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The Kalman-Like Particle Filter: Optimal Estimation With Quantized Innovations/Measurements

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2 Author(s)
Ravi Teja Sukhavasi ; Qualcomm Research, San Diego, USA ; Babak Hassibi

We study the problem of optimal estimation and control of linear systems using quantized measurements. We show that the state conditioned on a causal quantization of the measurements can be expressed as the sum of a Gaussian random vector and a certain truncated Gaussian vector. This structure bears close resemblance to the full information Kalman filter and so allows us to effectively combine the Kalman structure with a particle filter to recursively compute the state estimate. We call the resulting filter the Kalman-like particle filter (KLPF) and observe that it delivers close to optimal performance using far fewer particles than that of a particle filter directly applied to the original problem.

Published in:

IEEE Transactions on Signal Processing  (Volume:61 ,  Issue: 1 )