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A majority of target tracking algorithms are derived assuming the infinite resolution sensor(s), where each measurement has only one source. This assumption is an approximation as the sensors (e.g., radars and sonars) have finite resolution, where close measurements from multiple sources may merge into one. This disparity between assumptions and reality may significantly degrade target tracking performance. This paper proposes a multitarget tracking algorithm that assumes finite resolution sensor(s). A finite resolution variant of linear multitarget tracking is proposed to limit the computational complexity. The proposed algorithm provides both the trajectory estimate and the track quality measure enabling automatic track initiation and termination (false track discrimination) for a complete target tracking solution in nonhomogeneous clutter. A simulation study vindicates the approach in a difficult situation with 10 crossing targets in clutter that may merge measurements. [ABSTRACT FROM AUTHOR]

In a target tracking situation, with clutter measurements and uncertain detections, true tracks often get lost due to temporal occlusions or unfavourable detection sequences, as well as the effects of measurement noise, clutter measurements, or any combination thereof. After a detection delay a new track is then initialized and subsequently confirmed for the same target, and the track becomes segmented. The Markov chain two (MC2) model for target existence propagation includes this temporal loss of detectability and often allows the track to continue through adverse conditions. This paper integrates the MC2 model with the integrated track splitting (ITS) algorithm resulting in a significant reduction of track segmentation. [ABSTRACT FROM PUBLISHER]

Target tracking algorithms usually treat the probability of detection as independent of the target state. In most cases, this assumption is not true, with subsequent degradation in the target tracking performance from both expected and optimal levels. One typical example is the Doppler frequency based clutter rejection, the other is obfuscation (shadowing) of ground based targets, and the third is antijamming notch filtering. This dependence of the probability of target detection on the target trajectory state modulates the measurement likelihood, which, in turn, introduces measurement nonlinearity. In this paper, we first present a general algorithm for target tracking in clutter when the probability of detection is target state dependent, and then proceed to an algorithm where both target state estimate and the probability of detection are modeled by Gaussian mixtures. The probability of target existence is recursively updated as the track quality measure used for false track discrimination. A two-sensor-based ground maneuvering target tracking in clutter simulation validates this approach. [ABSTRACT FROM AUTHOR]

Assume an electronically steered antenna (ESA) radar. In between the search mode operations (where the radar completes one scan of the surveillance area), we may optimize performance by judiciously updating individual tracks. The optimization is often translated into the choice of the next track to update. We propose a resource allocation scheme designed to minimize the response time to changing target situation; i.e., confirmation of new tracks following new targets, and termination of tracks without targets. The resource allocation cost is a function of the probability of target existence of individual tracks. Approximative expressions are derived and verified. Simulation results show that resource allocation based on this cost significantly improves the response time of the system to the changes in target situation. As a spurious benefit, some of the sensor time is liberated for other activities. [ABSTRACT FROM PUBLISHER]