Showing papers by "Neil Gordon published in 2012"
TL;DR: This paper shows how the Histogram Probabilistic Multi-Hypothesis Tracker approach can be adapted to address the problem of group target tracking where the motion of several targets is correlated.
Abstract: The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric track-before-detect algorithm that has
been shown to give good performance at a relatively low computation cost. Recent research has extended the algorithm
to allow it to estimate the signature of targets in the sensor image. This paper shows how this approach can be adapted to
address the problem of group target tracking where the motion of several targets is correlated. The group structure is treated
as the target signature, resulting in a two-tiered estimator for the group bulk-state and group element relative position.
8 citations
11 Oct 2012
TL;DR: A Bayesian algorithm based on importance sampling for the estimation of θ, which is applicable to asynchronous sensors and does not require prior knowledge of measurement-to-track associations, is proposed.
Abstract: Tracking algorithms are based on models: target dynamic and sensor measurement model. In most practical situations the two models are not known exactly and are typically parametrised by an unknown random vector θ. The paper proposes a Bayesian algorithm based on importance sampling for the estimation of θ. The input are detections/measurements collected by the tracking system from non-cooperative targets. The algorithm relies on the particle filter implementation of the probability density hypothesis (PHD) filter to evaluate the likelihood of the measurement set history conditioned on θ. As a byproduct, the proposed algorithm can also output a multi-target state estimate over time. An application to sensor bias estimation is presented in detail as an illustration. The resulting sensor-bias estimation method is applicable to asynchronous sensors and does not require prior knowledge of measurement-to-track associations.
8 citations
TL;DR: An adaptive coded-aperture imager operating in the midwave IR that can produce images with a resolution better than the detector pixel limit by combining multiple frames of data recorded with different coding is described.
Abstract: We describe an adaptive coded-aperture imager operating in the midwave IR. This consists of a coded-aperture mask, a set of optics, and a 4k×4k focal plane array (FPA). This system can produce images with a resolution better than the detector pixel limit by combining multiple frames of data recorded with different coding. This superresolution capability has been demonstrated both in the laboratory and with targets placed outside, the highest resolution being one-half of the FPA pixel pitch.
4 citations