The Kalman like particle filter: Optimal estimation with quantized innovations/measurements

— We study the problem of optimal estimation using quantized innovations, with application to distributed estimation over sensor networks. We show that the state probability density conditioned on the quantized innovations 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 resuting 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. We also note that the conditional state density follows a, so called, generalized closed skew-normal (GCSN) distribution.

• Type: Conference
• Year: 2009
• Where: CDC
• Authors: Ravi Teja Sukhavasi, Babak Hassibi
• Keywords: CDC 2009, Control Systems, Gaussian Random Vector, Particle filter, Truncated Gaussian Vector