Murat Efe

Phased array radar (PAR) has attracted considerable attention in civil and military applications due to its capability of performing multiple tasks such as surveillance, tracking and weapon engagement simultaneously. To make better use of limited radar resources and to offer best operating performance, an efficient resource allocation strategy is necessary. Pencil-beams with super narrow beamwidth is prospective to resource-aware design but using them to cover areas of interest especially in cases of maneuvering targets with high motion uncertainty requires more study. Existing works often assume that a beam can cover the entire area of interest and the problem of scheduling small-beamwidth pencilbeam to perform search and track (SAT) efficiently is barely discussed or addressed in literature. In this paper, the problems of tracking with pencil-beam and its beam scheduling optimization are addressed. Three beam scheduling strategies, fixed linear wipe, open-loop linear wipe that uses hierarchical genetic algorithm (HGA), and expected posterior Cramér–Rao lower bound (EPCRLB) based optimal solution, are proposed to solve the mixed integer nonlinear problem (MINP). To handle the partially covered target existence area by pencil-beam, a new concept of predicted expected posterior Cramér–Rao lower bound (P-EPCRLB) is proposed and used as the main optimization criterion for the scheduling strategy. Numerical results demonstrate the superior performance of the proposed EPCRLB based optimal solution strategy and its effectiveness as a proposed solution.

Object superposition is a way to derive Bayesian estimators for multiple object tracking using point processes. A low computational complexity Bayesian multiple target tracking filter, based on target superposition, is presented. The concept of superposition is introduced and applied to the well-known Joint Probabilistic Data Association (JPDA) filter to derive the JPDA with superposition (JPDAS) filter. The JPDAS intensity function is evaluated to machine precision “for free” by computing the generating functional of the posterior process using complex arithmetic. A simulated example with eight targets is presented.

A multiple target tracking filter is developed for merged measurement problems that arise with finite resolution sensors. The resulting combinatorial problem is incorporated directly in the joint likelihood function using Analytic Combinatorics techniques. The Bayesian filter is a sum of several terms that correspond one-to-one to the set of all feasible hypotheses about measurements and objects, i.e., resolved/unresolved, detected/undetected. Performance is demonstrated for two targets in both crossing and parallel target motion scenarios.

Extended target tracking (ETT) is an issue in high resolution radar surveillance, ship tracking, and video tracking. Most previous works focus on tracking an ellipsoidal extended target without measurement origin uncertainty (missed detections and clutter). In this paper, a new estimator called the Gaussian process probabilistic data association (GP-PDA) filter is proposed to track an irregularly shaped extended target with measurement origin uncertainty. First, a generalized measurement model for ETT using Gaussian process (GP) is presented. Both the interior scattering points and the external clutter are considered in this model. Second, a GP-based gating technique is constructed to select validated measurements to feed the filter. Third, the GPPDA filter is proposed to simultaneously estimate the kinematic state and the contour state of the extended target with measurement origin uncertainty. It is proven that the GP-PDA is a generalized version of the classic PDA, and the latter is a special case of the former in the point target tracking applications. Finally, the GP-based posterior Cramer-Rao lower bound (PCRLB) is derived to evaluate performance of the ETT with measurement origin uncertainty. Two cases of the PCRLB are discussed: with the number of scattering points being known and unknown. Simulation results verify the effectiveness of the proposed method.

In this study, the classification problem of the ships that near field of the radar is researched. For this classification process, two dimensional high resolution range profiles obtained from radar simulator are used. In this scope the features extracted from image data that obtained from radar near field and classification success of different classifiers are presented.

The pair correlation function is introduced to target tracking filters that use a finite point process target model as a means to investigate interactions in the Bayes posterior target process. It is shown that the Bayes posterior target point process of the probability hypothesis density (PHD) filter-before using the Poisson point process approximation to close the recursion-is a spatially correlated process with weakly repulsive pair interactions. The reduced Palm target point process is introduced to define the conditional target point process given the state of one or more known targets. Using the intensity function of the reduced Palm process, an approximate two-stage pseudomaximum a posteriori track extractor is developed. The proposed track extractor is formulated for the PHD filter and implemented in a numerical study that involves tracking two nearby targets. Numerical results demonstrate improvement in the mean optimal subpattern assignment statistic for the proposed track extractor in comparison to the Gaussian mixture PHD filter's state extractor.