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The Histogram Probabilistic Multi-Hypothesis Tracker is a multi-target track before detect method with low computational complexity. However, for high resolution sensors with millions of pixels, a naive implementation can be too slow. Single target chip processing is proposed as an approximate implementation scheme that dramatically reduces computation cost without significant performance degradation. Simulated examples are used to quantify the improvements achieved. [ABSTRACT FROM AUTHOR]

The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. The original implementations of H-PMHT dealt with Gaussian shaped targets with fixed or known extent. More recent applications have addressed other special cases of the target shape. This article reviews these recent extensions and consolidates them into a new unified framework for targets with arbitrary appearance. The framework adopts a stochastic appearance model that describes the sensor response to each target and describes filters and smoothers for several example models. The article also demonstrates that H-PMHT can be interpreted as the decomposition of multi-target track-before-detect into decoupled single target track-before-detect using the notion of associated images. [ABSTRACT FROM PUBLISHER]

Track-Before-Detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Existing literature uses only the envelope of complex data; this article presents an approach that also includes the phase information. The inclusion of phase is shown to both improve the discrimination of targets from noise and reduce the computation overhead, with improved performance demonstrated using three representative algorithms. [ABSTRACT FROM AUTHOR]