Abstract
A new fusion strategy is introduced in this article to estimate state for multi-rate multi-sensor systems with missing measurements. N sensors, which possess various sampling rates, render the measurements. Missing measurements with a certain probability pattern are also investigated. For these types of systems, Multi-rate Kalman filters are designed to estimate a target position at various sampling rates. Next, Ordered Weighted Averaging (OWA) operator is utilized to integrate multi-rate Kalman filters and improve the estimation quality. A new fusion strategy based on a real covariance matrix is introduced for updating the weighting factors, and proof of convergence is granted. Simulation studies on a tracking system verify the superior performance of the proposed fusion strategy in comparison with the Kalman filter, the multi-rate Kalman filters, and also the previous fusion methodology.
Highlights
With an ever-increasing number of sensor-rich complex engineering systems, the subject of sensor data fusion has become an essential area in the field of estimation
We introduce a new Ordered Weighted Averaging (OWA) based fusion strategy using multi-rate Kalman filters
X(k|k) = Wi,k xi,k (k|k) i=1 where x(k|k) is the fused state estimated by OWA operator at step k, xi,k (k|k) is the state estimated by the ith multi-rate Kalman filter, and W(i, k) is the weighting factor which is driven by the following theorem
Summary
With an ever-increasing number of sensor-rich complex engineering systems, the subject of sensor data fusion has become an essential area in the field of estimation. Data fusion is extensively employed in different fields, such as navigation, air traffic control, process control, and robotics [1]–[3]. Integrated information from various sources often enhances the estimation accuracy and strengthens the robustness of observations. Multi-sensor fusion is a powerful technique, which elicits significant information from multiple sensors to acquire an optimal or suboptimal state estimation. The integration of multiple sensors contributes to more reliable results compared to a separate sensor due to complementary information among the resources [4]–[6].
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