One-Step-at-a-Time Tracking
نویسندگان
چکیده
We introduce a new mathematical method for tracking position and orientation. The method, which is applicable to a wide variety of virtual-environment tracking systems, offers several advantages: (1) significantly improved estimate rates and latencies; (2) improved accuracy; (3) a flexible framework for heterogeneous multi-sensor data fusion; and (4) a unique opportunity to perform simultaneous source and sensor autocalibration . We use an extended Kalman filter in an unusual fashion, maintaining a leastsquares estimate of a target’s position and orientation using a sequence of single sensor measurements as opposed to groups of measurements. Even though single sensor measurements offer incomplete information, they provide some information and are used to incrementally improve the previous estimate. By incorporating an ongoing sequence of individual sensor measurements in this way we can track position and orientation, one step at a time.
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