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A systolic VLSI implementation of Kalman-filter-based algorithms for signal reconstruction

The problem of improving the performance of the implementation in VLSI technology of Kalman-based algorithms for signal reconstruction in real time is discussed. A systolic approach is proposed to develop architecture expressly for this specific application. Implemented algorithms are based on the steady-state version of the Kalman filter, which performs for a broad field of specific applications, but the use of a co-processor for the Kalman gain is allowed. We show that the autoregressive model of Kalman filtering is particularly adapted to parallel processing and is well suited for implementation. Although intended to improve signal reconstruction, other applications where a similar autoregressive model of Kalman filtering is required are allowed. The performance of the systolic architecture is validated by comparison with Motorola's general-purpose DSP56002 digital signal for real-world spectrometric signal reconstruction.