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6th International Conference on Signal Processing, ICSP 2002

作者： Behazin, Farid Nabavi, Abdolreza Fesharaki, Mehdi N. YMA College P.O. Box 19585-774 Tehran Iran Electrical Engineering Department Tarbiat-Modarres University Tehran Iran

ISBN: (纸本)0780374886

This paper presents a reformulation on iterated EKF (IEKF) using inverse covariance form, and two modifications on it. The reformulation makes computation saving, particularly when the dimension of measurement is large. The modifications reduce the mean and standard deviation of estimation error. The performance comparison of proposed algorithms against EKF is presented using a number of simulation examples in a video tracking for small target application. © 2002 ieeE.

关键词： Extended Kalman filters

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iee Workshop target tracking: algorithms and applications

IEE Workshop Target Tracking: Algorithms and Applications

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iee target tracking: algorithms and applications (Ref. No. 2001/174)

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A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking

A tutorial on particle filters for on-line nonlinear/non-Gau...

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iee target tracking: algorithms and applications (Ref. No. 2001/174)

作者： S. Maskell N. Gordon Engineering Department University of Cambridge UK Qinetiq Limited UK

Bayesian methods provide a rigorous general framework for dynamic state estimation problems. We describe the nonlinear/non-Gaussian tracking problem and its optimal Bayesian solution. Since the optimal solution is intractable, several different approximation strategies are then described. These approaches include the extended Kalman filter and particle filters. For a particular problem, if the assumptions of the Kalman filter hold, then no other algorithm can out-perform it. However, in a variety of real scenarios, the assumptions do not hold and approximate techniques must be employed. The extended Kalman filter approximates the models used for the dynamics and measurement process, in order to be able to approximate the probability density by a Gaussian. Particle filtering approximates the density directly as a finite number of samples. A number of different types of particle filter exist and some have been shown to outperform others when used for particular applications. However, when designing a particle filter for a particular application, it is the choice of importance density that is critical. These notes are of a tutorial nature and so, to facilitate easy implementation, 'pseudo-code' for algorithms are included at relevant points.

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tracking trends in the chemical composition of systems using chromatic mapping

Tracking trends in the chemical composition of systems using...

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iee seminar on Intelligent and Self-Validating Instruments - Sensors and Actuators (Ref. No. 2001/179)

作者： J. Zhang X. Du W.D. Yuan A. Deakin J.W. Spencer G.R. Jones J.R. Gibson W.B. Hall A. McGrail H. Tonge Centre for Intelligent Monitoring Systems University of Liverpool UK VA TECH Reyrolle ACP Limited UK NGC Leatherhead UK AMEC Inc. Kuwait

The application of chromatic methodology has been extended to provide a means of tracking trends in the chemical composition of three different systems. The first system is a high voltage oil filled transformer where unwanted discharge activity may lead to the formation of a range of decomposition products. The second is a waste reduction facility which involves the production of methane. The third examines the recombination of SF/sub 6/ gas which is used as an electrical arc quenching medium for fault current interruption on electrical distribution and transmission systems. The chemical composition for the first application is obtained from residual gas analysis and for the second and third applications from mass spectrometers. The results show that for all these cases the chromatic algorithms permits the tracking of trends in the chemical composition and direct interpretation of these chromatic parameters, enabling rapid assessment of the status of the system without being overwhelmed by incumbent data.

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Joint super-resolution moving target feature extraction and stationary clutter suppression

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iee PROCEEDINGS-RADAR SONAR AND NAVIGATION 2000年第1期147卷 23-29页

作者： Bi, Z Wu, R Li, J Williams, R Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA USAF Res Lab Wright Patterson AFB OH 45433 USA

High range resolution (HRR) moving target indicator radar is becoming increasingly important for many military and civilian applications involving the detection and classification of moving targets within a clutter background. For ground-based HRR radar, when targets are moving slowly or near-broadside and the coherent processing interval or dwell time is not too long, the effects of range migration and range feature distortion can be ignored. Based on this assumption, relaxation-based algorithms that are robust and computationally simple are proposed for HRR feature extraction of moving targets consisting of scatterers closely spaced in range in the presence of stationary clutter. Numerical examples show that the proposed algorithms exhibit super-resolution and excellent estimation performance.

关键词： range migration Signal detection radar detection clutter background radar resolution estimation performance Radar theory feature extraction civilian applications radar tracking Electromagnetic compatibility and interference radar clutter dwell time relaxation-based algorithms stationary clutter suppression moving target detection stationary clutter coherent processing interval moving target indicator radar HRR feature extraction ground-based HRR radar moving target classification electromagnetic wave scattering high range resolution target tracking signal classification range feature distortion military applications interference suppression scatterers super-resolution moving target feature extraction

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Identification and tracking of maritime objects in near-infrared image sequences for collision avoidance

Identification and tracking of maritime objects in near-infr...

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Proceedings of the 1999 7th International Conference on Image Processing and its applications

作者： Smith, A.A.W. Teal, M.K. Bournemouth Univ United Kingdom

This paper describes the continuing development of an image processing system for use on high-speed passenger ferries. The system automatically identifies objects in a maritime scene and uses the detected motion to alert a human observer to potential collision situations. Three integrated image-processing algorithms, namely an image pre-processor, a motion cue generator, and a target tracker, perform the identification and tracking of maritime objects. The pre-processing filters the image and applies a histogram technique to segment the sea from potential objects of interest. The segmented image is passed to the motion cue generator, which provides motion cues based on the differences between consecutive frames of segmented image data. The target tracker applies dynamic constraints on object motion to solve the correspondence problem, thus increasing the confidence that an identified object is a target. Identified and tracked objects are highlighted to a human observer using a white box viewing cue placed directly around the object of interest.

关键词： Object recognition

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target tracking: algorithms and applications

Target tracking: algorithms and applications

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iee Colloquium on Trget tracking: algorithms and applications (Ref. No. 1999/090, 1999/215)

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A novel local likelihood approach to data fusion in passive target tracking

A novel local likelihood approach to data fusion in passive ...

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iee Colloquium on Trget tracking: algorithms and applications (Ref. No. 1999/090, 1999/215)

作者： P. Gerbino M. Ali TCESP DOIS Royal Military College of Science Cranfield University Wiltshire UK

In the class of target tracking of slow time varying targets using bearings only information, it can be inappropriate to use recursive estimation schemes such as the Kalman class of filters. In particular the use of a unimodal Kalman estimator to estimate a target state can produce erratic estimates due to the presence of manuevers. There is a need to produce smooth, optimal estimates of the target state in time in the presence of a maneuver from the target. In all cases these estimates are based on a number of noise corrupted bearing measurements from a number of sensors. Using recursive systems in this situation can be difficult due to poor conditioning and divergence in the solution due to observability problems. In this paper it is suggested that by employing local maximum likelihood estimates, which are smoothed with Gaussian kernels, one can produce a better fit of the bearing data for a target carrying out a maneuver. Results in this paper show that the extended Kalman filter in bearings only passive target tracking reports higher errors than the local likelihood estimation scheme suggested.

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Topography-based VS-IMM estimator for large-scale ground target tracking

Topography-based VS-IMM estimator for large-scale ground tar...

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iee Colloquium on Trget tracking: algorithms and applications (Ref. No. 1999/090, 1999/215)

作者： T. Kirubarajan Y. Bar-Shalom K.R. Pattipati Electrical and Systems Engineering Departincnt University of Connecticut Storrs CT USA

The Interacting Multiple Model (IMM) estimator has been shown to be very effective in various target tracking problems. It is possible to vary the set of models in the IMM estimator based on some criteria to yield better estimates. This results in a Variable Structure IMM (VS-IMM) estimator where the mode set not only differs across targets, but also varies with time for a given target. This paper addresses the problem of tracking convoys of ground targets using Moving target Indicator (MTI) reports to illustrate the development, operation and the benefits of an adaptive VS-IMM estimator. The targets under track are moving along a constrained path, for example, a highway, with varying obscuration due to changing terrain conditions. Here we present a VS-IMM estimator, where filter modules are adaptively modified, added or removed depending on the terrain topography, for tracking on-road and off-road targets within the same framework. At each scan, the structure of the estimator for every target is individually modified based on the known topography of the surveillance region and the predicted location of the target. This enables the estimator to handle the variation in the possible motion modes across targets as well as with time for each target. Because of the nonlinear nature of the MTI reports, which consist of two-dimensional positions and range rate, extended Kalman filters are used as IMM estimator modules.

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Multiple model tracking with intermittent mode observations

Multiple model tracking with intermittent mode observations

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iee Colloquium on Trget tracking: algorithms and applications (Ref. No. 1999/090, 1999/215)

作者： S. Allam F. Dufour P. Bertr Lc.aFbro ratoire des Signaux et Systemes CNRS-Supélec Gif-Sur-Yvette France

This paper is focused on the estimation of both mode and state of a hybrid system when intermittent mode observations are available. These intermittent data are modeled by a marked point process. The exact hybrid filter based on this formalism is elaborated by use of the reference probability method. A recursive form of the exact conditional density is given in the general case as well as in the initial Gaussian case. A suboptimal and finite dimensional filter extracted from the previous form is used to design a radar plus imaging sensor maneuvering target tracker. Simulations show the efficiency of this new tracker.

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