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Bayesian information fusion and multitarget tracking for maritime situational awareness

iet RADAR SONAR AND NAVIGATION 2020年第12期14卷 1845-1857页

作者： Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z. NATO Ctr Maritime Res & Expt CMRE La Spezia Italy Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn Dept La Jolla CA 92093 USA TU Wien Inst Telecommun Vienna Austria MIT Lab Informat & Decis Syst LIDS 77 Massachusetts Ave Cambridge MA 02139 USA

The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

关键词： target tracking ships oceanographic techniques sensor fusion radar detection Bayes methods radar tracking geophysical signal processing marine radar Bayesian information fusion maritime situational awareness wide-area operational picture ship traffic coastal areas central sensing modality high-frequency surface-wave radars sea areas over-the-horizon distances low environmental footprint low power requirements MSA applications dedicated information fusion tracking algorithms multisensor-multitarget tracking problem Bayesian framework loopy sum-product algorithm suitably devised factor graph authors context-based information information fusion algorithms HFSW radar data

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Design and implementation of multi-feature fusion kernel correlation filtering algorithm based on HLS 5

Design and implementation of multi-feature fusion kernel cor...

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iet International Radar Conference 2020, iet IRC 2020

作者： Cong, Peiyu Xie, Min Yang, Kaiming Zhang, Xiaofeng Su, Hongyan Fu, Xiongjun School of Information and Electronics Beijing Institute of Technology Beijing China Science and Technology on Millimeter-Wave Laboratory Beijing Institute of Remote-Sensing Equipment Beijing China

ISBN: (纸本)9781839535635

In order to balance the accuracy and real-time performance of the moving target tracking system, an optimized design and implementation method based on high-level synthesis (HLS) of multi-feature fusion with kernel correlation filtering algorithms on FPGA is designed. This design improves the KCF algorithm with LBP and HOG features, and proposes a new dimensionality reduction method for LBP, which enhances the real-time performance while maintaining effective extraction of target features. The algorithm is implemented with FPGA, and a well acceleration effect is obtained on the basis of high precision. In test, the frame rate reaches 35 frames per second. Finally, it is verified through simulation that this feature extraction method can be used to process various image data such as infrared detection and SAR radar imaging, and has a wide range of applications. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Design and implementation of multi-feature fusion kernel correlation filtering algorithm based on HLS

Design and implementation of multi-feature fusion kernel cor...

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iet International Radar Conference (iet IRC 2020)

作者： P. Cong M. Xie K. Yang X. Zhang H. Su X. Fu Beijing Institute of Technology Beijing China Beijing Institute of Remote-sensing Equipment Beijing China

关键词： target tracking filtering theory feature extraction image fusion synthetic aperture radar field programmable gate arrays object detection radar imaging

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Linear multitarget integrated probabilistic data association for multiple detection target tracking

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iet RADAR SONAR AND NAVIGATION 2018年第9期12卷 945-953页

作者： Huang, Yuan Song, Taek Lyul Kim, Da Sol Hanyang Univ Dept Elect Syst Engn Ansan South Korea LIG Nex1 Co Ltd Naval Combat Syst Seongnam South Korea

The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multiple detection problem. The typical algorithms with the point target assumption are not eligible for multiple detection tracking environments. The multiple detection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multiple detection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multiple detection multitarget tracking scenarios.

关键词： sensor fusion target tracking probability computational complexity multiple detection linear multitarget integrated probabilistic data association multiple detection multitarget tracking problem point target assumption multiple scattering points high-resolution sensor multiple detection joint integrated probabilistic data association algorithm computational complexity multiple detection target tracking computational efficiency modulated clutter measurement density measurement cell

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Automotive radar system for multiple-vehicle detection and tracking in urban environments

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iet INTELLIGENT TRANSPORT SYSTEMS 2018年第8期12卷 783-792页

作者： Eltrass, Ahmed Khalil, Mohammed Alexandria Univ Elect Engn Dept Fac Engn Alexandria Egypt Univ Maryland College Pk MD 20742 USA

In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the Joint Probabilistic data Association (JPDA) algorithm for data association, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.

关键词： road vehicle radar radar detection target tracking radar tracking regression analysis probability sensor fusion Kalman filters nonlinear filters automobiles automotive radar system multiple-vehicle detection multiple-vehicle tracking urban environments track management problem low computational cost unscented Kalman filter constant turn rate and acceleration dynamic model single target tracking tracking accuracy linear regression algorithm UKF-CTRA algorithm joint probabilistic data association algorithm CTRA-UKF-LR-JPDA-composite-M/N-tests multitarget tracking

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iet Conference on data fusion and target tracking 2014: algorithms and applications

IET Conference on Data Fusion and Target Tracking 2014: Algo...

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iet Conference on data fusion and target tracking 2014: algorithms and applications

ISBN: (纸本)9781849198639

The proceedings contain 12 papers. The topics discussed include: Bayesian tracking and multi-core beamforming for estimation of correlated brain sources;particle filtering for network-based positioning terrestrial radio networks;tracking simulated UAV swarms using particle filters;rectangular extended object tracking with box particle filter using dynamic constraints;probabilistic step and turn detection in indoor localization;fusing kinect sensor and inertial sensors with multi-rate kalman filter;piecewise constant sequential importance sampling for fast particle filtering;hybrid gauss-hermite filter;regional variance in target number: analysis and application for multi-Bernoulli point processes;combined evidential data association;and PHD filtering in presence of highly structured sea clutter process and tracks with extent.

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iet seminar on target tracking and data fusion: algorithms and applications

IET Seminar on Target Tracking and Data Fusion: Algorithms a...

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iet seminar on target tracking and data fusion: algorithms and applications

ISBN: (纸本)9780863419102

The proceedings contain 23 papers. The topics discussed include: fusion without independence;investigation into the utility of using CFAR cluster size information in target track association;multi-sensor debris tracking;Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models;population based particle filtering;MAP estimation in particle filter tracking;a new algorithm for GMTI tracking problems, subject to a Doppler blind zone constraint;a technique to segment by tracking extended targets manoeuvring through complex scenes;video tracking using dual-tree wavelet polar matching and particle filtering;cluster tracking under kinematical constraints using random matrices;evolving networks for group object motion estimation;fusion of novel biometrics for human tracking and identification;and NATO intelligence surveillance reconnaissance tracking standard NATO STANAG 4676.

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Combined evidential data association

Combined evidential data association

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iet Conference on data fusion and target tracking 2014: algorithms and applications

作者： Dallil, A. Ecole Militaire Polytechnique Algeria

ISBN: (纸本)9781849198639

In this paper, we present a new method for data association in multi-target tracking situation in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal generates two belief matrices using two different specialized basic belief mass assignments. While the decision making process is based on the extension of the frame of hypotheses. The method has been tested for a nearly constant velocity target in two ambiguous cases using Monte Carlo simulations.

关键词： target tracking

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tracking simulated UAV swarms using particle filters

Tracking simulated UAV swarms using particle filters

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iet Conference on data fusion and target tracking 2014: algorithms and applications

作者： Easthope, P.F. L-3 Communications ASA Ltd Rusint House Fleet Hampshire GU51 2QS United Kingdom

ISBN: (纸本)9781849198639

This paper provides an initial examination of the use of particle filters in tracking swarms of small targets such as Unmanned Aerial Vehicles using a radar. From the standpoint of conventional tracking solutions, such swarms present a severe challenge - due not only to the quasi-erratic motion of the UAVs relative to the swarm trajectory as a whole, but also from the effects of the small target size upon radar resolution and detection probability. It is shown here that a particle filter is capable of providing a stable track on the swarm centroid, although not the individual constituent UAVs.

关键词： Unmanned aerial vehicles (UAV)

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Regional variance in target number: Analysis and application for multi-Bernoulli point processes

Regional variance in target number: Analysis and application...

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iet Conference on data fusion and target tracking 2014: algorithms and applications

作者： Delande, E.D. Houssineau, J. Clark, D.E. School of Engineering and Physical Sciences Heriot-Watt University United Kingdom

ISBN: (纸本)9781849198639

In the context of multi-target tracking application, the concept of variance in the number of targets estimated in specified regions of the surveillance scene has been recently introduced for multi-object filters. This article has two main objectives. First, the regional variance is derived for a multi-object representation commonly used in the tracking literature, known as the multi-Bernoulli point process, in which the multi-target state is described with a set of hypothesised tracks with associated existence probabilities. This model is exploited in multi-target applications where it can be assumed that targets evolve independently of each other and generate sensor observations that are uncorrelated with other targets. An illustration of the concept of regional statistics (mean and variance) in target number, and how to interpret them in the broader context of multi-object filtering, it then provided. Possible applications include performance assessment and sensor control for multi-target tracking.

关键词： Clutter (information theory)

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