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

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