Local tracking in clutter initialises and updates true and false tracks. Local false track discrimination uses a track quality measure to confirm most of the true tracks, and to terminate most of the false tracks. Confirmed tracks are transmitted for track-totrack fusion. The sets of tracks being considered for fusion may contain both true and false tracks. The authors assume that each track information also includes the track quality measure in the form of the probability of target existence information. This information is used for additional false track discrimination at the fusion centre. They also use this information to enhance the track-to-track association. They propose three different strategies for track fusion with the target existence information: the 'single target', the 'joint multitarget' and the 'linear multitarget'.

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The joint target tracking and classification using target-to-sensor aspect-dependent Radar Cross Section (RCS) and kinematic data for multistatic sonar network is presented in this paper. The scattered signals measured from different orientations of a target may vary due to aspect-dependant RCS. A complex target may contain several dozen significant scattering centers and dozens of other less significant scatterers. Because of this multiplicity of scatterers, the net RCS pattern exhibits high variation with aspect angle. Thus, radar cross sections from multiple aspects of a target, which are obtained via multiple sensors, will help in accurately determining the target class. By modeling the deterministic relationship that exits between RCS and target aspect, both the target class information and the target orientation can be estimated. Kinematic data are also very helpful in determining the target class as it describes the target motion pattern and its orientation. The proposed algorithm exploits the inter-dependency of target state and the target class using aspect-dependent RCS and kinematic information in order to improve both the state estimates and classification of each target. The simulation studies demonstrate the merits of the proposed joint target tracking and classification algorithm based on aspect-dependant RCS and kinematic information.

In this paper, a new joint target tracking and classification technique based on Observable Operator Models (OOM) is considered. The OOM approach, which has been proposed as a better alternative to the Hidden Markov Model (HMM), is used to model the stochastic process of target classification. These OOMs afford both mathematical simplicity and algorithmic efficiency compared to HMM. Conventional classification techniques use only the feature information from target signatures. The proposed OOM based classification technique incorporates the target-to-sensor orientation together with a sequence of feature information from multiple sensors. The target-to-sensor orientation evolves over time and the target aspect is important in determining the target classes. The multi-aspect classification is modeled using OOM to handle unknown target orientation. This algorithm exploits the inter-dependency of target state and the target class, which improves both the state estimates and classification of each target. Measurement ambiguity is present in both kinematic and feature measurement and therefore, the OOM based classifier is integrated into the multiframe data association framework that is used to resolve measurement origin uncertainties. This technique enables one to overcome ambiguity in feature measurements while improving track purity. A two dimensional example demonstrates the merits of the proposed OOM based joint target tracking and classification algorithm.

In this paper, we present an efficient data association algorithm for tracking ground targets that perform movestop-move maneuvers using ground moving target indicator (GMTI) radar. A GMTI radar does not detect the targets whose radial velocity falls below a certain minimum detectable velocity. Hence, to avoid detection enemy targets deliberately stop for some time before moving again. When targets perform move-stop-move maneuvers, a missed detection of a target by the radar leads to an ambiguity as to whether it is because the target has stopped or due to the probability of detection being less than one. A solution to track move-stop-move target tracking is based on the variable structure interacting multiple model (VS-IMM) estimator in an ideal scenario (single target tracking with no false measurements) has been proposed. This solution did not consider the data association problem. Another solution, called two-dummy solution, considered the data association explicitly and proposed a solution based on the multiframe assignment algorithm. This solution is computationally expensive, especially when the scenario is complex (e.g., high target density) or when one wants to perform high dimensional assignment. In this paper, we propose an efficient multiframe assignment-based solution that considers the second dummy measurement as a real measurement than a dummy. The proposed algorithm builds a less complex assignment hypothesis tree, and, as a result, is more efficient in terms of computational resource requirement.