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Performance and stability analysis of interacting multiple model estimator under unobservable packet loss

ISA TRANSACTIONS 2024年 155卷 193-204页

作者： Lin, Hong Lam, James Wang, Zidong Shu, Zhan Shenzhen Polytech Univ Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China Univ Hong Kong Dept Mech Engn Pokfulam Rd Hong Kong Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England Univ Alberta Fac Engn Dept Elect & Comp Engn Edmonton AB Canada

For a system with packet loss, if the estimator cannot observe the packet-loss status (PLS), the system is called an unobservable-packet-loss (UPL) system. Otherwise, it is called an observable-packet-loss (OPL) system. This paper studies the interacting multiple model (IMM) estimator for UPL systems, and the main contributions are twofold. (i) Estimation accuracy of the unobservable PLS. For an unstable UPL system, we prove that the UPL system will become an OPL one with time, since the PLS can be exactly estimated with time. For a stable UPL system, there exists an accuracy threshold such that the estimation accuracy of the PLS cannot be better than this threshold. (ii) Stability of the IMM estimator. For an unstable UPL system, we establish a necessary and sufficient condition: there exists a threshold such that the IMM estimator is stable almost everywhere if and only if the packet-arrival rate is greater than this threshold. For a stable UPL system, we show that the IMM estimator is stable, no matter what value the packet-arrival rate is.

关键词： interacting multiple model estimator Stability Unobservable packet loss

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Generation and estimation of randomly maneuvering target trajectories towards metacognitive tracking radar applications 26

Generation and estimation of randomly maneuvering target tra...

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Conference on Radar Sensor Technology XXVI Part of SPIE Defense and Commercial Sensing Conference

作者： Sreekantamurthy, Veena DeVault, Jordan A. Martone, Anthony F. Kirk, Benjamin H. Narayanan, Ram M. Penn State Univ Dept Elect Engn University Pk PA 16802 USA US Army Res Lab Sensor & Elect Devices Directorate Adelphi MD 20783 USA

ISBN: (纸本)9781510650930;9781510650923

There is growing research interest to merge the idea of a metacognitive radar with that of a tracking radar. The concept of metacognition can be broadly summarized as the process of learning about learning. In a metacognitive tracking radar, the system uses cognitive processes to detect and track a target in a dynamic environment. The radar then applies metacognitive techniques to select the cognitive process that yields the most accurate target track. In the context of target tracking, cognitive processes are various tracking algorithms. Currently, metacognitive tracking radar systems have only been demonstrated on targets of known trajectories. Their performance in the case of a randomly maneuvering target has not been explored. This paper presents an initial approach to this problem. First, an algorithm to generate random target trajectories is presented. Then, these trajectories are estimated using two estimation algorithms: the Extended Kalman Filter (EKF) and the interacting multiple model (IMM) estimator. Finally, the performances of these two algorithms are compared.

关键词： Maneuvering target target tracking Kalman filter interacting multiple model estimator metacognitive radar

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Robust state estimation with trajectory shape constraints

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IET RADAR SONAR AND NAVIGATION 2020年第7期14卷 1046-1054页

作者： Li, Keyi Zhou, Gongjian Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Marine Environm Monitoring & Informat Pro Harbin Peoples R China

In some tracking applications, target states are subjected to a trajectory shape constraint, where only the trajectory shape is known a priori. By incorporating the trajectory shape constraint, the tracking performance can be improved. However, the existing state estimation methods with trajectory shape constraints are either of high computational load or of poor robustness due to the nature of the existing constraint models. In this study, in order to improve the robustness of the state estimation method with trajectory shape constraints, a generalised model for the trajectory shape constraint imposed by a straight trajectory is proposed. Three unknown general trajectory parameters of the trajectory are treated as states to be estimated along with the target state. Then, pseudo-measurements are constructed to incorporate the constraint information into estimators. For non-manoeuvring targets, a robust trajectory shape constraint filter is developed. The sequential measurement processing scheme is employed to reduce the computational load. A corresponding filter initialisation method is derived. While for manoeuvring targets, the proposed constraint filters are embedded into a conventional interacting multiple model estimator as sub-filters to handle the manoeuvring target tracking problem with a trajectory shape constraint. Monte-Carlo simulation results illustrate the effectiveness and robustness of the proposed algorithms.

关键词： state estimation Monte Carlo methods radar tracking target tracking filtering theory robust trajectory shape constraint filter robust state estimation tracking applications tracking performance improvement trajectory parameter estimation nonmanoeuvring targets sequential measurement processing interacting multiple model estimator manoeuvring target tracking problem Monte-Carlo simulation

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Adaptive state estimation for tracking of civilian aircraft

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第6期12卷 777-784页

作者： Patra, Nilanjan Sadhu, Smita Ghoshal, Tapan Kumar Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Tracking of manoeuvring subsonic aerospace vehicles has traditionally been handled by state estimators. Ordinary state estimators perform poorly as the concerned process model can only be defined imprecisely. This contribution evaluates and compares the performance of adaptive single mode non-linear estimators against several versions of other estimators. The primary comparison is with the recently introduced smooth variable structure filter (SVSF) which is claimed to inherit the robustness of variable structure approach. Both the above types of estimators are then benchmarked with a well-known version of interacting multiple model (IMM) estimator which treats the manoeuvring aircraft as a hybrid system consisting of multiple modes. Monte Carlo simulation has been used and several descriptors have been used for comparison. The comparison demonstrates that a version of non-linear adaptive estimators incorporating sigma points and a single constant turn model performs substantially better than the SVSF and its tracking performance approaches that obtainable by the IMM estimator. Novelty of this contribution lies in providing a detailed comparison of the above three families of estimators, which provides adequate insight for selecting tracking estimators by trading off estimation accuracy, algorithm complexity, tuning requirement, and computational load.

关键词： Monte Carlo methods nonlinear estimation aircraft adaptive estimation state estimation nonlinear adaptive state estimation manoeuvring subsonic aerospace vehicle tracking civilian aircraft tracking adaptive single mode nonlinear estimator smooth variable structure filter SVSF interacting multiple model estimator IMM estimator Monte Carlo simulation

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A model-based, multichannel, real-time capable sawtooth crash detector

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PLASMA PHYSICS AND CONTROLLED FUSION 2016年第7期58卷 075002-075002页

作者： van den Brand, H. de Baar, M. R. van Berkel, M. Blanken, T. C. Felici, F. Westerhof, E. Willensdorfer, M. DIFFER Dutch Inst Fundamental Energy Res FOM Inst POB 6336 NL-5600 HH Eindhoven Netherlands Eindhoven Univ Technol Control Syst Technol Grp Dept Mech Engn POB 513 NL-5600 MB Eindhoven Netherlands Vrije Univ Brussel Dept Fundamental Elect & Instrumentat Pl Laan 2 B-1050 Brussels Belgium Max Planck Inst Plasma Phys Boltzmannstr 2 D-85748 Garching Germany

Control of the time between sawtooth crashes, necessary for ITER and DEMO, requires real-time detection of the moment of the sawtooth crash. In this paper, estimation of sawtooth crash times is demonstrated using the model-based interacting multiple model (IMM) estimator, based on simplified models for the sawtooth crash. In contrast to previous detectors, this detector uses the spatial extent of the sawtooth crash as detection characteristic. The IMM estimator is tuned and applied to multiple ECE channels at once. A model for the sawtooth crash is introduced, which is used in the IMM algorithm. The IMM algorithm is applied to seven datasets from the ASDEX Upgrade tokamak. Five crash models with different mixing radii are used. All sawtooth crashes that have been identified beforehand by visual inspection of the data, are detected by the algorithm. A few additional detections are made, which upon closer inspection are seen to be sawtooth crashes, which show a partial reconnection. A closer inspection of the detected normal crashes shows that about 42% are not well fitted by any of the full reconnection models and show some characteristics of a partial reconnection. In some case, the measurement time is during the sawtooth crashes, which also results in an incorrect estimate of the mixing radius. For data provided at a sampling rate of 1 kHz, the run time of the IMM estimator is below 1 ms, thereby fulfilling real-time requirements.

关键词： tokamak sawtooth detector interacting multiple model estimator

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Distributed target tracking by time of arrival and received signal strength with unknown path loss exponent

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IET SIGNAL PROCESSING 2015年第9期9卷 681-686页

作者： Li, Wenling Jia, Yingmin Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China

The problem of distributed target tracking using time of arrival and received signal strength with unknown path loss exponent (PLE) is studied. The PLE is modelled as a Markov chain with three states and an adaptive gridding strategy is adopted to adjust the PLE recursively in a bounded interval. Therefore, the target tracking model is formulated as a jump Markov non-linear system. The interacting multiple model () estimator is applied to derive the target state estimates for each sensor and the covariance intersection approach is used to fuse sensor-based estimates in a distributed fashion. Simulation results show a promising performance for the proposed filter.

关键词： time-of-arrival estimation target tracking RSSI tracking filters Markov processes state estimation sensor fusion covariance matrices distributed target tracking time-of-arrival estimation received signal strength path loss exponent PLE Markov chain adaptive griding strategy jump Markov nonlinear system interacting multiple model estimator target state estimation covariance intersection approach sensor fusion based estimation distributed fashion

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Laser-Based Tracking of Groups of People with Sudden Changes in Motion

Laser-Based Tracking of Groups of People with Sudden Changes...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Hashimoto, Masafumi Nishio, Azusa Tsuji, Atsushi Takahashi, Kazuhiko Doshisha Unvers Fac Sci & Engn Kyoto 6100394 Japan Doshisha Unvers Grad Sch Sci & Engn Kyoto 6100394 Japan

ISBN: (纸本)9781479978007

This paper presents a method for laser-based tracking of people in a group. A two-layer laser scanner set in an environment provides laser-scanned images. A background subtraction method extracts people's position data from the images. Data association based on heuristic rules and the global nearest neighbor (GNN) identifies multiple people in crowded environments. Their people are then tracked via a model-based tracker;the interacting multiple model estimator is applied in order to track people with sudden changes in motion (walking, running, starting, stopping, or turning suddenly). People in a group meet a considerable number of occlusions, which often deteriorates tracking performance. To cope with this problem, the proposed method groups people with similar motions. Thus, when people are occluded by other people in the same group, the occluded people are tracked using laser position data related to their nearest neighbor in the group, which improves the tracking performance. The experimental results of 11 people tracked in a small area validate the effectiveness of this method.

关键词： People Tracking Group People Laser Scanner interacting multiple model estimator

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Identification and Tracking Using Laser and Vision of People Maneuvering in Crowded Environments

Identification and Tracking Using Laser and Vision of People...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hashimoto, Masafumi Bai, Zhitao Konda, Tomoki Takahashi, Kazuhiko Doshisha Univ Fac Sci & Engn Kyoto 6100394 Japan Doshisha Univ Grad Sch Engn Kyoto 6100394 Japan

ISBN: (纸本)9781424465880

This paper presents a people tracker that uses a two-layered laser range sensor (LRS) and a fisheye camera. The LRS detects waists and knees of people, and the camera captures color images of torsos of people detected by the LRS. From the data of people's position obtained by the LRS and the color histogram obtained by the camera, heuristic-rule-based and global-nearest-neighbor-based data association can identify multiple people in crowded environments. The identified people are tracked via a model-based tracker;an interacting multiple model estimator is applied to track people maneuvers, such as walking, running, sudden starting/stopping, and sudden turning. Simulation and experimental results show that our people identification and tracking method provides better performance than conventional methods.

关键词： Laser range sensor Vision People tracking interacting multiple model estimator Color histogram Data association

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multiple-model probability hypothesis density filter for tracking maneuvering targets

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2008年第1期44卷 87-98页

作者： Punithakumar, K. Kirubarajan, T. Sinha, A. McMaster Univ Tracking & Fus Lab Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. With multiple targets, representing the full posterior distribution over target states is not practical. The problem becomes even more complicated when the number of targets varies, in which case the dimensionality of the state space itself becomes a discrete random variable. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment (the PHD) of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems with a varying number of targets. The integral of PRD in any region of the state space gives the expected number of targets in that region. With maneuvering targets, detecting and tracking the changes in the target motion model also become important. The target dynamic model uncertainty can be resolved by assuming multiple models for possible motion modes and then combining the mode-dependent estimates in a manner similar to the one used in the interacting multiple model (IMM) estimator. This paper propose a multiple-model implementation of the PHD filter, which approximates the PHD by a set of weighted random samples propagated over time using sequential Monte Carlo (SMQ methods. The resulting filter can handle nonlinear, non-Gaussian dynamics with uncertain model parameters in multisensor-multitarget tracking scenarios. Simulation results are presented to show the effectiveness of the proposed filter over single-model PHD filters.

关键词： Monte Carlo methods filtering theory probability sensor fusion target tracking first-order statistical moment interacting multiple model estimator maneuvering multisensor target tracking multiple-model PHD filter non-Gaussian dynamics

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Radar Measurement Noise Variance Estimation with Several Targets of Opportunity

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2008年第3期44卷 985-995页

作者： Osborne, R. W., III Bar-Shalom, Y. Kirubarajan, T. Univ Connecticut Dept Elect & Comp Engn Storrs CT 06264 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Univ Connecticut Estimat & Signal Proc Res Lab Storrs CT 06264 USA

A number of methods exist to track a target's uncertain motion through space using inherently inaccurate sensor measurements. A powerful method of adaptive estimation is the interacting multiple model (IMM) estimator. In order to carry out state estimation from the noisy measurements of a sensor, however, the filter should have knowledge of the statistical characteristics of the noise associated with that sensor. The statistical characteristics (accuracies) of real sensors, however, are not always available, in particular for legacy sensors. A method is presented of determining the measurement noise variances of a sensor, assumed to be constant, by using multiple IMM estimators while tracking targets whose motion is not known-targets of opportunity. Combining techniques outlined in [2] and [6], the likelihood functions are obtained for a number of IMM estimators, each with different assumptions on the measurement noise variances. Then a search is carried out over a varying grid of IMMs to bracket the variances of the sensor measurement noises. The end result consists of estimates of the measurement noise variances of the sensor in question.

关键词： adaptive estimation radar tracking statistical analysis target tracking interacting multiple model estimator likelihood functions radar measurement noise variance estimation sensor measurements statistical characteristics Likelihood Functions VARIANCES Noise measurement Target tracking Sensors Radar measurement radar target tracking Statistical analysis adaptive estimation IMM Statistical methods

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