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Robust adaptive quantization: A Kalman‐filter‐based approach
Author(s) -
Crisafulli Sam,
Bitmead Robert R.
Publication year - 1994
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480080501
Subject(s) - kalman filter , quantization (signal processing) , control theory (sociology) , computer science , smoothing , algorithm , invariant extended kalman filter , mathematics , extended kalman filter , artificial intelligence , control (management) , computer vision
Adaptive quantizers/dequantizers are systems that are used to quantize efficiently signals with a large variation in short‐term variance. They are typically found in telecommunication systems where highly non‐stationary signals such as speech need to be represented digitally with the minimum number of bits. Channel errors that are introduced owing to non‐ideal transmission significantly reduce the performance of adaptive dequantizers. In this paper we extend recent Kalman‐filter‐based adaptive quantization techniques to arrive at new dequantization schemes which are more robust to channel errors. This is achieved by utilizing the estimates produced by a Kalman filter based on a linear signal model which embodies the entire encoder/channel combination. Extensions to Kalman smoothing based on the same signal model result in further performance improvement at the expense of a small delay.