KEYWORDS: Particles, Field programmable gate arrays, Clocks, Nonlinear filtering, Particle filters, Digital signal processing, Signal processing, Control systems, Logic, Process modeling
The particle flow filters, proposed by Daum & Hwang, provide a powerful means for density-based nonlinear filtering but their computation is intense and may be prohibitive for real-time applications. This paper proposes a design for superfast implementation of the exact particle flow filter using a field-programmable gate array (FPGA) as a parallel environment to speedup computation. Simulation results from a nonlinear filtering example are presented to demonstrate that using FPGA can dramatically accelerate particle flow filters through parallelization at the expense of a tolerable loss in accuracy as compared to nonparallel implementation.
This paper is Part VIc of a comprehensive survey of maneuvering target tracking without addressing the so-called
measurement-origin uncertainty. It provides an in-depth coverage of various approximate density-based nonlinear filters
in discrete time developed particularly for handling the uncertainties induced by potential target maneuvers as well as nonlinearities
in the dynamical systems commonly encountered in target tracking. An emphasis is given to more recent results,
especially those with good potential for tracking applications.
This paper is Part VIb of a comprehensive survey of maneuvering target tracking without addressing the so-called
measurement-origin uncertainty. It provides an in-depth coverage of various approximate density-based nonlinear filters
in mixed time developed particularly for handling the uncertainties induced by potential target maneuvers as well as nonlinearities
in the dynamical systems commonly encountered in target tracking. An emphasis is given to the more recent results,
especially those with good potential for tracking applications. Approximate nonlinear filtering techniques for point estimation
have been covered in a previous part. Approximate nonlinear filtering in discrete time and sampling-based nonlinear filters
will be surveyed in forthcoming parts.
This paper is Part VIa of a comprehensive survey of maneuvering target tracking without addressing the so-called
measurement-origin uncertainty. It covers theoretical results of density-based exact nonlinear filtering for handling the uncertainties
induced by potential target maneuvers as well as nonlinearities in the dynamical systems commonly encountered
in target tracking. An emphasis is given to the results of significance for practical considerations, especially those of good
potential for tracking applications.
This paper deals with models of ballistic target (BT) motion during the boost phase for target tracking. Different
options to improve the accuracy of modeling are discussed and several enhanced models are proposed. They include
simple kinematic models of the so-called gravity turn (GT) target motion and more sophisticated models, accounting for
the BT flight dynamics during boost, as well. Tracking simulations are presented.
This paper proposes a multiple-model (MM) hypothesis testing approach for detection of unknown target maneuvers that may have several possible prior distributions. An MMmaneuver detector based on sequential hypothesis testing is developed. Simulation results that compare the performance of the proposed MM detector to that of traditional maneuver detectors are presented. They demonstrate that the new sequential MM detector outperforms traditional multiple hypothesis testing based detectors when the prior acceleration distributions are unknown.
Many multiple-model (MM) algorithms for tracking maneuvering targets are available, but there are few comparative studies of their performance. This work compares seven MM algorithms for maneuvering target tracking in terms of tracking performance and computational complexity. Six of them are well known and widely used. They are the autonomous multiple-model algorithm, generalized pseudo-Bayesian algorithm of first order (GPB1), and of second order (GPB2), interacting multiple-model (IMM) algorithm, B-best based MM algorithm, and Viterbi-based MM algorithm. Also considered is the reweighted interacting multiple-model algorithm, which was developed recently. The algorithms were compared using three scenarios. The first scenario consists of two segments of tangential acceleration while the second scenario consists of two segments of normal acceleration. Both of these scenarios have maneuvers that are represented by one of the models in the model set. The third scenario, however, has a single maneuver that consists of a tangential and a normal acceleration. This type of maneuver is not overed by the model set and is used to see how the algorithms react to a maneuver outside of the model set. Based on the study, there is no clear-cut best algorithm but the IMM algorithm has the best computational complexity among the algorithms that have acceptable tracking errors. It also showed a remarkable robustness to model mismatching, and appears to be the top choice if the computational cost is of concern.
This is a part of Part VI (nonlinear filtering) of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I [52] and Part II [48] deal with target motion models. Part III [49], Part IV [50], and Part V [51] cover measurement models, maneuver detection based techniques, and multiple-model methods, respectively. This part surveys approximation techniques for point estimation of nonlinear dynamic systems that are general, applicable to a wide spectrum of nonlinear filtering problems, especially those in the context of maneuvering target tracking. Three classes of such techniques are survey here: function approximation, moment approximation, and stochastic model approximation.
In tracking applications, target dynamics is usually modeled in the Cartesian coordinates, while target measurements are directly available in the original sensor coordinates. Measurement conversion is widely used to do linearization such that the Kalman filter can be applied in the Cartesian coordinates. A number of improved measurement-conversion techniques have been proposed recently. However, they have fundamental limitations, resulting in performance degradation, as pointed out in Part III of a recent survey conducted by the authors. This paper proposes a recursive filter that is theoretically optimal in the sense of minimizing the mean-square error among all linear unbiased filters in the Cartesian coordinates. The proposed filter is free of the fundamental limitations of the measurement-conversion approach. Results of an approximate implementation for measurements in the spherical coordinates are compared with those obtained by two state-of-the-art conversion techniques. Simulation results are provided.
This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods---the use of multiple models (and filters) simultaneously---which is the prevailing approach to maneuvering target tracking in the recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes on the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.
This is the fourth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers the measurement models and the associated techniques. This part surveys tracking techniques that are based on decisions regarding target maneuver. Three classes of techniques are identified and described: equivalent noise, input detection and estimation, and switching model. Maneuver detection methods are also included.
This paper is the second part in a series that provides a comprehensive survey of the problems and techniques of tracking maneuvering targets in the absence of the so-called measurement-origin uncertainty. It surveys motion models of ballistic targets used for target tracking. Models for all three phases (i.e., boost, coast, and reentry) of motion are covered.
This is the third part of a series of papers that provide a comprehensive survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with general target motion models and ballistic target motion models, respectively. This part surveys measurement models, including measurement model-based techniques, used in target tracking. Models in Cartesian, sensor measurement, their mixed, and other coordinates are covered. The stress is on more recent advances - topics that have received more attention recently are discussed in greater details.
KEYWORDS: Motion models, 3D modeling, Mathematical modeling, Process modeling, 3D acquisition, Kinematics, Systems modeling, Data modeling, Detection and tracking algorithms, Performance modeling
This is the first part of a series of papers that provide a comprehensive and up-to-date survey of the problems and techniques of tracking maneuvering targets in the absence of the so-called measurement-origin uncertainty. It surveys the various mathematical models of target dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target dynamics. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among the models surveyed and insight to the pros and cons of the models are provided. Some material presented here has not appeared elsewhere.
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