Real-time implementation of a narrow-band Kalman filter with a floating-point processor DSP32

Abstract

The author presents experimental results from two studies. First, a real-time narrowband Kalman filter is implemented with a floating-point digital processor DSP32. The real-time capability of this narrowband filter is investigated by varying parameters Q and R. The covariance matrices Q and R of the dynamic and measurement noise sequences are found to exhibit duality in the real-time tuning process and have a direct effect on system stability. If the value of Q used is smaller (with fixed R), the tracking time and the narrower tracking bandwidth of the filter will be longer. In addition, if the value of R used (with fixed Q) is smaller, the tracking time will be smaller, and the tracking bandwidth of the filter will be larger. The results are tabulated. Second, two optimal codes (in the sense of the execution speed), straight-line code and general matrix-based code, have been developed for implementing the narrowband Kalman filter. These two codes are compared in terms of program memory size, data memory size, and speed of execution. With the matrix-based code, the DSP32 performance is evaluated in terms of speed and memory size by varying the number of states of a Kalman filter. The results are also tabulated.<>