Abstract
We present a strategy for designing an α - β - η - θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α - β filter. Moreover, we apply an α - β - η - θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α - β - η - θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target’s acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.
Highlights
Monitoring systems for robots and intelligent vehicles that employ remote sensors, such as cameras and radar, require the tracking of moving objects
This shows that the steady state performance of the PVM filter is close to that of the α-β-η-θ filter, and the α-β-η-θ filter analysis is effective in performance predictions of the PVM Kalman filters
We have proposed a gain design strategy for α-β-η-θ filters and applied it to UWB Doppler radar simulations
Summary
Monitoring systems for robots and intelligent vehicles that employ remote sensors, such as cameras and radar, require the tracking of moving objects. Adaptive tracking techniques such as Kalman and extended Kalman filters [1,2,3,4,5] and particle filters [6,7] are commonly used for this purpose because of their accuracy. For these reasons, fixed gain filters are still being widely used in applications that strongly require real-time capability and simple implementation, such as tracking in ultrasonography in medicine [13], Appl.
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