Name
Abstract | ||
|---|---|---|
This paper addresses estimation fusion in the presence of possible mismatches of sensor model. The main concerns of the paper lie in two aspects. One is to improve the filter performance of the single sensor when there are possible mismatches about the sensor model. The other one is to adopt a good fusion scheme to combine local estimates. For these purposes, the measurement process of the local sensor is modeled by multiple models firstly, and the IMM (interacting multiple model) estimator is implemented to produce estimates for individual models. Next, we describe the local estimate by a Gaussian mixture rather than a single Gaussian density in the baseline IMM filter. Such a GMM (Gaussian mixture model) representation of the system state allows us to keep the detailed information about the local tracker, which contributes to the further fusion if treated properly. Finally, the fusion of two Gaussian mixtures is done in the probabilistic framework, and a close-form solution is derived without complex numerical operations. Simulation results demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.inffus.2014.02.002 | Information Fusion |
Keywords | Field | DocType |
Gaussian mixture model (GMM),Estimation fusion,Interacting multiple model (IMM) | Pattern recognition,Fusion,Gaussian,Artificial intelligence,Sensor model,Fusion scheme,Mixture model,Mathematics,Estimator,Probabilistic framework,Multiple Models | Journal |
Volume | ISSN | Citations |
20 | 1566-2535 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyan Zhu | 1 | 42 | 8.57 |
| Shuo Chen | 2 | 199 | 33.91 |
| Chongzhao Han | 3 | 446 | 71.68 |