Resolution improvement of measurement systems through optimal filtering techniques-Implementation issues on discrete signal processors

In many measurement problems, it is found that the lack of resolution of the measuring device is the consequence of some blurring of the measured signal. Under linearity and shift-invariance assumptions, the signal restoration can be performed by a linear filtering of the data implementing some minimum mean square error (MSE) deconvolution. One possible solution to the problem involves the use of a Kalman filter. If all the processes are stationary and the measurement noise is white, the steady-state Kalman filter and the infinite impulse response (IIR) Wiener filter are identical. The recursiveness of the Kalman filter algorithm is very amenable to VLSI implementation. The aim is to discuss the problems inherent in the implementation of a Kalman filter structure on specialized VLSI devices such as discrete signal processors (DSP). To this end, the basic algorithm is split into elementary operations involving functional units with a high degree of internal parallelism such as a multiplier-accumulator unit. Due to the real-time processing constraint, special attention is paid to rounding effects, and a comparison is made between fixed point and floating point arithmetics