Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Sensors, measurement fusion and missile trajectory optimisation

When considering advances in “smart” weapons it is clear that air-launched systems have adopted an integrated approach to meet rigorous requirements, whereas air-defence systems have not. The demands on sensors, state observation, missile guidance, and simulation for air-defence is the subject of this research. Historical reviews for each topic, justification of favoured techniques and algorithms are provided, using a nomenclature developed to unify these disciplines. Sensors selected for their enduring impact on future systems are described and simulation models provided. Complex internal systems are reduced to simpler models capable of replicating dominant features, particularly those that adversely effect state observers. Of the state observer architectures considered, a distributed system comprising ground based target and own-missile tracking, data up-link, and on-board missile measurement and track fusion is the natural choice for air-defence. An IMM is used to process radar measurements, combining the estimates from filters with different target dynamics. The remote missile state observer combines up-linked target tracks and missile plots with IMU and seeker data to provide optimal guidance information. The performance of traditional PN and CLOS missile guidance is the basis against which on-line trajectory optimisation is judged. Enhanced guidance laws are presented that demand more from the state observers, stressing the importance of time-to-go and transport delays in strap-down systems employing staring array technology. Algorithms for solving the guidance twopoint boundary value problems created from the missile state observer output using gradient projection in function space are presented. A simulation integrating these aspects was developed whose infrastructure, capable of supporting any dynamical model, is described in the air-defence context. MBDA have extended this work creating the Aircraft and Missile Integration Simulation (AMIS) for integrating different launchers and missiles. The maturity of the AMIS makes it a tool for developing pre-launch algorithms for modern air-launched missiles from modern military aircraft.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Approximate Gaussian Conjugacy: Parametric Recursive Filtering Under Nonlinearity, Multimodal, Uncertainty, and Constraint, and Beyond

This is a post-peer-review, pre-copyedit version of an article published in Frontiers of Information Technology & Electronic Engineering. The final authenticated version is available online at: https://doi.org/10.1631/FITEE.1700379Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Practical methods for Gaussian mixture filtering and smoothing

In many applications, there is an interest in systematically and sequentially estimating quantities of interest in a dynamical system, using indirect and inaccurate sensor observations. There are three important sub-problems of sequential estimation: prediction, filtering and smoothing. The objective in the prediction problem is to estimate the future states of the system, using the observations until the current point in time. In the filtering problem, we seek to estimate the current state of the system, using the same information and in the smoothing problem, the aim is to estimate a past state. The smoothing estimate has the advantage that it offers the best performance on average compared to filtering and prediction estimates. Often, the uncertainties regarding the system and the observations are modeled using Gaussian mixtures (GMs). The smoothing solutions to GMs are usually based on pruning approximations, which suffer from the degeneracy problem, resulting in inconsistent estimates. Solutions based on merging have not been explored well in the literature. We address the problem of GM smoothing using both pruning and merging approximations. We consider the two main smoothing strategies of forward-backward smoothing (FBS) and two-filter smoothing (TFS), and develop novel algorithms for GM smoothing which are specifically tailored for the two principles. The FBS strategy involves forward filtering followed by backward smoothing. The existing literature provides pruning-based solutions to the forward filtering and the backward smoothing steps involved. In this thesis, we present a novel solution to the backward smoothing step of FBS, when the forward filtering uses merging methods. The TFS method works by running two filtering steps: forward filtering and backward filtering. It is not possible to apply the pruning or merging strategies to the backward filtering, as it is not a density function. To the best of our knowledge, there does not exist practical approximation techniques to reduce the complexity of the backward filtering. Therefore, in this thesis we propose two novel techniques to approximate the output of the backward filtering, which we call intragroup approximation and smoothed posterior pruning. We also show that the smoothed posterior pruning technique is applicable to forward filtering as well. The FBS and TFS solutions based on the proposed ideas are implemented for a single target tracking scenario and are shown to have similar performance with respect to root mean squared error, normalized estimation error squared, computational complexity and track loss. Compared to the FBS based on N-scan pruning, both these algorithms provide estimates with high consistency and low complexity

Integrated perception, modeling, and control paradigm for bistatic sonar tracking by autonomous underwater vehicles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 357-364).In this thesis, a fully autonomous and persistent bistatic anti-submarine warfare (ASW) surveillance solution is developed using the autonomous underwater vehicles (AUVs). The passive receivers are carried by these AUVs, and are physically separated from the cooperative active sources. These sources are assumed to be transmitting both the frequency-modulated (FM) and continuous wave (CW) sonar pulse signals. The thesis then focuses on providing novel methods for the AUVs/receivers to enhance the bistatic sonar tracking performance. Firstly, the surveillance procedure, called the Automated Perception, is developed to automatically abstract the sensed acoustical data from the passive receiver to the track report that represents the situation awareness. The procedure is executed sequentially by two algorithms: (i) the Sonar Signal Processing algorithm - built with a new dual-waveform fusion of the FM and CW signals to achieve reliable stream of contacts for improved tracking; and (ii) the Target Tracking algorithm - implemented by exploiting information and environmental adaptations to optimize tracking performance. Next, a vehicular control strategy, called the Perception-Driven Control, is devised to move the AUV in reaction to the track report provided by the Automated Perception. The thesis develops a new non-myopic and adaptive control for the vehicle. This is achieved by exploiting the predictive information and environmental rewards to optimize the future tracking performance. The formulation eventually leads to a new information-theoretic and environmental-based control. The main challenge of the surveillance solution then rests upon formulating a model that allows tracking performance to be enhanced via adaptive processing in the Automated Perception, and adaptive mobility by the Perception-Driven Control. A Unified Model is formulated in this thesis that amalgamates two models: (i) the Information-Theoretic Model - developed to define the manner at which the FM and CW acoustical, the navigational, and the environmental measurement uncertainties are propagated to the bistatic measurement uncertainties in the contacts; and (ii) the Environmental-Acoustic Model - built to predict the signal-to-noise power ratios (SNRs) of the FM and CW contacts. Explicit relationships are derived in this thesis using information theory to amalgamate these two models. Finally, an Integrated System is developed onboard each AUV that brings together all the above technologies to enhance the bistatic sonar tracking performance. The system is formulated as a closed-loop control system. This formulation provides a new Integrated Perception, Modeling, and Control Paradigm for an autonomous bistatic ASW surveillance solution using AUVs. The system is validated using the simulated data, and the real data collected from the Generic Littoral Interoperable Network Technology (GLINT) 2009 and 2010 experiments. The experiments were conducted jointly with the NATO Undersea Research Centre (NURC).by Raymond Hon Kit Lum.Sc.D

Application of data and information fusion

Ph.DDOCTOR OF PHILOSOPH

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Bowdoin Orient v.135, no.1-25 (2005-2006)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1006/thumbnail.jp