Fuzzy interacting multiple model H∞ particle filter algorithm based on current statistical model

In this paper, fuzzy theory and interacting multiple model are introduced into H∞ filter-based particle filter to propose a new fuzzy interacting multiple model H∞ particle filter based on current statistical model. Each model uses H∞ particle filter algorithm for filtering, in which the current statistical model can describe the maneuver of target accurately and H∞ filter can deal with the nonlinear system effectively. Aiming at the problem of large amount of probability calculation in interacting multiple model by using combination calculation method, our approach calculates each model matching probability through the fuzzy theory, which can not only reduce the calculation amount, but also improve the state estimation accuracy to some extent. The simulation results show that the proposed algorithm can be more accurate and robust to track maneuvering target

Multistatic Tracking with the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker

Multistatic sonar tracking is a difficult proposition. The ocean environment typically features very complex propagation conditions, causing low target probabilities of detection and high clutter levels. Additionally, most sonar targets are relatively low speed, which makes it difficult to use Doppler (if available) to separate target returns from clutter returns. The Maximum Likelihood Probabilistic Data Association Tracker (ML-PDA) and the Maximum Likelihood Probabilistic Multi-Hypothesis Tracker (ML-PMHT) --- a similar algorithm to ML-PDA --- can be implemented as effective multistatic trackers. This dissertation will develop a tracking framework for these algorithms. This framework will focus mainly on ML-PMHT, which has an inherent advantage in that its log-likelihood ratio (LLR) has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. First, this multitarget LLR will be implemented for ML-PMHT, which will give it superior performance over ML-PDA for instances where multiple targets are closely spaced with similar motion dynamics. Next, the performance of ML-PMHT will be compared when it is applied in Cartesian measurement space and in delay-bearing measurement space, where the measurement covariance is more accurately represented. Following this, a maneuver-model parameterization will be introduced that will allow ML-PDA and ML-PMHT to follow sharply maneuvering targets; their previous straight-line parameterization only allowed them to follow moderately maneuvering targets. Finally, a novel method of determining a tracking threshold for ML-PMHT will be developed by applying extreme value theory to the probabilistic properties of the clutter. This will also be done with target measurements, which will allow the issue of trackability for ML-PMHT to be explored. Probabilistic expressions for the maximum values of the LLR surface caused by both clutter and the target will be developed, which will allow for the determination of target trackability in any given scenario

Persistent vision-based search and track using multiple UAVs

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 93-98).Unmanned aerial vehicles (UAVs) have attracted interest for their ability to carry out missions such as border patrol, urban traffic monitoring, persistent surveillance, and search and rescue operations. Most of these missions require the ability to detect and track objects of interest on or near the ground. In addition, most of the missions are inherently long-duration, requiring multiple UAVs to cooperate over time periods longer than the endurance of a single vehicle. This thesis presents a framework to enable such missions to be carried out autonomously and robustly. First, a technique for vision-based target detection and bearing determination that utilizes a video camera onboard each UAV is presented. The technique is designed to detect the presence of targets of interest in the camera video stream and determine the bearing from the UAV to the target even when the video data is noisy. Next, a cooperative, bearings-only target estimation algorithm is presented. The algorithm is shown to provide better estimates of a target's position and velocity in three dimensions than could be achieved by a single vehicle, while being computationally efficient and naturally distributable among multiple UAVs.(cont. )Next, a task assignment algorithm that incorporates closed-loop feedback on the performance of individual UAVs and sensor suites is developed, enabling underperforming UAVs to be dynamically swapped out by the tasking system. Finally, flight results from several persistent, multiple-target search and track experiments conducted on MIT's Real-time indoor Autonomous Vehicle test ENvironment (RAVEN) are presented.by Brett Bethke.S.M

Orbit determination for impulsively maneuvering spacecraft using a modified state transition tensor

This paper proposes a method to accurately resolve orbit determination (OD) for a spacecraft with unknown impulsive maneuvers. The proposed method handles the unknown impulsive maneuver by incorporating the magnitude, direction, and time of the impulsive maneuver into the estimation parameter vector. First, a modified state transition tensor (STT) is proposed via orbit division and segment connection, allowing the orbit to be directly propagated under the effects of impulsive maneuver uncertainties. Then, based on the modified STT, a second-order measurement model is established with the estimation parameter vector as the input. Combining the second-order measurement model with observations, a second-order optimal solution is derived to correct the estimation parameters. The spacecraft orbit, together with the magnitude, direction, and time of the impulsive maneuver, are simultaneously estimated in an iterative framework. The performance of the proposed method is validated in a low-Earth-orbit case and a high-Earth-orbit case. Simulations show that the proposed method outperforms its linear version in terms of convergence, accuracy, and uncertainty quantification capacity. Its maneuver reconstruction and orbit estimation errors are one order of magnitude less than those of competitive methods. Moreover, the proposed method can handle severe conditions and is robust to initial guesses

Motion Coordination of Multiple Autonomous Vehicles in a Spatiotemporal Flowfield

The long-term goal of this research is to provide theoretically justified control strategies to operate autonomous vehicles in spatiotemporal flowfields. The specific objective of this dissertation is to use estimation and nonlinear control techniques to generate decentralized control algorithms that enable motion coordination for multiple autonomous vehicles while operating in a time-varying flowfield. A cooperating team of vehicles can benefit from sharing data and tasking responsibilities. Many existing control algorithms promote collaboration of autonomous vehicles. However, these algorithms often fail to account for the degradation of control performance caused by flowfields. This dissertation presents decentralized multivehicle coordination algorithms designed for operation in a spatially or temporally varying flowfield. Each vehicle is represented using a Newtonian particle traveling in a plane at constant speed relative to the flow and subject to a steering control. Initially, we assume the flowfield is known and describe algorithms that stabilize a circular formation in a time-varying spatially nonuniform flow of moderate intensity. These algorithms are extended by relaxing the assumption that the flow is known: the vehicles dynamically estimate the flow and use that estimate in the control. We propose a distributed estimation and control algorithm comprising a consensus filter to share information gleaned from noisy position measurements, and an information filter to reconstruct a spatially varying flowfield. The theoretical results are illustrated with numerical simulations of circular formation control and validated in outdoor unmanned aerial vehicle (UAV) flight tests

Automated tracking of the Florida manatee (Trichechus manatus)

The electronic, physical, biological and environmental factors involved in the automated remote tracking of the Florida manatee (Trichechus manatus) are identified. The current status of the manatee as an endangered species is provided. Brief descriptions of existing tracking and position locating systems are presented to identify the state of the art in these fields. An analysis of energy media is conducted to identify those with the highest probability of success for this application. Logistic questions such as the means of attachment and position of any equipment to be placed on the manatee are also investigated. Power sources and manateeborne electronics encapsulation techniques are studied and the results of a compter generated DF network analysis are summarized

Trajectory optimization for target localization using small unmanned aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 189-197).Small unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance missions. One particular mission of interest involves computing location estimates for targets detected by onboard sensors. Combining UAV state estimates with information gathered by the imaging sensors leads to bearing measurements of the target that can be used to determine the target's location. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods produce biased and uncertain estimates, occasionally leading to filter instabilities. Careful selection of the measurement locations greatly enhances filter performance, motivating the development of UAV trajectories that minimize target location estimation error and improve filter convergence. The objective of this work is to develop guidance algorithms that enable the UAV to fly trajectories that increase the amount of information provided by the measurements and improve overall estimation observability, resulting in proper target tracking and an accurate target location estimate. The performance of the target estimation is dependent upon the positions from which measurements are taken relative to the target and to previous measurements. Past research has provided methods to quantify the information content of a set of measurements using the Fisher Information Matrix (FIM). Forming objective functions based on the FIM and using numerical optimization methods produce UAV trajectories that locally maximize the information content for a given number of measurements. In this project, trajectory optimization leads to the development of UAV flight paths that provide the highest amount of information about the target, while considering sensor restrictions, vehicle dynamics and operation constraints.(cont.) The UAV trajectory optimization is performed for stationary targets, dynamic targets and multiple targets, for many different scenarios of vehicle motion constraints. The resulting trajectories show spiral paths taken by the UAV, which focus on increasing the angular separation between measurements and reducing the relative range to the target, thus maximizing the information provided by each measurement and improving the performance of the estimation. The main drawback of information based trajectory design is the dependence of the Fisher Information Matrix on the true target location. This issue is addressed in this project by executing simultaneous target location estimation and UAV trajectory optimization. Two estimation algorithms, the Extended Kalman Filter and the Particle Filter are considered, and the trajectory optimization is performed using the mean value of the target estimation in lieu of the true target location. The estimation and optimization algorithms run in sequence and are updated in real-time. The results show spiral UAV trajectories that increase filter convergence and overall estimation accuracy, illustrating the importance of information-based trajectory design for target localization using small UAVs.by Sameera S. Ponda.S.M

A perspective on emerging automotive safety applications, derived from lessons learned through participation in the DARPA Grand Challenges

This paper reports on various aspects of the Intelligent Vehicle Systems (IVS) team's involvement in the recent 2007 DARPA Urban Challenge, wherein our platform, the autonomous “XAV-250,'' competed as one of the 11 finalists qualifying for the event. We provide a candid discussion of the hardware and software design process that led to our team's entry, along with lessons learned at this event and derived from participation in the two previous Grand Challenges. In addition, we give an overview of our vision-, radar-, and LIDAR-based perceptual sensing suite, its fusion with a military-grade inertial navigation package, and the map-based control and planning architectures used leading up to and during the event. The underlying theme of this article is to elucidate how the development of future automotive safety systems can potentially be accelerated by tackling the technological challenges of autonomous ground vehicle robotics. Of interest, we will discuss how a production manufacturing mindset imposes a unique set of constraints upon approaching the problem and how this worked for and against us, given the very compressed timeline of the contests. © 2008 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61244/1/20264_ftp.pd

Trajectory design and control for formation flying spaceborne interferometers

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (p. 125-127).Spaceborne interferometry promises to greatly expand our knowledge of astronomy and astrophysics, and open the doors to many new discoveries. The purpose of this study is to investigate optimal resource management techniques for separated space-craft interferometers to successfully synthesize images. Assuming optimal imaging configurations that satisfy astronomical requirements have been selected, a two-step approach is taken to satisfy these requirements: (1) develop a framework to man-age control effort among different satellites during observation and retargeting of the spacecraft formations, to thereby maximize the number of observations that can be taken with a given amount of consumables, and (2) determine computationally efficient control techniques to minimize control effort while meeting image synthesis metrics. First, issues relating to planning optimal trajectories that trade imaging metrics for spacecraft design metrics such as mission length and spacecraft mass are addressed. The determination of optimal spacecraft locations or trajectories for image acquisition is studied to satisfy astronomical constraints. These positioning requirements lead to the computation of trajectories for the retargeting of formation flying interferometers to capture images of a new astronomical target. Second, the trajectories planned under this approach are used in the formulation of a tracking control problem for spaceborne interferometric apertures.(cont.) The assumptions made in the control problem are used as a basis for the development of different control techniques that trade image quality for fuel expenditure, and evaluated according to scenarios involving different properties relevant to synthetic imaging. The result from these two steps are then applied to the SPHERES testbed, a six-degree-of-freedom facility designed for the incremental maturation of formation flight technologies in a risk-tolerant microgravity environment. Results from simulations and experiments on board the space station are presented and compared to their theoretical outcomes.by Christophe Ph. Mandy.S.M