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145
Distributing the Kalman filters for largescale systems
 IEEE Trans. on Signal Processing, http://arxiv.org/pdf/0708.0242
"... Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale sys ..."
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Cited by 55 (11 self)
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Abstract—This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, largescale,dimensional, dynamical system monitored by a network of sensors. Local Kalman filters are implemented ondimensional subsystems,, obtained by spatially decomposing the largescale system. The distributed Kalman filter is optimal under an th order Gauss–Markov approximation to the centralized filter. We quantify the information loss due to this thorder approximation by the divergence, which decreases as increases. The order of the approximation leads to a bound on the dimension of the subsystems, hence, providing a criterion for subsystem selection. The (approximated) centralized Riccati and Lyapunov equations are computed iteratively with only local communication and loworder computation by a distributed iterate collapse inversion (DICI) algorithm. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter. Nowhere in the network, storage, communication, or computation ofdimensional vectors and matrices is required; only dimensional vectors and matrices are communicated or used in the local computations at the sensors. In other words, knowledge of the state is itself distributed. Index Terms—Distributed algorithms, distributed estimation, information filters, iterative methods, Kalman filtering, largescale systems, matrix inversion, sparse matrices. I.
Distributed cooperative active sensing using consensus filters.
 In IEEE Int. Conf. on Robotics and Automation,
, 2007
"... AbstractWe consider the problem of multiple mobile sensor agents tracking the position of one or more moving targets. In our formulation, each agent maintains a target estimate, and each agent moves so as to maximize the expected information from its sensor, relative to the current uncertainty in ..."
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Cited by 46 (0 self)
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AbstractWe consider the problem of multiple mobile sensor agents tracking the position of one or more moving targets. In our formulation, each agent maintains a target estimate, and each agent moves so as to maximize the expected information from its sensor, relative to the current uncertainty in the estimate. The novelty of our approach is that each agent need only communicate with onehop neighbors in a communication network, resulting in a fully distributed and scalable algorithm, yet the performance of the system approximates that of a centralized optimal solution to the same problem. We provide two fully distributed algorithms based on onetime measurements and a Kalman filter approach, and we validate the algorithms with simulations.
Distributed Tracking for Mobile Sensor Networks with InformationDriven Mobility
"... In this paper, we address distributed target tracking for mobile sensor networks using the extension of a distributed Kalman filtering (DKF) algorithm introduced by the author in [11]. It is shown that improvement of the quality of tracking by mobile sensors (or agents) leads to the emergence of f ..."
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Cited by 44 (0 self)
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In this paper, we address distributed target tracking for mobile sensor networks using the extension of a distributed Kalman filtering (DKF) algorithm introduced by the author in [11]. It is shown that improvement of the quality of tracking by mobile sensors (or agents) leads to the emergence of flocking behavior. We discuss the benefits of a flockingbased mobility model for distributed Kalman filtering over mobile networks. This mobility model uses author’s flocking algorithm with a natural choice of a moving rendezvous point that is the target itself. As the agents “flock ” towards the target, the information value of their sensor measurements improves in time. During this process, smaller flocks merge and form larger flocks and eventually a single flock with a connected topology emerges. This allows the agents to perform cooperative filtering using the DKF algorithm which considerably improves their tracking performance. We show that this flocking algorithm is in fact an informationdriven mobility that acts as a cooperative control strategy that enhances the aggregate information value of all sensor measurements. A metric for information value is given that has close connections to Fisher information. Simulation results are provided for a group of UAVs with embedded sensors tracking a mobile target using cooperative filtering.
Multiagent coordination by decentralized estimation and control
 IEEE Transactions on Automatic Control
, 2008
"... Abstract — We describe a framework for the design of collective behaviors for groups of identical mobile agents. The approach is based on decentralized simultaneous estimation and control, where each agent communicates with neighbors and estimates the global performance properties of the swarm neede ..."
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Cited by 43 (2 self)
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Abstract — We describe a framework for the design of collective behaviors for groups of identical mobile agents. The approach is based on decentralized simultaneous estimation and control, where each agent communicates with neighbors and estimates the global performance properties of the swarm needed to make a local control decision. Challenges of the approach include designing a control law with desired convergence properties, assuming each agent has perfect global knowledge; designing an estimator that allows each agent to make correct estimates of the global properties needed to implement the controller; and possibly modifying the controller to recover desired convergence properties when using the estimates of global performance. We apply this framework to two different problems: (1) controlling the moment statistics describing the location and shape of a swarm, and (2) cooperative target localization. For the swarm formation control problem, we derive smallgain conditions which, if satisfied, guarantee that the formation statistics are driven to desired values, even in the presence of a changing network topology and the addition and deletion of robots. Index Terms — Multiagent systems, decentralized control, distributed control, dynamic average consensus estimation, formation control. I.
An overview of recent progress in the study of distributed multiagent coordination
, 2012
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Average consensus on networks with transmission noise or quantization
 Proceedings of European Control Conference
, 2007
"... Abstract — In this note we study the average consensus algorithm in a distributed system of agents which are allowed to communicate according to a directed graph. Moreover, the communication between connected agents is not perfect, but affected by some error, which can be either a random additive no ..."
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Cited by 31 (9 self)
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Abstract — In this note we study the average consensus algorithm in a distributed system of agents which are allowed to communicate according to a directed graph. Moreover, the communication between connected agents is not perfect, but affected by some error, which can be either a random additive noise or produced by a quantization. We investigate the effects of these constraints on the performance of the average consensus algorithms. I.
Cooperative filters and control for cooperative exploration
 IEEE Trans. Automatic Control
, 2010
"... Abstract—Autonomous mobile sensor networks are employed to measure largescale environmental fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the cooperative sensing is still an open problem. We develop strategies for multiple sensor platforms to ..."
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Cited by 31 (2 self)
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Abstract—Autonomous mobile sensor networks are employed to measure largescale environmental fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the cooperative sensing is still an open problem. We develop strategies for multiple sensor platforms to explore a noisy scalar field in the plane. Our method consists of three parts. First, we design provably convergent cooperative Kalman filters that apply to general cooperative exploration missions. Second, we present a novel method to determine the shape of the platform formation to minimize error in the estimates and design a cooperative formation control law to asymptotically achieve the optimal formation shape. Third, we use the cooperative filter estimates in a provably convergent motion control law that drives the center of the platform formation to move along level curves of the field. This control law can be replaced by control laws enabling other cooperative exploration motion, such as gradient climbing, without changing the cooperative filters and the cooperative formation control laws. Performance is demonstrated on simulated underwater platforms in simulated ocean fields. Index Terms—Adaptive Kalman filtering, cooperative control, cooperative filtering, mobile sensing networks. I.
Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones
"... We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learnin ..."
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Cited by 30 (4 self)
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We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensorenabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to massscale adoption of mobile phone sensing applications: (i) the humanburden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the “phone experience ” (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proofofconcept speaker recognition application without additional burden to users.
Decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems
 IEEE Trans. Signal Process
, 2007
"... Abstract—In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximumlikelihood (ML) estimate through selfsynchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a firstorder dynamical system ini ..."
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Cited by 29 (2 self)
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Abstract—In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximumlikelihood (ML) estimate through selfsynchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a firstorder dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results. Index Terms—Distributed consensus, distributed estimation, dynamical systems, sensor networks. I.
Distributed consensus with limited communication data rate
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 2011
"... Communication data rate and energy constraints are important factors which have to be considered when investigating distributed coordination of multiagent networks. Although many proposed averageconsensus protocols are available, a fundamental theoretic problem remains open, namely, how many bits ..."
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Cited by 27 (3 self)
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Communication data rate and energy constraints are important factors which have to be considered when investigating distributed coordination of multiagent networks. Although many proposed averageconsensus protocols are available, a fundamental theoretic problem remains open, namely, how many bits of information are necessary for each pair of adjacent agents to exchange at each time step to ensure average consensus? In this paper, we consider averageconsensus control of undirected networks of discretetime firstorder agents under communication constraints. Each agent has a realvalued state but can only exchange symbolic data with its neighbors. A distributed protocol is proposed based on dynamic encoding and decoding. It is proved that under the protocol designed, for a connected network, average consensus can be achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. An explicit form of the asymptotic convergence rate is given. It is shown that as the number of agents increases, the asymptotic convergence rate is related to the scale of the network, the number of quantization levels and the ratio of the second smallest eigenvalue to the largest eigenvalue of the Laplacian of the communication graph. We also give a performance index to characterize the total communication energy to achieve average consensus and show that the minimization of the communication energy leads to a tradeoff between the convergence rate and the number of quantization levels.