Cooperative location method based on EKF (Extended Kalman Filter) and PF (Particle Filter) under nonlinear and non-Gaussian condition

Abstract

The invention discloses a cooperative location method based on an EKF (Extended Kalman Filter) and a PF (Particle Filter) under a nonlinear and non-Gaussian condition. The method comprises the following steps of obtaining TOA (Time of Arrival) original data of a target node and each base station and computing to obtain a distance between the target node and each base station; utilizing a Wylie identification method to judge whether an NLOS (Non Line of Sight) error exists; obtaining a difference of the TOA value to obtain a TDOA (Time Difference of Arrival) value and reconstructing a distance different rm1 corresponding to the TDOA value; respectively utilizing an EKF algorithm and a PF algorithm to estimate position coordinates of the target node at the moment tk; carrying out residual weighting to obtain a final estimated value at the moment tk; and carrying out weighted smoothing on the position coordinates at all moments to obtain a final positioning result. In comparison with the EKF, the method is more suitable for the nonlinear and non-Gaussian positioning environment; in comparison with the PF, the use of incorrect data is effectively avoided and the calculated amount is reduced. According to the method, the influence of the NLOS error is effectively reduced, the advantages of the EKF and the F are combined, the defects of the EKF and the F are overcome, and the more precise positioning is realized.

Description

technical field [0001] The invention relates to the technical field of indoor positioning and tracking, in particular to a collaborative positioning method based on EKF and PF under nonlinear and non-Gaussian conditions. Background technique [0002] With the rapid development of microelectronics technology, communication technology and computer technology, wireless positioning, as an important application of sensor network and Internet of Things, has attracted more and more attention. Currently, ranging-based positioning methods mainly include RSSI (signal strength of arrival), AOA (angle of arrival), TOA (time of arrival), and TDOA (time difference of arrival). Among them, RSSI is a theoretically ideal algorithm that converts propagation loss into distance, but in the actual positioning environment, electromagnetic wave propagation has dynamic characteristics, which will cause serious interference to the ranging results and cause large ranging errors; The hardware system ...

AI Technical Summary

Problems solved by technology

[0005] In the actual application process, EKF is a relatively common nonlinear filtering method, but it is only suitable for the case where the filtering error and prediction error are small, otherwise it may cause filtering instability or even divergence; PF gets rid of the problem of nonlinear filtering The random quantity must satisfy the constraints of the Gaussian distribution in the problem, but the algorithm also has some problems. It relies on a large number of sample data to better approximate the posterior probability density of the system, and the calculation is relatively large.

Images