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ICASSP
2009
IEEE

Particle filtering for Quantized Innovations

10 years 4 months ago
Particle filtering for Quantized Innovations
In this paper, we re-examine the recently proposed distributed state estimators based on quantized innovations. It is widely believed that the error covariance of the Quantized Innovation Kalman filter [1,2] follows a modified Riccati recursion. We present stable linear dynamical systems for which this is violated and the filter diverges. We propose a Particle Filter that approximates the optimal nonlinear filter and observe that the error covariance of the Particle Filter follows the modified Riccati recursion of [1]. We also simulate a Posterior CramerRao bound (PCRB) for this filtering problem.
Ravi Teja Sukhavasi, Babak Hassibi
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Ravi Teja Sukhavasi, Babak Hassibi
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