Rule-Based System Architecting of Earth Observing Systems: Earth Science Decadal Survey

This paper presents a methodology to explore the architectural trade space of Earth observing satellite systems, and applies it to the Earth Science Decadal Survey. The architecting problem is formulated as a combinatorial optimization problem with three sets of architectural decisions: instrument selection, assignment of instruments to satellites, and mission scheduling. A computational tool was created to automatically synthesize architectures based on valid combinations of options for these three decisions and evaluate them according to several figures of merit, including satisfaction of program requirements, data continuity, affordability, and proxies for fairness, technical, and programmatic risk. A population-based heuristic search algorithm is used to search the trade space. The novelty of the tool is that it uses a rule-based expert system to model the knowledge-intensive components of the problem, such as scientific requirements, and to capture the nonlinear positive and negative interactions between instruments (synergies and interferences), which drive both requirement satisfaction and cost. The tool is first demonstrated on the past NASA Earth Observing System program and then applied to the Decadal Survey. Results suggest that the Decadal Survey architecture is dominated by other more distributed architectures in which DESDYNI and CLARREO are consistently broken down into individual instruments."La Caixa" FoundationCharles Stark Draper LaboratoryGoddard Space Flight Cente

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

Rule-based system architecting of Earth observation satellite systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 399-412).System architecting is concerned with exploring the tradespace of early, high-level, system design decisions with a holistic, value-centric view. In the last few years, several tools and methods have been developed to support the system architecting process, focusing on the representation of an architecture as a set of interrelated decisions. These tools are best suited for applications that focus on breadth - i.e., enumerating a large and representative part of the architectural tradespace -as opposed to depth - modeling fidelity. However, some problems in system architecting require good modeling depth in order to provide useful results. In some cases, a very large body of expert knowledge is required. Current tools are not designed to handle such large bodies of knowledge because they lack scalability and traceability. As the size of the knowledge base increases, it becomes harder: a) to modify existing knowledge or add new knowledge; b) to trace the results of the tool to the model assumptions or knowledge base. This thesis proposes a holistic framework for architecture tradespace exploration of large complex systems that require a large body of expert knowledge. It physically separates the different bodies of knowledge required to solve a system architecting problem (i.e., knowledge about the domain, knowledge about the class of optimization or search problem, knowledge about the particular instance of problem) by using a rule-based expert system. It provides a generic population-based heuristic algorithm for search, which can be augmented with rules that encode knowledge about the domain, or about the optimization problem or class of problems. It identifies five major classes of system architecting problems from the perspective of optimization and search, and provides rules to enumerate architectures and search through the architectural tradespace of each class. A methodology is also defined to assess the value of an architecture using a rule-based approach. This methodology is based on a decomposition of stakeholder needs into requirements and a systematic comparison between system requirements and system capabilities using the rules engine. The framework is applied to the domain of Earth observing satellite systems (EOSS). Three EOSS are studied in depth: the NASA Earth Observing System, the NRC Earth Science Decadal Survey, and the Iridium GEOscan program. The ability of the framework to produce useful results is shown, and specific insights and recommendations are drawn.by Daniel Selva Valero.Ph.D