An improved tracking model based on self-adaptive Kalman filter

DOI

10.2991/icmmita-16.2016.290How to use a DOI?

Keywords

Kalman filtering; Self-adaptive; Irestrictions;Multi-model;VSIFT map

Abstract

The self-adaptive Kalman filter algorithm can solve the problem of filtering divergence and tracking in conventional Kalman filter algorithm, so it is widely used in GPS data processing of vehicles and ships. But in practical application, the tracking of adaptive algorithm is still insufficient. Therefore, this paper proposes an improved tracking model based on adaptive Kalman filter. In this paper, by adding velocity and acceleration constraints to the conventional Kalman equation, we find that the equation only changes in the one-step prediction equation. By adding the changing parameters to the self-adaptive parameters in self-adaptive Kalman filter algorithm, it can effectively enhance the tracking ability of the adaptive algorithm to the velocity and acceleration changes. At last, the improved model is improved by multi-model and VSIFT. The simulation results show that the improved method can effectively enhance the model tracking performance.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Meng Lian
AU  - Yi Sun
PY  - 2017/01
DA  - 2017/01
TI  - An improved tracking model based on self-adaptive Kalman filter
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1272
EP  - 1278
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.290
DO  - 10.2991/icmmita-16.2016.290
ID  - Lian2017/01
ER  -