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Multiple model tracking of manoeuvring targets accounting for standoff jamming information

IET RADAR SONAR AND NAVIGATION 2013年第4期7卷 342-350页

作者： Hou, Jing Li, X. Rong Jing, Zhanrong Northwestern Polytech Univ Dept Elect & Informat Xian 710072 Peoples R China Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA

An Interacting Multiple Model (imm) algorithm for manoeuvring target tracking in the presence of standoff jammer is proposed. In the imm, the conventional Gaussian likelihood is replaced with a Gaussian sum (GS) likelihood, derived from a sensor model accounting for both the measurements and jamming information. Thus, the model-conditioned posterior probability density function of the state is also a weighted sum of Gaussians but with recalculated weights. As a result of the combination of multiple models and jamming information, the proposed approach has a significant performance improvement when both manoeuvre and jamming occur. Simulation results show that the proposed approach outperforms the original imm algorithm as well as the GS filters with only one model in terms of track loss and track accuracy.

关键词： Gaussian processes jamming target tracking multiple model tracking manoeuvring targets standoff jamming information interacting multiple model imm algorithm Gaussian sum likelihood model-conditioned posterior probability density function GS filters track loss track accuracy

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PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2013年第7期29卷 1751-1757页

作者： Liu, Zhigang Wang, Jinkuan Xue, Yanbo Northeastern Univ Inst Engn Optimizat & Smart Antenna Qinhuangdao 066004 Peoples R China McMaster Univ Cognit Syst Lab Hamilton ON L8S 4K1 Canada

Recent years have witnessed the shift of wireless sensor networks (WSNs) from theoretical research to practical applications. Due to their simplicity, range-based sensor networks have been widely used. To track a maneuvering target in range-based sensor networks, first, we derive the relationship between the multiple model posterior Cramer-Rao lower bound (PCRLB) and the distance from the sensor to the target, which forms the basis of choosing the subset of candidate sensors that may attend the incoming tracking event. Second, we design two optimization strategies under the communication constraint, namely the optimal sensor selection and cluster head selection. Third, we can estimate the state of the maneuvering target by making use of the interacting multiple model (imm) algorithm and predict the model index one time step ahead. Last, simulation results show the effectiveness of the proposed schemes. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Sensor selection Posterior Cramer-Rao lower bound Target tracking imm algorithm

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Ground Target Tracking of Vehicles in a Wireless Ground Sensor Network

Ground Target Tracking of Vehicles in a Wireless Ground Sens...

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International Conference on Information Fusion

作者： Mats Ekman Henrik P?lsson Security and Defense Solutions Saab AB J?rf?lla SE-175 88 Sweden

ISBN: (纸本)9781467304177

In this paper data from a wireless unattended ground sensor network is used for tracking vehicles travelling in a road network. A multiple model particle filter algorithm, taking into account the road information, is studied. The algorithm enables tracking of targets driving both on and off road. In the PF algorithm two different dynamic models, a second order linear Gaussian model and a constant velocity model, are used to represent the on and off road motion. In order to handle realistic ground target tracking scenarios, the tracking algorithm is integrated into a multiple-sensor tracking system platform which is able to handle real data from a wireless ground sensor network. The performance of the ground target tracking algorithm is evaluated using real data together with simulated data.

关键词： Particle Filter Wireless Ground Sensor Network Ground Target Trackin imm algorithm

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PCRLB-based Optimal Sensor Selection for Maneuvering Target Tracking

PCRLB-based Optimal Sensor Selection for Maneuvering Target ...

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7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)

作者： Liu, Zhigang Wang, Jinkuan Northeastern Univ Inst Engn Optimizat & Smart Antenna Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9781424462520

To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the multiple model posterior Cramer-Rao lower bound(PCRLB), and on the basis of this bound, choose the sensor subset that may attend the incoming tracking event. Secondly, we design the sensor selection strategy under communication constraint, and further pick up the optimal sensor. Thirdly, we can estimate the state of the maneuvering target by making use of the interacting multiple model(imm) algorithm. Finally, the simulation results show the effectiveness of the proposed scheme.

关键词： Covariance matrix Equations imm algorithm Mathematical model PCRLB-based optimal sensor selection Prediction algorithms Radar tracking Signal processing Target tracking interacting multiple model algorithm maneuvering target tracking multiple model posterior Cramer-Rao lower bound optimal sensor range-based sensor networks sensor placement sensor placement target tracking wireless sensor networks wireless sensor networks

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A multiple model approach to doubly-selective channel estimation using exponential basis models

A multiple model approach to doubly-selective channel estima...

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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Song, Liying Tugnait, Jitendra K. Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

ISBN: (纸本)9781424414833

An adaptive channel estimation scheme, exploiting the over-sampled complex exponential basis expansion model (CEBEM), is presented for doubly-selective channels where we track the BEM coefficients via a multiple model approach. In the past work the number of BEM coefficients used to model the doubly-selective channels for channel estimation has been based on an upperbound on the channel Doppler spread. Higher the Doppler spread, more the number of BEM coefficients leading to higher channel estimation variance. In this paper we propose to use a multiple model framework where several candidate Doppler spread values are used to cover the range from zero to an upperbound, leading to multiple CE-BEM channel models, each corresponding to an assumed value of the Doppler spread. Subsequently the well-known interacting multiple model (imm) algorithm is used for symbol detection based on multiple state-space models corresponding to the multiple estimated channels. A simulation example is presented to illustrate the proposed approach.

关键词： doubly-selective channels adaptive channel estimation basis expansion models imm algorithm

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A non-linear filtering and predication (NFP) method for maneuvering target tracking

A non-linear filtering and predication (NFP) method for mane...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XV

作者： Chen, H. Chang, K. C. George Mason Univ Dept SEOR 4400 Univ Dr Fairfax VA 22030 USA

ISBN: (纸本)0819462918

A new non-linear filtering and predication (NFP) algorithm with input estimation is proposed for maneuvering target tracking. In the proposed method, the acceleration level is determined by a decision process, where a least squares (LS) estimator plays a major role to detect target maneuvering within a sliding window. In this paper, we first illustrate that the optimal solution to minimize the mean squared error (MSE) must consider a trade-off between the bias and error variance. For the application of target tracking, we then derive the MSE of target positions in a close form by using orthogonal space decompositions. Then we discuss the NFP estimator, and evaluate how well the approach potentially works in the case of given system parameters. Comparing with the traditional unbiased minimum variance filter (UMVF), Kalman filter, and interactive multiple model (imm) algorithms, numerical results show that the newly proposed NFP method performs comparable or better in all scenarios with less computational requirements.

关键词： maneuvering target tracking least squares estimate imm algorithm and non-linear filtering

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Networked sensor management and data rate control for tracking maneuvering targets

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2005年第6期53卷 1979-1991页

作者： Evans, R Krishnamurthy, V Nair, G Sciacca, L Univ Melbourne Natl ICT Australia Victoria Res Lab Melbourne Vic 3010 Australia Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Def Sci & Technol Org Syst Sci Lab Elect Warfare & Radar Div Canberra ACT 5111 Australia

This paper presents sensor and data rate control algorithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple Model (imm), Particle filter tacker, and Probabilistic Data Association (PDA) algorithms to handle sensor and data rate control. Numerical studies illustrate the performance of these sensor and data rate control algorithms.

关键词： imm algorithm particle filters PDA algorithm sensor selection target tracking

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Model-set adaptation using a fuzzy Kalman filter

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MATHEMATICAL AND COMPUTER MODELLING 2001年第7-8期34卷 799-812页

作者： Ding, Z Leung, H Chan, K Zhu, ZW Raytheon Syst Canada Ltd Adv Syst Dev Waterloo ON N2J 4K6 Canada Univ Calgary Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Nortel Networks Toronto Multimedia Applicat Ctr Toronto ON M5G 1W7 Canada

In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-set adaptation problem of multiple model estimation. The fuzzy KF is found to be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covariance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting multiple model (imm) algorithm, hence, a fuzzy imm (Fimm) algorithm is obtained. The performance of the Fimm algorithm is compared with that of an adaptive imm (Aimm) algorithm using real radar data. Simulation result shows that the Fimm algorithm greatly outperforms the Aimm algorithm in terms of both the root mean square prediction error and the number of track loss. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： target tracking model-set adaptation imm algorithm adaptive imm algorithm fuzzy Kalman filter

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Multisensor, multisite integration for composite tracking of maneuvering targets

Multisensor, multisite integration for composite tracking of...

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Conference on Signal and Data Processing of Small Targets

作者： Watson, GA Anthony, KS Rice, TR USN Ctr Surface Warfare Dahlgren Div Syst Res & Technol DeptDigital Syst Branch Dahlgren VA 22448 USA

ISBN: (纸本)0819428221

The integration of multiple sensors for target tracking has been intensely investigated in recent years. The techniques for integrating multiple sensors are complex but have the potential to provide very accurate state estimates. For most systems, each sensor provides its information to a central location where the integration is performed. This approach is typically employed for a single platform and the resulting composite track can be very accurate when compared to the individual sensor tracks. Additional platforms can also contribute their information to improve this composite track. This composite track has the potential for enhanced system decisions and to provide targeting information not otherwise available. This paper presents algorithms for the composite tracking of maneuvering targets through the use of effective multisensor-multiplatform integration and simulation results illustrating the benefits of the proposed approach.

关键词： track continuity composite tracking sensor integration imm algorithm

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Comparison of various schema of filter adaptivity for the tracking of maneuvering targets

Comparison of various schema of filter adaptivity for the tr...

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Conference on Signal and Data Processing of Small Targets

作者： Jouan, A Bosse, E Simard, MA Shahbazian, E Lockheed Martin Canada R&D Dept Montreal PQ Canada

ISBN: (纸本)0819428221

Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (imm) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters (Adaptive Single Model, Input Estimation, Variable Dimension, etc.). However, the complexity of the imm algorithm can prohibit its use in those applications for which simpler algorithms can not provide the necessary accuracy and which can not afford the computational load of imm algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel (3CV-PAR) and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a Target Maneuver Threshold (TMTh). The tracking performance of the 3CV-PAR track filter is compared to : an adaptive single motion model Kalman filter (ASMMKF);an imm algorithm using the same three CV models as the 3CV-PAR filter;an imm filter using a CV model and a constant-acceleration (CA) model producing a CVCA filter;an imm filter using a CV and two CA models (CA1, CA2) differing only by the level of process noise producing a CV2CA filter. Calculations of the average RMS error on 100 Monte-Carlo runs permit to evaluate the tracking accuracy of the 3CV-PAR track filter compared to the above algorithms on a challenging multiple sensors scenario.

关键词： tracking imm algorithm

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