Moving object tracker using sensor fusion of ultrasonic range finder and accelerometer

Abstract

We present an accurate real-time moving object tracker using an α-β-γ filter for a position/ acceleration sensor fusion system comprising ultrasonic range finders and an accelerometer. For this sensor fusion system, an optimal and stable α-β-γ filter receiving simultaneous measurements of position/acceleration is introduced based on a minimum-variance filter criterion. Numerical analyses and simulations verify its effectiveness in terms of stability, accuracy, convergence speed, and simplicity in parameter design. Experiments demonstrate its efficacy as a realistic tracking system and verify that the proposed α-β-γ filter achieves faster convergence and better accuracy than conventional position-only-measured α-β-γ filters with a steady-state error of 70% that of a conventional α-β-γ filter.

Correct Stability Condition and Fundamental Performance Analysis of the α-β-γ-δ Filter

“…Please note that the smoothing processes of (26) and (27) are the same as those of (5) and (6). Similarly, the α-β-γ filter is a steady-state third-order Kalman filter using the constant acceleration model; thus, it tracks the position, velocity, and acceleration of moving objects.…”

“…For such applications, Kalman filter- [1][2][3][4] and Bayesian filter-based trackers [5] are commonly used to track the position, velocity, and acceleration of the targets. However, in recent years, the importance of jerk, which is the derivative of acceleration with respect to time, has been realized to address tracking problems [4,6,7]. In [4,6], it was proved that the use of a high-dimensional parameter such as jerk can improve the performance of the tracking filters for highly maneuvering targets.…”

“…However, in recent years, the importance of jerk, which is the derivative of acceleration with respect to time, has been realized to address tracking problems [4,6,7]. In [4,6], it was proved that the use of a high-dimensional parameter such as jerk can improve the performance of the tracking filters for highly maneuvering targets. Thus, the consideration of jerk in tracking filters is promising.…”

“…However, commonly used and well-analyzed conventional tracking filters do not consider tracking jerk. A steady-state second-order Kalman filter, known as an α-β filter [8][9][10], tracks position and velocity only, and a steady-state third-order tracking filter, known as an α-β-γ filter [6,[11][12][13], tracks these in addition to acceleration.…”