Comparison of a grid-based filter to a Kalman filter for the state estimation of a maneuvering target

Mark Silbert; Thomas Mazzuchi; Shahram Sarkani

doi:10.1117/12.892477

16 September 2011 Comparison of a grid-based filter to a Kalman filter for the state estimation of a maneuvering target

Mark Silbert, Thomas Mazzuchi, Shahram Sarkani

Proceedings Volume 8137, Signal and Data Processing of Small Targets 2011; 81370J (2011) https://doi.org/10.1117/12.892477
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

Providing accurate state estimates of a maneuvering target is an important problem. This problem occurs when tracking maneuvering boats or even people wandering around. In our earlier paper, a specialized grid-based filter (GBF) was introduced as an effective method to produce accurate state estimates of a target moving in two dimensions, while requiring only a two-dimensional grid. The paper showed that this GBF produces accurate state estimates because the filter can capture the kinematic constraints of the target directly, and thus account for them in the estimation process. In this paper, the relative performance of a GBF to a Kalman filter is investigated. The state estimates (position and velocity) from a GBF are compared to those from a Kalman filter, against a maneuvering target. This study will employ the comparison paradigm presented by Kirubarajan and Bar-Shalom. The paradigm incrementally increases the maneuverability of a target to determine how the two different track filters compare as the target becomes more maneuverable. The intent of this study is to determine how maneuverable the target must be to gain the benefit from a GBF over a Kalman filter. The paper will discuss the target motion model, the GBF implementation, and the Kalman filter used for the study. Our results show that the GBF outperforms a Kalman filter, especially as the target becomes more maneuverable. A disadvantage of the GBF is that it is more computational than a Kalman filter. The paper will discuss the grid and sample sizing needed to obtain quality estimates from a GBF. It will be shown that the sizes are much smaller than what may be expected and is quite stable over a large range of sizes. Furthermore, this GBF can exploit parallelization of the computations, making the processing time significantly less.

Citation Download Citation

Mark Silbert, Thomas Mazzuchi, and Shahram Sarkani "Comparison of a grid-based filter to a Kalman filter for the state estimation of a maneuvering target", Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 81370J (16 September 2011); https://doi.org/10.1117/12.892477

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