Tracking interacting dust: comparison of tracking and state estimation techniques for dusty plasmas

TL;DR

This paper compares tracking and state estimation techniques for dusty plasmas, showing that including neighboring particles improves accuracy, but simpler algorithms are often sufficient and more efficient.

Contribution

It demonstrates that incorporating neighboring particles enhances tracking accuracy and that simplified state estimation algorithms are effective, reducing computational complexity.

Findings

Including all nearest neighbors improves target tracking accuracy.

Higher-accuracy measurement techniques do not significantly enhance accuracy.

Simplified algorithms effectively reduce errors like pixel locking.

Abstract

When tracking a target particle that is interacting with nearest neighbors in a known way, positional data of the neighbors can be used to improve the state estimate. Effects of the accuracy of such positional data on the target track accuracy are investigated in this paper, in the context of dusty plasmas. In kinematic simulations, notable improvement in the target track accuracy was found when including all nearest neighbors in the state estimation filter and tracking algorithm, whereas the track accuracy was not significantly improved by higher-accuracy measurement techniques. The state estimation algorithm, involving an extended Kalman filter, was shown to either remove or significantly reduce errors due to "pixel locking". It is concluded that the significant extra complexity and computational expense to achieve these relatively small improvements are likely to be unwarranted for many situations. For the purposes of determining the precise particle locations, it is concluded that the simplified state estimation algorithm can be a viable alternative to using more computationally-intensive measurement techniques.