Research On Nonlinear Filtering Algorithm And Its Application In Integrated Positioning

GPS/DR integrated positioning system is essientially nonlinear. At present, the nonlinear filtering method applied for integrated positioning system is mainly Extended Kalman filter (EKF). However, EKF has some shortcomings including low filtering precision, poor robustness on system model error and noise statistics and difficult implementation in practice. Therefore, a novel type of nonlinear filtering algorithm with better practicability and filtering estimation accuracy than traditional EKF, Sigma Point Kalman Filter(SPKF), is introduced.With development of non-linear filtering theroy, the actual integrated positioning systems have new features such as noise correlation, model uncertainty etc. Traditional SPKF algorithm has poor robustness to model uncertainty, meanwhile, it is established on condition that system noise and measurement noise are not related. Therefore, like EKF, SPKF algorithm still can’t satisfy requirements of integrated positioning systems.In view of the theory limitations put forward above existing in traditional SPKF algorithm, the paper propose a new STF-SPKF-CN algorithm. First, considering the correlation of process noise and measurement noise, we propose SPKF-CN filter that can be used to deal with the systems with correlated noise. Then, in order to enhance the SPKF-CN algorithm’s robustness to deal with the model uncertainty, we introduce the idea of STF to SPKF-CN and propose the STF-SPKF-CN algrithm, which not only keep the good characteristics of strong tracking filter, but also fuse the advantage of SPKF algorithm’s high estimate accuracy, so is the perfect combination of STF and SPKF for noise correlation systems.Meanwhile, the above-established non-linear filtering theory innovations about SPKF is used in GPS/DR integrated positioning system, and the simulations show that the STF-SPKF-CN algorithm can meet the requirements of high precision and reliability in integrated positioning systems.