Improved nonlinear filtering for target tracking.

The objective of this research is to develop robust and accurate tracking algorithms for various tracking applications. These tracking problems can be formulated as nonlinear filtering problems. The tracking algorithms will be developed based on an emerging promising nonlinear filter technique, known as sequential importance sampling (nick-name: particle filtering). This technique was introduced to the engineering community in the early years of 2000, and it has recently drawn significant attention from engineers and researchers in a wide range of areas. Despite the encouraging results reported in the current literature, there are still many open questions to be answered. For the first time, the major research effort will be focusing on making improvement to the particle filter based tracking algorithm in the following three aspects: (I) refining the particle filtering process by designing better proposal distributions (II) refining the dynamic model by using multiple-model method, (i.e. using switching dynamics and jump Markov process) and (III) refining system measurements by incorporating a data fusion stage for multiple measurement cues

Robust Multi-Object Tracking: A Labeled Random Finite Set Approach

The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable

Modeling and Estimation for Maneuvering Target Tracking with Inertial Systems using Interacting Multiple Models

Projecte final de carrea realitzat en col.laboracio amb Centre Tecnològic de Telecomunicacions de CatalunyaThe aim of this Thesis is to study and develop Estimation Technique that enhances the Dynamic Tracking capability of Maneuvering Targets based using Inertial Systems. Inertial Measurement Systems have measurement biases and drifts and properly estimating their errors is a real time problem. Moreover, different targets perform different types of maneuvers during different stages of their trajectory and as such it is not possible to obtain accurate tracking of target maneuvers using a filters based on conventional single model approach. As such, a technique is required which is dynamic in both estimating and filtering the errors in inertial measurements and in switching to appropriate motion models according to the current maneuver of the vehicle. This thesis suggests and evaluates ‘Interacting Multiple Models (IMM)’ scheme for the solution to the above problem. Performance of the IMM scheme is proven over conventional single model based filters like Kalman Filter through both simulations and real target tracking

Trajectory generation for lane-change maneuver of autonomous vehicles

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during lane-change maneuvers. ^ We first propose a two-stage, interactive-multiple-model-based estimator to perform multi-target tracking of neighboring vehicles in a lane-changing scenario. The first stage deals with an adaptive window based turn-rate estimation for tracking maneuvering target vehicles using Kalman filter. In the second stage, variable-structure models with updated estimated turn-rate are utilized to perform data association followed by velocity estimation. Based on the estimated velocities of neighboring vehicles, piecewise Bezier-curve-based methods that minimize the safety/collision risk involved and maximize the comfort ride have been developed for the generation of desired trajectory for lane-change maneuvers. The proposed velocity-estimation and trajectory-generation algorithms have been validated experimentally using Pioneer3- DX mobile robots in a simulated lane-change environment as well as validated by computer simulations

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.United States. Office of Naval Research (Grant N000140711102)United States. Office of Naval Research. Multidisciplinary University Research InitiativeSingapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin