Feedback Particle Filter: A novel algorithm for nonlinear estimation


Background:

Nonlinear estimation algorithms are a widely pervasive platform technology. One specific application domain is target state estimation (target tracking). It is an important in a number of Intelligence, Surveillance, and Reconnaissance (ISR) systems and products: Radar-based air moving target indicator (AMTI) systems are used in surveillance of enemy aircraft. In missile defense, the objective is to track and intercept ballistic objects, including parts of aging satellites that may re-enter the atmosphere. The radar-based ground moving target indicator (GMTI) systems are used for military surveillance and increasingly for counter-terrorism applications. In wide-area maritime surveillance, an air-borne aircraft uses radar-based measurements to detect and track maritime surface vessels. In related applications, passive sonar devices are employed for the purposes of underwater submarine tracking. Apart from these, such algorithms also find extensive use in civilian applications, including: air traffic surveillance, weather surveillance, ground mapping, geophysical surveys, remote sensing, autonomous navigation, and robotics. For example, current air traffic surveillance systems use radar-based measurements to track aircrafts.

State-of-the-art and its limitations:

Current state estimation systems use a Kalman filter, or one of its extensions (e.g., extended Kalman filter). The limitations of these tools in applications arise on account of nonlinearities in signal and sensor models.. The nonlinearities lead to a non-Gaussian multi-modal conditional distribution. For such cases, Kalman and extended Kalman filters are known to perform poorly.

Feedback Particle Filter:

Feedback particle filter is a novel algorithm for nonlinear estimation that has been developed by our research group. The feedback particle filter provides for a generalization of the Kalman filter to a general class of nonlinear non-Gaussian problems. Our algorithm inherits many of the structural features, such as an innovation error-based feedback structure and robustness properties, which have made the Kalman filter so widely applicable over the past five decades. Compared to other types of particle filter algorithms, our algorithm provides more accurate estimates at a fraction of the computational cost. Our algorithms are also more robust, cost-effective, and relatively easier to debug and implement. This is now being documented in open literature, both in research carried out in our group as well as externally by independent groups.

Presentations:

Plenary lecture at the 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, Santa Barbara, September 16, 2012.

Feedback Particle Filter and its Applications to Neuroscience

Publications:

Yang, T., P. G. Mehta, and S. P. Meyn, ''Feedback Particle Filter,'' IEEE Transactions on Automatic Control, To appear in 2013.

Tilton, A., Shane Ghiotto, P. G. Mehta, ''A Comparative Study of Nonlinear Filtering Techniques,'' ICIF, 2013.

Abstract and Downloads

A self-contained introduction to feedback particle filter, and its relationship with Kalman filter and the conventional importance sampling-based particle filter.

Awards and Honors:

  1. Paper ''Multivariable Feedback Particle Filter'' with graduate student, Tao Yang, finalist for the Best Student Paper Award at the IEEE Conference on Decision and Control, Hawaii, December 2012

  2. Plenary lecture, 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, Santa Barbara, September 2012.

Acknowledgements:

Financial support from the Air Force Office of Scientific Research (AFOSR) grant FA9550-09-1-0190 and the National Science Foundation (NSF) grant EECS 09-25534 is gratefully acknowledged.