Decentralized Observability with Limited Communication between Sensors

In this paper, we study the problem of jointly retrieving the state of a dynamical system, as well as the state of the sensors deployed to estimate it. We assume that the sensors possess a simple computational unit that is capable of performing simple operations, such as retaining the current state and model of the system in its memory. We assume the system to be observable (given all the measurements of the sensors), and we ask whether each sub-collection of sensors can retrieve the state of the underlying physical system, as well as the state of the remaining sensors. To this end, we consider communication between neighboring sensors, whose adjacency is captured by a communication graph. We then propose a linear update strategy that encodes the sensor measurements as states in an augmented state space, with which we provide the solution to the problem of retrieving the system and sensor states. The present paper contains three main contributions. First, we provide necessary and sufficient conditions to ensure observability of the system and sensor states from any sensor. Second, we address the problem of adding communication between sensors when the necessary and sufficient conditions are not satisfied, and devise a strategy to this end. Third, we extend the former case to include different costs of communication between sensors. Finally, the concepts defined and the method proposed are used to assess the state of an example of approximate structural brain dynamics through linearized measurements.Comment: 15 pages, 5 figures, extended version of paper accepted at IEEE Conference on Decision and Control 201

On the Limited Communication Analysis and Design for Decentralized Estimation

This paper pertains to the analysis and design of decentralized estimation schemes that make use of limited communication. Briefly, these schemes equip the sensors with scalar states that iteratively merge the measurements and the state of other sensors to be used for state estimation. Contrarily to commonly used distributed estimation schemes, the only information being exchanged are scalars, there is only one common time-scale for communication and estimation, and the retrieval of the state of the system and sensors is achieved in finite-time. We extend previous work to a more general setup and provide necessary and sufficient conditions required for the communication between the sensors that enable the use of limited communication decentralized estimation~schemes. Additionally, we discuss the cases where the sensors are memoryless, and where the sensors might not have the capacity to discern the contributions of other sensors. Based on these conditions and the fact that communication channels incur a cost, we cast the problem of finding the minimum cost communication graph that enables limited communication decentralized estimation schemes as an integer programming problem.Comment: Updates on the paper in CDC 201

Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement

Gossip Algorithms for Distributed Signal Processing

Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This article presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page

Limited Bandwidth Wireless Communication Strategies for Structural Control of Seismically Excited Shear Structures

Structural control is used to mitigate unwanted vibrations in structures when large excitations occur, such as high winds and earthquakes. To increase reliability and controllability in structural control applications, engineers are making use of semi-active control devices. Semi-active control gives engineers greater control authority over structural response versus passive controllers, but are less expensive and more reliable than active devices. However, the large numbers of actuators required for semi-active structural control networks introduce more cabling within control systems leading to increased cost. Researchers are exploring the use of wireless technology for structural control to cut down on the installation cost associated with cabling. However wireless communication latency (time delays in data transmissions) can be a barrier to full acceptance of wireless technology for structural control. As the number of sensors in a control network grows, it becomes increasingly difficult to transmit all sensor data during a single control step over the fixed wireless bandwidth. Because control force calculations rely on accurate state measurements or estimates, the use of strategic bandwidth allocation becomes more necessary to provide good control performance. The traditional method for speeding up the control step in larger wireless networks is to spatially decentralize the network into multiple subnetworks, sacrificing communication for speed. This dissertation seeks to provide an additional approach to address the issue of communication latency that may be an alternative, or even a supplement, to spatial decentralization of the control network. The proposed approach is to use temporal decentralization, or the decentralization of the control network over time, as opposed to space/location. Temporal decentralization is first presented with a means of selecting and evaluating different communication group sizes and wireless unit combinations for staggered temporal group communication that still provide highly accurate state estimates. It is found that, in staggered communication schemes, state estimation and control performance are affected by the network topology used at each time step with some sensor combinations providing more useful information than others. Sensor placement theory is used to form sensor groups that provide consistently high-quality output information to the network during each time step, but still utilize all sensors. If the demand for sensors to communicate data outweighs the available bandwidth, traditional temporal and spatial approaches are no longer feasible. This dissertation examines and validates a dynamic approach for bandwidth allocation relying on an extended, autonomous and controller-aware, carrier sense multiple access with collision detection (CSMA/CD) protocol. Stochastic parameters are derived to strategically alter back-off times in the CSMA/CD algorithm based on nodal observability and output estimation error. Inspired by data fusion approaches, this second study presents two different methods for neighborhood state estimation using a dynamic form of measurement-only fusion. To validate these wireless structural control approaches, a small-scale experimental semi-active structural control testbed is developed that captures the important attributes of a full-scale structure

Optimal co-design of control, scheduling and routing in multi-hop control networks

A Multi-hop Control Network consists of a plant where the communication between sensors, actuators and computational units is supported by a (wireless) multi-hop communication network, and data flow is performed using scheduling and routing of sensing and actuation data. Given a SISO LTI plant, we will address the problem of co-designing a digital controller and the network parameters (scheduling and routing) in order to guarantee stability and maximize a performance metric on the transient response to a step input, with constraints on the control effort, on the output overshoot and on the bandwidth of the communication channel. We show that the above optimization problem is a polynomial optimization problem, which is generally NP-hard. We provide sufficient conditions on the network topology, scheduling and routing such that it is computationally feasible, namely such that it reduces to a convex optimization problem.Comment: 51st IEEE Conference on Decision and Control, 2012. Accepted for publication as regular pape

sUAS Swarm Navigation using Inertial, Range Radios and Partial GNSS

Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e. a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g., precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU, baro-altimeter and range-radio measurements to obtain the platform’s absolute and relative position. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range meas-urements. This solution enables the system to maintain an accurate relative position estimate, and reduce the drift in the swarm’s absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components (three multi-copters and one ground station) and analyzes and discusses the performance using both simulation and sUAS flight test data