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| In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a di erent view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples. |
| This paper presents the search problem formulated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decisionmaking quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results. |
| This paper presents a decentralized motion planning algorithm for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets. Our gradient-descent method is based on a cost function that measures the overall quality of sensing. We also investigate the role of imperfect communication between sensor agents in this framework, and examine the trade-offs in performance between sensing and communication. Simulations illustrate the basic characteristics of the algorithms. |
| A stochastic algorithm for solving the sensor selection problem is presented. The problem arises when many sensors are jointly trying to estimate a process but only a subset of them can take measurements at any time step. The proposed stochastic sensor selection strategy is easy to implement and is computationally tractable. The algorithm is illustrated through simple examples of sensor scheduling and dynamic sensor coverage. |
| In this paper we characterize the impact of imperfect communication on the performance of a decentralized mobile sensor network. We first examine and demonstrate the trade-offs between communication and sensing objectives, by determining the optimal sensor configurations when introducing imperfect communication. We further illustrate the performance degradation caused by non-ideal communication links in a decentralized mobile sensor network. To address this, we propose a decentralized motion-planning algorithm that considers communication effects. The algorithm is a cross-layer design based on the proper interface of physical and application layers. Simulation results will show the performance improvement attained by utilizing this algorithm. |
| electronic and hardcopy (Chung) |
| In this paper, we consider the problem of active sensing using mobile nodes as a sensor network to estimate the state of a dynamic target. We propose a gradient-search-based decentralized algorithm that demonstrates the benefits of distributed sensing. We then examine the task of tracking multiple targets, and address it via a simple extension to our algorithm. Simulation results show that our simple decentralized approach performs quite well and leads to interesting cooperative behavior. |
| electronic and hardcopy |
| We examine the problem of distributed estimation when only one sensor can take a measurement per time step. We solve for the optimal recursive estimation algorithm when the sensor switching schedule is given. We then consider the effect of noise in communication channels. We also investigate the problem of determining an optimal sensor switching strategy. We see that this problem involves searching a tree in general and propose two strategies for pruning the tree to minimize the computation. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. The performance of the algorithms is illustrated using numerical examples. |
| electronic and hardcopy |
| In this paper, we consider the scenario where many sensors co-operate to estimate a process. Only one sensor can take a measurement at any time step. We wish to come up with optimal sensor scheduling algorithms. The problem is motivated by the use of sonar range-finders used by the vehicles on the Caltech Multi-Vehicle Wireless Testbed. We see that this problem involves searching a tree in general and propose and analyze two strategies for pruning the tree to keep the computation limited. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. We also study a technique that employs choosing the sensors randomly from a probability distribution which can then be optimized. The performance of the algorithms are illustrated with the help of numerical examples. |
| electronic and hardcopy (Chung) |
| electronic |
| This paper presents a formulation for the distributed sensing of a noisy dynamical process by multiple cooperating mobile sensor agents. This problem is motivated by localization and tracking tasks of dynamic targets, and a cost function based on the global quality of sensing is constructed. We seek to understand how mobility of sensing agents can be utilized to improve this quality of sensing. Further, we provide analysis for generating decentralized motion control signals for the mobile agents, derived from the gradient of the cost function. We then investigate the role of imperfect communication between sensor agents in this general framework, and examine the ensuing trade-off in performance between sensing and communication. This approach yields a general understanding of the role of mobility in sensor-based objectives in the presence of a noisy environment. |
| electronic and hardcopy (Chung) |
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