Kevin Gilholm

Bio: Kevin Gilholm is an academic researcher from Qinetiq. The author has contributed to research in topics: Coded aperture & Computational photography. The author has an hindex of 4, co-authored 7 publications receiving 305 citations.

Poisson models for extended target and group tracking

Abstract: It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, the measurements are modelled as a Poisson process with a spatially dependent rate parameter. This representation allows us to model extended targets as an intensity distribution rather than a set of points and, for a target formation, it gives the option of modelling part of the group as a spatial distribution of target density. Furthermore, as a direct consequence of the Poisson model, the measurement likelihood may be evaluated without constructing explicit association hypotheses. This considerably simplifies the filter and gives a substantial computational saving in a particle filter implementation. The Poisson target-measurement model will be described and its relationship to other filters will be discussed. Illustrative simulation examples will be presented.

Adaptive coded aperture imaging in the infrared: towards a practical implementation

Abstract: An earlier paper [1] discussed the merits of adaptive coded apertures for use as lensless imaging systems in the thermal infrared and visible It was shown how diffractive (rather than the more conventional geometric) coding could be used, and that 2D intensity measurements from multiple mask patterns could be combined and decoded to yield enhanced imagery Initial experimental results in the visible band were presented Unfortunately, radiosity calculations, also presented in that paper, indicated that the signal to noise performance of systems using this approach was likely to be compromised, especially in the infrared This paper will discuss how such limitations can be overcome, and some of the tradeoffs involved Experimental results showing tracking and imaging performance of these modified, diffractive, adaptive coded aperture systems in the visible and infrared will be presented The subpixel imaging and tracking performance is compared to that of conventional imaging systems and shown to be superior System size, weight and cost calculations indicate that the coded aperture approach, employing novel photonic MOEMS micro-shutter architectures, has significant merits for a given level of performance in the MWIR when compared to more conventional imaging approaches

A wavelet-based method for finding inputs of given energy which maximize the outputs of nonlinear systems

Abstract: The invention herein is directed toward a process of preparing free flowing thermoset resin particles of polybutadiene and includes the steps of physically mixing a filler material with polybutadiene followed by chopping the mixture into small particles or granules which are irregularly shaped. A vulcanizing agent can be added during the mixing step as well as other ingredients commonly added to polybutadiene resin formulations, as is customarily done in the art, without affecting the free flowing properties of the polybutadiene treated according to the process of this invention.

Tracking point targets at sub-pixel accuracy in adaptive coded aperture imagery without decoding

Abstract: Coded aperture imagery poses a challenge for traditional image tracking algorithms because of the highly distributed nature of the coded imagery Traditional algorithms would require this imagery to undergo a computationally expensive decoding operation before subsequent processing and tracking In this paper, a novel tracking algorithm is described that can track point-source targets at sub-pixel accuracy without requiring the coded aperture imagery to be decoded Furthermore, it is shown that the algorithm is robust to changes in the coded aperture mask pattern, and so is suitable for use in adaptive coded aperture imaging systems Some results of the algorithm on synthetic and initial MWIR experimental data will be given

An experimental infrared sensor using adaptive coded apertures for enhanced resolution

Abstract: Adaptive coded aperture imaging (ACAI) has the potential to enhance greatly the performance of sensing systems by allowing sub detector pixel image and tracking resolution. A small experimental system has been set up to allow the practical demonstration of these benefits in the mid infrared, as well as investigating the calibration and stability of the system. The system can also be used to test modeling of similar ACAI systems in the infrared. The demonstrator can use either a set of fixed masks or a novel MOEMS adaptive transmissive spatial light modulator. This paper discusses the design and testing of the system including the development of novel decoding algorithms and some initial imaging results are presented.

Tracking of Extended Objects and Group Targets Using Random Matrices

Abstract: The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Due to limited sensor resolution capabilities, group targets (i.e., a number of closely spaced targets moving in a coordinated fashion) may show a similar detection pattern as extended objects, namely a varying number of detections whose spread is determined by both the statistical sensor errors as well as the physical extension of the group or extended object. In both cases, tracking and data association under the “one target-one detection” assumption are no longer applicable. This paper deals with the problem of maintaining a track for an extended object or group target with varying number of detections. Herein, object extension is represented by a symmetric positive definite random matrix. A recently published Bayesian approach to tackling this problem is analyzed and discussed. From there, a new approach is derived that is expected to overcome some of the weaknesses the mentioned Bayesian approach suffers from in certain applications.

Tracking of Extended Objects and Group Targets using Random Matrices - A Performance Analysis.

Abstract: The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Due to limited sensor resolution capabilities, group targets (i. e., a number of closely spaced targets moving in a coordinated fashion) may show a similar detection pattern as extended objects, namely a varying number of detections whose spread is determined by both the statistical sensor errors as well as the physical extension of the group or extended object. Different tracking approaches treating these situations have been proposed where physical extension is represented by a symmetric positive definite random matrix. In this paper, a recently published Bayesian approach is discussed with regard to the estimator’s self-assessment of the estimation error for both kinematics and extension.

A phd Filter for Tracking Multiple Extended Targets Using Random Matrices

Abstract: This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices For this purpose, the random matrix framework developed recently by Koch is adapted into the extended target phd framework, resulting in the Gaussian inverse Wishart phd (giw-phd) filter A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans

Shape tracking of extended objects and group targets with star-convex RHMs

Abstract: This paper is about tracking an extended object or a group target, which gives rise to a varying number of measurements from different measurement sources. For this purpose, the shape of the target is tracked in addition to its kinematics. The target extent is modeled with a new approach called Random Hypersurface Model (RHM) that assumes varying measurement sources to lie on scaled versions of the shape boundaries. In this paper, a star-convex RHM is introduced for tracking star-convex shape approximations of targets. Bayesian inference for star-convex RHMs is performed by means of a Gaussian-assumed state estimator allowing for an efficient recursive closed-form measurement update. Simulations demonstrate the performance of this approach for typical extended object and group tracking scenarios.

Extended Target Tracking using a Gaussian-Mixture PHD Filter

Abstract: This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.