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31st IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Gunes, Abmet Gebze Tekn Univ Savunma Teknol Enstitusu Kocaeli Turkiye

ISBN: (纸本)9798350343557

sensor control provides a framework for the autonomous functioning of mobile sensors. The parameters of a target is estimated using a cost function. By optimizing the cost function, the actions to be taken by the vehicle is determined, In the literature, the vehicles are assumed as point robots so far and vehicle dynamics are ignored. In this work, the vehicle dynamics are involved in the equations for the first time. Performance of the systems with and without vehicle dynamics are analyzed in V-REP simulation environment and the results are presented.

关键词： sensor control vehicle dynamics V-REP

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sensor control for Selective Object Tracking Using Labeled Multi-Bernoulli Filter 21

Sensor Control for Selective Object Tracking Using Labeled M...

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21st International Conference on Information Fusion (FUSION)

作者： Panicker, Sabita Gostar, Amirali Khodadadian Bab-Haidashar, Alireza Hoseinnezhad, Reza RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9780996452762

With the recent advent of labeled random finite set filters, it is now possible to not only estimate the number of objects and their states, but also track their trajectories all within the stochastic filtering scheme. This paper investigates how the objects label information returned by a labeled multi-Bernoulli filter can be effectively used for sensor control purposes. The main focus is on selective multi-object tracking applications where objects with particular labels are of high priority, and the sensor needs to be controlled to achieve maximum confidence in tracking performance of the filter for those objects. We formulated and examined two novel solutions. The first is based on maximization of the confidence in what the filter returns in relation to the existence of the objects of interest. The second solution is based on maximization of the confidence in what the filter returns in relation to the both existence and the states of the objects of interest. We also present an intuitive solution for tracking scenarios when some of the targets of interest temporarily disappear then reappear. Simulation results indicate how the proposed methods can lead to significant improvements in terms of tracking accuracy of objects of interest, compared to using the generic (non-selective) sensor control methods.

关键词： random finite sets labeled multi-Bernoulli filter sensor control sensor management

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sensor control for Multi-Target Tracking in the Presence of Doppler Blind Zone 8

Sensor Control for Multi-Target Tracking in the Presence of ...

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8th International Conference on control, Automation and Information Sciences (ICCAIS)

作者： Xie, Xin Sun, Hemin Wu, Weihua Cai, Yichao Jiang, Surong Wu, Changfei Air Force Early Warning Acad Wuhan Peoples R China

ISBN: (纸本)9781728123110

For multi-target tracking of unmanned airborne radar, tracking performance is seriously deteriorated by the presence of the Doppler blind zone (DBZ) Aiming at this problem, a sensor control method based on Cauchy-Schwarz divergence (CSD) to suppress the influence of DBZ is proposed in this paper. Firstly, the jump Markov model is introduced into the novel Gaussian mixture probability hypothesis density (PHD) filter. Secondly, the Cauchy Schwarz divergence expression between the predicted intensity and the posterior intensity of the filter is derived. Finally, based on Cauchy-Schwarz divergence, by controlling the trajectory of the unmanned aerial vehicle (UAV), the DBZ is avoided as much as possible, thereby improving the quality of measurements and the multi-maneuvering-target tracking performance of the unmanned airborne radar. Simulation results show that the proposed method can effectively suppress the influence of DBZ.

关键词： multi-target tracking sensor control doppler blind zone randomfinite set Cauchy-Schwartz divergence

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sensor control for Multi-object Bayesian Filtering Based on Minimum Predicted Miss-Distance

Sensor Control for Multi-object Bayesian Filtering Based on ...

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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Jian, Chunxiao Yang, Wei Liu, Peiguo Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783037857519

The sensor control is concerned in this paper to exploit the multi-object filtering capability of the sensor system. The proposed control algorithm is formulated in the framework of partially observed Markov decision processes as previous work, while it adopts a new reward function (RF). Multi-object miss-distance can jointly capture detection and estimation error in a mathematically consistent manner and is generally employed as the final performance measure for the multi-object filtering;therefore, the predicted multi-object miss-distance can be naturally selected as a RF. However, there is no analytical expression of the predicted multi-object miss-distance generally. The computation of this predicted miss-distance is discussed in detail. Future work will concentrate on providing a complete comparison of different sensor control schemes.

关键词： sensor control multi-object filtering reward function multi-object miss-distance

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MM-GLMB Filter-Based sensor control for Tracking Multiple Maneuvering Targets Hidden in the Doppler Blind Zone

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 4555-4567页

作者： Wu, Weihua Sun, Hemin Cai, Yichao Xiong, Jiajun Air Force Early Warning Acad Wuhan 430019 Peoples R China

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines the sensor control technique to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.

关键词： Target tracking Radar tracking Information filters Doppler effect Radio frequency Filtering algorithms sensor control multi-maneuvering-target tracking (MMTT) Doppler blind zone (DBZ) multiple model generalized labeled multi-Bernoulli (MM-GLMB) partially observable Markov decision process (POMDP) Cauchy-Schwarz divergence (CSD)

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Resolvable Group Target Tracking via Multi-Bernoulli Filter and Its Application to sensor control Scenario

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 6286-6299页

作者： Li, Guchong Li, Gang He, You Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Naval Aviat Univ Inst Informat Fus Yantai 264001 Peoples R China

In this paper, we address the problem of resolvable group target tracking (RGTT) and the application of RGTT in the sensor control scenarios. The aim of our approach is to further improve the tracking performance of multi-Bernoulli (MB) filter by exploiting the group structure information. Firstly, a single-target state transition function (SSTF) is derived from the direct integral solution of the single-target state stochastic differential equation (SDE), which is used to revise the prediction process of the standard MB filter. Then, combing the Gaussian mixture (GM) implementation, the prediction process of the proposed SSTF-based MB (SSTF-MB) filter is detailed. Next, considering the benefits and importance of group structure in the sensor control scenarios, the proposed SSTF-MB filter is applied into a mobile sensor platform whose control actions (e.g., motion direction) are uncertain. Specifically, the proposed SSTF-MB filter is merged into a sensor control strategy where the Cauchy-Schwarz (CS) divergence is selected as the objective function to compute the discrepancy between two first-order moment approximations of MBs. Finally, the efficiency and performance of the proposed SSTF-MB filter and its application to sensor control scenario is well demonstrated in the simulation experiments.

关键词： Target tracking Time measurement Standards Density measurement Radio frequency Predictive models Monte Carlo methods Cauchy-Schwarz divergence multi-Bernoulli resolvable group target tracking sensor control single-target state transition function

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Cell Multi-Bernoulli (Cell-MB) sensor control for Multi-Object Search-While-Tracking (SWT)

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第6期45卷 7195-7207页

作者： LeGrand, Keith A. A. Zhu, Pingping Ferrari, Silvia Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Marshall Univ Dept Elect Engn Huntington WV 25755 USA Cornell Univ Sibley Sch Mech & Aerosp Engn Lab Intelligent Syst & Controls LISC Ithaca NY 14850 USA

Information-driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in uncertainty also known as information gain. Random finite set (RFS) theory provides a formalism for quantifying and estimating information gain in multi-object tracking problems. However, estimating information gain in these applications remains computationally challenging. This paper presents a new tractable approximation of the RFS expected information gain applicable to sensor control for multi-object search and tracking. Unlike existing RFS approaches, the information gain approximation presented in this paper considers the contributions of non-ideal noisy measurements, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information-driven sensor control is demonstrated through two multi-vehicle search-while-tracking experiments using real video data from remote terrestrial and satellite sensors.

关键词： Radio frequency Search problems Gain measurement Noise measurement Surveillance Extraterrestrial measurements Uncertainty sensor control information gain multi-object tracking random finite set cell multi-Bernoulli bounded field-of-view Kullback-Leibler divergence

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OSPA-Based sensor control 4

OSPA-Based Sensor Control

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The Fourth International Conference On control, Automation and Information Sciences

作者： Gostar, Amirali K. Hoseinnezhad, Reza Bab-Hadiashar, Alireza Papi, Francesco RMIT Univ Sch Aerosp Mech & Mfg Engn Melbourne Vic Australia Curtin Univ Technol ECE Dept Perth WA 6845 Australia

ISBN: (纸本)9781479998920

This paper presents a new sensor control method for multi-object filtering, that is designed based on maximizing a measure of confidence in state estimation accuracy. Confidence of estimation is quantified by measuring the dispersion of the multi-object posterior about its statistical mean using Optimal Sub-Pattern Assignment (OSPA). The proposed method is generic and the presented algorithm can be used with common statistical filters. Implementation of the algorithm in conjunction with a labeled multi-Bernoulli filter is presented. Simulation studies demonstrate that the proposed method works in a challenging sensor control for multi-target tracking scenario.

关键词： Labeled Multi-Bernoulli Filter Random Finite Sets PHD Filter sensor control OSPA metric PEECS

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Threat Assessment and sensor control for Multi-target Tracking via PHD Filter 14

Threat Assessment and Sensor Control for Multi-target Tracki...

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IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Chen, Hui He, Zhongliang Liu, Bei Lanzhou Univ Technol Sch Elect & Informat Engn Lanzhou 730050 Gansu Peoples R China

ISBN: (纸本)9781509067596

In consideration of the sensor control under the real tracking environment, this paper presents a threat assessment based sensor control. First, the factors that affect the threat level of target are analyzed. Moreover, this paper uses the approximate multi-target filter to estimate multi-target states, and determine the target with the maximum threat degree at the different times based on the Tactical Significance Map (TSM) function. Finally, the Renyi divergence is used as objective function for sensor control, and the final control policy is solved through the maximum information gain criterion. Simulation results show the effectiveness of the presented method.

关键词： multi-target tracking threat degree tactical significance map sensor control

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INFORMATION THEORETIC APPROACH TO ROBUST MULTI-BERNOULLI sensor control

INFORMATION THEORETIC APPROACH TO ROBUST MULTI-BERNOULLI SEN...

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IEEE Workshop on Statistical Signal Processing (SSP)

作者： Gostar, Amirali K. Hoseinnezhad, Reza Bab-Hadiashar, Alireza RMIT Univ Sch Aerosp Mech & Mfg Engn Bundoora Vic 3083 Australia

ISBN: (纸本)9781479949755

A novel sensor control solution is presented, formulated within a Multi-Bernoulli-based multi-target tracking framework. The proposed method is especially designed for the general multi-target tracking case, where no prior knowledge of the clutter distribution or the probability of detection profile are available. In an information theoretic approach, our method makes use of R` enyi divergence as the reward function to be maximized for finding the optimal sensor control command at each step. We devise a Monte Carlo sampling method for computation of the reward. Simulation results demonstrate successful performance of the proposed method in a challenging scenario involving five targets maneuvering in a relatively uncertain space with unknown distance-dependent clutter rate and probability of detection.

关键词： Random finite sets multi-target filtering sequential Monte Carlo sensor control Renyi divergence

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