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Distributed compressionestimation using wireless sensor networks  The design goals of performance, bandwidth efficiency, scalability, and robustness
 IEEE SIGNAL PROCESSING MAG
, 2006
"... A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advance ..."
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Cited by 78 (1 self)
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A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications [1], [32].
Distributed Kalman filtering based on consensus strategies
, 2007
"... In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman ..."
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Cited by 56 (1 self)
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In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalmanlike measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we showed that although the joint optimization of the consensus matrix and the Kalman gain is in general a nonconvex problem, it is possible to compute them under some important scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.
Diffusion strategies for distributed Kalman filtering: formulation and performance analysis
 in Proceedings of the IAPR Workshop on Cognitive Information Processing
, 2008
"... We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the informa ..."
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Cited by 34 (5 self)
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We consider the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their individual measurements. Our focus is on diffusion strategies, where nodes communicate with their direct neighbors only, and the information is diffused across the network. We derive and analyze the mean and meansquare performance of the proposed algorithms and show by simulation that they outperform previous solutions. 1.
Optimal Motion Strategies for Rangeonly Constrained Multisensor Target Tracking
, 2006
"... Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, ..."
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Cited by 26 (8 self)
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Abstract—In this paper, we study the problem of optimal trajectory generation for a team of mobile sensors tracking a moving target using distanceonly measurements. This problem is shown to be NPHard, in general, when constraints are imposed on the speed of the sensors. We propose two algorithms, modified GaussSeidelrelaxation and LPrelaxation, for determining the set of feasible locations that each sensor should move to in order to collect the most informative measurements; i.e., distance measurements that minimize the uncertainty about the position of the target. Furthermore, we prove that the motion strategy that minimizes the trace of the position error covariance matrix is equivalent to the one that maximizes the minimum eigenvalue of its inverse. The two proposed algorithms are applicable regardless of the process model that is employed for describing the motion of the target, while the computational complexity of both methods is linear in the number of sensors. Extensive simulation results are presented demonstrating that the performance attained with the proposed methods is comparable to that obtained with gridbased exhaustive search, whose computational cost is exponential in the number of sensors, and significantly better than that of a random, towards the target, motion strategy.
Distributed Object Tracking Using a ClusterBased Kalman Filter in Wireless Camera Networks
, 2008
"... Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges such as the necessity to es ..."
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Cited by 22 (4 self)
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Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges such as the necessity to estimate, usually in realtime, the constantlychanging state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a clusterbased Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to
Cooperative MultiRobot Localization under Communication Constraints
"... Abstract — This paper addresses the problem of cooperative localization (CL) under severe communication constraints. Specifically, we present minimum mean square error (MMSE) and maximum a posteriori (MAP) estimators that can process measurements quantized with as little as one bit per measurement. ..."
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Cited by 18 (6 self)
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Abstract — This paper addresses the problem of cooperative localization (CL) under severe communication constraints. Specifically, we present minimum mean square error (MMSE) and maximum a posteriori (MAP) estimators that can process measurements quantized with as little as one bit per measurement. During CL, each robot quantizes and broadcasts its measurements and receives the quantized observations of its teammates. The quantization process is based on the appropriate selection of thresholds, computed using the current state estimates, that minimize the estimation error metric considered. Extensive simulations demonstrate that the proposed IterativelyQuantized Extended Kalman filter (IQEKF) and the Iteratively Quantized MAP (IQMAP) estimator achieve performance indistinguishable of that of their realvalued counterparts (EKF and MAP, respectively) when using as few as 4 bits for quantizing each robot measurement. I.
Kalman Filtering in Wireless Sensor Networks REDUCING COMMUNICATION COST IN STATEESTIMATION PROBLEMS
"... Awireless sensor network (WSN) is a collection of physically distributed sensing devices that can communicate through a shared wireless channel. Sensors can be deployed, for example, to detect the presence of a contaminant in a water reservoir, to estimate the temperature in an orange grove, or to t ..."
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Cited by 13 (1 self)
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Awireless sensor network (WSN) is a collection of physically distributed sensing devices that can communicate through a shared wireless channel. Sensors can be deployed, for example, to detect the presence of a contaminant in a water reservoir, to estimate the temperature in an orange grove, or to track the position of a moving target. The promise of WSNs stems from the benefits of distributed sensing and control. For example, in the targettracking setup depicted in Figure 1, where sensors measure their distance to a target whose trajectory is to be estimated, the benefit of distributed sensing is the availability of observations with high signaltonoise ratio (SNR). Whether collected by a passive radar, which estimates distances by the strength of an electromagnetic signature emitted by the
Optimal motion strategies for rangeonly distributed target tracking
 in Proceedings of the American Control Conference
"... Abstract — In this paper we study the problem of optimal trajectory generation for a team of mobile robots that tracks a moving target using rangeonly measurements. We propose an adaptiverelaxation algorithm for determining the set of feasible locations that each robot must move to in order to col ..."
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Cited by 11 (1 self)
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Abstract — In this paper we study the problem of optimal trajectory generation for a team of mobile robots that tracks a moving target using rangeonly measurements. We propose an adaptiverelaxation algorithm for determining the set of feasible locations that each robot must move to in order to collect the most informative measurements; i.e., distance measurements that minimize the uncertainty about the position of the target. We prove that the motion strategy that minimizes the trace of the position error covariance matrix is equivalent to the one that minimizes its maximum eigenvalue. The proposed method is applicable regardless of the process model employed for describing the motion of the target while its computational complexity is linear in the number of robots. Extensive simulation results are presented, demonstrating that the performance attained with the proposed method is comparable to that obtained with exhaustive search whose computational cost is exponential in the number of robots. I.
Decentralized Variational Filtering for Target Tracking in Binary Sensor Networks
 Proc. IEEE CS First Ann. Conf. Sensor and Ad Hoc Comm
, 2010
"... Abstract—The prime motivation of our work is to balance the inherent tradeoff between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a clusterbased scheme is exploited. At every sampli ..."
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Cited by 11 (3 self)
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Abstract—The prime motivation of our work is to balance the inherent tradeoff between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a clusterbased scheme is exploited. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a nonmyopic rule, which is based on not only the target state prediction but also its future tendency. Second, the variational filtering algorithm is capable of precise tracking even in the highly nonlinear case. Furthermore, since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved. The intercluster information exchange is thus reduced to one single Gaussian statistic, dramatically cutting down the resource consumption. Third, a binary proximity observation model is employed by the activated slave sensors to reduce the energy consumption and to minimize the intracluster communication. Finally, the effectiveness of the proposed approach is evaluated and compared with the stateoftheart algorithms in terms of tracking accuracy, internode communication, and computation complexity.
2007a), ‘Binary Variational Filtering for Target Tracking in Sensor Networks
 in Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 26–29
"... Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and internode information exchange of the tracking algorithm. In this paper, we propose a binary variational alg ..."
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Cited by 10 (5 self)
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Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and internode information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decisionmaking. Its binary extension further reduces the resource consumption by locally exchanging only few bits. 1.