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Robust interacting multiple model algorithms based on multi-sensor fusion criteria

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第1期47卷 92-106页

作者： Zhou, Weidong Liu, Mengmeng Harbin Engn Univ HEU Coll Automat Harbin Peoples R China

This paper is concerned with the state estimation problem for a class of Markov jump linear discrete-time stochastic systems. Three novel interacting multiple model (imm) algorithms are proposed based on the H infinity technique, the correlation among estimation errors of mode-conditioned filters and the multi-sensor optimal information fusion criteria. Mode probabilities in the novel algorithms are derived based on the error cross-covariances instead of likelihood functions. The H infinity technique taking the place of Kalman filtering is applied to enhance the robustness of the new approaches. Theoretical analysis and Monte Carlo simulation results indicate that the proposed algorithms are effective and have an obvious advantage in velocity estimation when tracking a maneuvering target.

关键词： imm algorithm robust filtering multi-sensor fusion criteria estimation error correlation

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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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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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INTERACTIVE GAUSSIAN-SUM FILTERING FOR ESTIMATING SYSTEMATIC RISK IN FINANCIAL ECONOMETRICS 5

INTERACTIVE GAUSSIAN-SUM FILTERING FOR ESTIMATING SYSTEMATIC...

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5th IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Mohammadiy, Arash Zhang, Xiao-Ping Plataniotis, Konstantinos N. Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada Ryerson Univ Elect & Comp Engn Toronto ON Canada Univ Toronto Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781509059904

Motivated by application of signal processing techniques in financial econometrics, we propose a novel adaptive and robust non-linear filtering methodology to estimate the systematic risk of an investment arising from exposure to general market movements. There are extensive evidence that the asset returns exhibit non-normalities due to fat tails, excessive kurtosis, and asymmetry of the financial data. Gaussian sum filters (GSF) are attractive estimators for state-estimation in such non-Gaussian problems. However, due to the high computational cost of the GSF when applied to non-Gaussian risk estimation problems, no GSF has yet been investigated in the context of financial econometrics. The paper addresses this gap. To tackle this non-Gaussian problem, we introduce the interactive Gaussian sum filter (IGSF) by representing the non-systematic part of the risk in the Fama-French multi-factor model with the Gilbert-Elliott (GE) model. Due to incorporation of the GE-model, the proposed IGSF is a computationally attractive adaptive filter with an interactive multiple model (imm) collapsing style. In other words, the number of Gaussian components is controlled utilizing a modified Bayesian learning technique, which is used to collapse the Gaussian mixture representation of the non-systematic risk into an equivalent Gaussian term at each filtering cycle.

关键词： Fama-French Factor Model Gaussian-Sum Filter Gilbert-Elliott Model imm algorithm Non-Gaussian Noise

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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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Enhanced electronically scanned array resource management through multisensor integration

Enhanced electronically scanned array resource management th...

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Conference on Signal and Data Processing of Small Targets 1997

作者： Watson, GA Blair, WD Rice, TR Naval Surface Warfare Ctr. (United States)

ISBN: (纸本)0819425850

The integration of multiple sensors for target tracking and resource management has been intensely investigated and several effective techniques have been developed. These conventional techniques employ decision-directed logic and are very complex but have the potential to improve performance. For most systems, each sensor provides its information to a central location where the integration occurs. The central track is employed for system decisions and it is typically not used by the individual sensors. This low level of integration provides a manageable tracking environment but restricts the potential for system improvement. An Electronically Scanned Array (ESA) is highly controllable and has the ability to greatly enhance tracking performance. Resource allocation for an ESA is critical since it must support multiple functions, and several modern techniques have been developed to enhance its performance as a stand-alone sensor by effectively managing its time-energy budget. The integration of an ESA with other sensors can further enhance the tracking and reduce the resource allocation requirements of the ESA. This paper presents a technique for ESA resource management through the use of multisensor integration. The proposed technique avoids the decision-directed logic associated with conventional techniques by employing the Interacting Multiple Model (imm) algorithm. Simulation results are provided to demonstrate the effectiveness of this modern integration technique.

关键词： tracking multisensor integration resource management imm algorithm electronically scanned array

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Multiple target tracking with wireless sensor network for ground battlefield surveillance 17

Multiple target tracking with wireless sensor network for gr...

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17th International Conference on Information Fusion (FUSION)

作者： Pannetier, Benjamin Dezert, Jean Sella, Genevieve Off Natl Etud & Rech Aerosp French Aerosp Lab Informat Proc & Modelling Dept F-91761 Palaiseau France SAGEM Def & Opt Div CED CV F-91344 Massy France

ISBN: (纸本)9781479916344

For ground surveillance applications, the wireless sensor networks play a strategic role in military operations. In this paper, we explore the problem of tracking multiple targets observed in the sensors fields based on onboard algorithms under survivability system constraints. The surveillance system consists of a number of sensor nodes scattered in a region in order to detect and track targets in a cluttered environment. The targets can move on and off the road under several possible motion models. We study a Multiple Target Tracking (MTT) algorithm that fits with operational needs and offers good track continuity performance. The performances of this multiple sensors ground target tracking algorithm are evaluated on a complex and realistic scenario.

关键词： Wireless Ground Sensor Network Multiple Ground Target Tracking imm algorithm

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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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