An assurance region at level <i>p</i>, A<sub>P=p</sub>, is
an area in motion space that contains the target with assigned
probability <i>p</i>. It is on the basis of A<sub>P=p</sub> that an action is
taken or a decision made. Common model-based trackers generate a
synthetic distribution function for the kinematic state of the target.
Unfortunately, this distribution is very coarse, and the resulting
A<sub>P=p</sub> lack credibility. It is shown that a map-enhanced, multiple
model algorithm reduces the tracking error and leads to a compact
A multiple model tracking algorithm has considerable advantage when the target is moving on a road grid. A map-enhanced algorithm magnifies this by excluding large regions of the 2D motion-space from search. However, integrating the grid of streets and junctions into a recursive algorithm is challenging. This paper presents a
map-enhanced, tracker that uses local models tuned to motions on the four cardinal directions. Map compliance is achieved by
re-initializing the local kinematic state when a model change occurs and by moving the local estimate to the map after a measurement update. Unfortunately, the transition dynamics of the model state depend upon the kinematic state. A proposed junction influence function is based upon a rapidly decaying measure of the distance from the target to the nearest compatible junction.
A multiple-model tracker; e.g., the Gaussian Wavelet Estimator (GWE), employs a family of linear, local models
to represent the motion of a maneuvering target over a range of operating modes. The state estimate generated
by the GWE is a distribution with diffuse support. A road map provides contemporaneous, albeit circumscribed,
information that can be integrated into the GWE to improve location estimation. However, fusing the inelastic
restrictions of a road grid with the broad state estimates generated from conventional kinematic measurement
requires considerable care. This paper presents a modified version of the GWE which integrates a map grid
into the state estimate. The result is a state estimate consisting of a set of singular Gaussian sub-estimates. It
is shown by example that map-enhancement improves the accuracy of the location estimates and sharpens the
calculated uncertainty region.
Tracking moving vehicles has received less attention than its its aerial counterpart. The smooth velocity transitions common to aircraft are replaced with abrupt turns and and speed changes. Though the kinematic evolution of a ground vehicle is more complex, the path is more restricted. For example, if target motion is constrained by a terrain map, the topography should be integrated into the tracking algorithm. This paper shows by means of an example that the Gaussian wavelet estimator is particularly suited to map-enhanced estimation.
Hybrid models have proven useful for tracking targets with multiple motion modes. Most emphasis in the literature has been devoted to
aircraft which transition from constant velocity motion to constant (or nearly constant) turns and back. Ground targets motions have
received less attention despite similarities with aircraft. This paper presents a study of the ground-tracking problem using the
Gaussian wavelet estimator as the basic algorithm. The sensor suite contains a matrix of range-bearing sensors of quality that is strongly
range dependent. There also may be an acoustic sensor which provides an auxiliary speed measurement. It is shown that the high degree of
partitioning of the kinematic state space provided by the algorithm is useful in this application.
Hybrid motion models are useful for deriving algorithms for tracking maneuvering targets. Performance analysis of such trackers usually focuses on the accuracy of estimates of the kinematic states; e.g., location and velocity. The performance metric discussed here emphasizes motion mode estimation. It is shown that a well-studied
tracker can be used for modal interval estimation and smoothing. An example displays the striking advantage of the approach.
Model-based, path estimation algorithms are commonly derived from a linear kinematic equation and an expansive observation model. The premise that the measurement region is all-inclusive simplifies the performance analysis, but it does not describe the relevant characteristics of some sensors. Most sensors have an active region. If the region contains the target, a noisy location measurement is made. Alternatively, no location measurement is returned. There is a clear tradeoff between measurement acuity and broad coverage. A sensor that adapts its active region to the most likely target location should achieve higher quality tracking and identification. However, effective utilization of this flexibility requires an accurate determination of the conditional distribution of the target-state. In this paper, the Gaussian Wavelet Estimator is used in an adaptive algorithm for sensor management. It is shown that the adaptive window provides performance that is superior to that achieved using a fixed window of comparable cover probability.
Without range measurements, a sensor platform must execute a nontrivial motion if good target location estimates are to be generated with conventional tracking algorithms. This paper shows that even a stationary image-based tracker can provide good location estimates when the target maneuvers. A tight cover region is generated with the proposed algorithm, and is compared with a more general bound.
The quality of multiple model estimators can be improved with multisensor fusion. This paper contrasts the performance of three multiple model algorithms. It is shown that the simplest is adequate in high signal-to-noise environments. The more sophisticated warrant attention when the observations are ambiguous.
Advances in image and signal processing permit implementation of sophisticated sensor fusion algorithms for tracking and target-identification. The polymorphic estimator employs a dual path architecture to accomplish these tasks simultaneously. It has been observed that there is an identification bias toward more agile targets. This bias can be overcome with higher quality sensors. In some cases more precise modeling of the target motion is an even better solution.