Estimation of a Human-Maneuvered Target Incorporating Human Intention

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

[1]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[2]  Y. Bar-Shalom,et al.  Multiple-model estimation with variable structure , 1996, IEEE Trans. Autom. Control..

[3]  Song Li,et al.  Unscented extended Kalman filter for target tracking , 2011 .

[4]  Yanjun Ma,et al.  Multiple-Model State Estimation Based on Variational Bayesian Inference , 2019, IEEE Transactions on Automatic Control.

[5]  Alper Akca,et al.  Multiple Model Kalman and Particle Filters and Applications: A Survey , 2019, IFAC-PapersOnLine.

[6]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[7]  Aaron D. Lanterman,et al.  Unscented Kalman Filters for Multiple Target Tracking With Symmetric Measurement Equations , 2009, IEEE Transactions on Automatic Control.

[8]  Tomonari Furukawa,et al.  Autonomous Robotic Escort Incorporating Motion Prediction and Human Intention , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Yu Zhang,et al.  Memory Pattern Identification for Feedback Tracking Control in Human-Machine Systems. , 2019, Human factors.

[10]  Aurélien Géron,et al.  Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems , 2017 .

[11]  Rüdiger Dillmann,et al.  A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[12]  Jinling Wang,et al.  Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration , 2010 .

[13]  P. Bogler.  Tracking a Maneuvering Target Using Input Estimation , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Gregery T. Buzzard,et al.  State and Unknown Input Observers for Nonlinear Systems With Bounded Exogenous Inputs , 2017, IEEE Transactions on Automatic Control.

[15]  J. Josiah Steckenrider,et al.  A Probabilistic Model-adaptive Approach for Tracking of Motion with Heightened Uncertainty , 2020 .

[16]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[17]  Iwao Sasase,et al.  Two-stage Kalman estimator using advanced circular prediction for maneuvering target tracking , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[18]  P. J. Costa.  Adaptive model architecture and extended Kalman-Bucy filters , 1994 .

[19]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[20]  X. Rong Li,et al.  Hybrid grid multiple-model estimation with application to maneuvering target tracking , 2010, 2010 13th International Conference on Information Fusion.

[21]  Alexandre Bernardino,et al.  Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization , 2020, J. Field Robotics.

[22]  Yuhua Qi,et al.  Drone Detection and Pose Estimation Using Relational Graph Networks , 2019, Sensors.

[23]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[24]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[25]  Qinggang Meng,et al.  Communication and Interaction With Semiautonomous Ground Vehicles by Force Control Steering , 2020, IEEE Transactions on Cybernetics.

[26]  R. Mehra.  On the identification of variances and adaptive Kalman filtering , 1970 .

[27]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[28]  Martin Saska,et al.  Onboard Marker-Less Detection and Localization of Non-Cooperating Drones for Their Safe Interception by an Autonomous Aerial System , 2019, IEEE Robotics and Automation Letters.

[29]  X. R. Li,et al.  Chapter 10 Engineer ’ s Guide to Variable-Structure Multiple-Model Estimation for Tracking , 2022 .

[30]  Hyondong Oh,et al.  An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information , 2016 .

[31]  Huibin Wang,et al.  Combination of Interacting Multiple Models with the Particle Filter for Three-Dimensional Target Tracking in Underwater Wireless Sensor Networks , 2012 .