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.
In multi-sensor fusion applications, various sources of data are combined to create a coherent situational picture. The
ability to track multiple targets using multiple sensors is an important problem. The data provided by these sensors can
be of varying quality, such as data from RADAR and AIS. Does this varied quality of data negatively impact the
tracking performance when compared to using the best data source alone? From an information-theoretic standpoint, the
answer would be no. However, this paper investigates this issue and exposes a few caveats. In particular, this study
addresses how the relative update rate of varying quality sensors affects tracking performance and answers the question
'Is more data always better?'
Although the Kalman filter is efficient and effective for computing state estimates of a moving target, it can produce
poor results when tracking a maneuvering target. The problem is that the Kalman filter must employ large plant noise
and/or large tracking gates to keep the target in track. This can result in larger errors in the state estimate as well as
larger uncertainties in these estimates. To track these maneuvering targets, a better approach would be to exploit the
kinematic constraints of the target to restrict the state estimates to only those where the target transition was possible.
Unfortunately, the Kalman filter cannot fully capture the physical constraints of the target motion. To address this
problem, several alternative approaches have been pursued including Kalman filter variants, particle filters, and gridbased
filters. Although grid-based filters can be effective, it seems they have been avoided due to their perceived
exponential computational requirements. A new approach for using a grid-based filter has been developed that can track
targets moving in two dimensions by using a well-confined, two-dimensional grid. As a result, this grid-based approach
is enormously more computationally efficient and can effectively exploit the kinematic constraints of the target. This
paper describes this grid-based filter, along with the inclusion of the kinematically-constrained target motion model. The
paper will then compare the tracking performance of this filter against a Kalman filter for maneuvering target scenarios.
The improved target state estimations from this grid-based filter will be shown and analyzed via Monte Carlo analysis.
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