  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.
