by experiments.