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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Ravi Teja Sukhavasi, Babak Hassibi
• Keywords: Error Covariance, ICASSP 2009, Modified Riccati Recursion, Particle Filter, Signal Processing