Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamentals of Convolutional Coding
Fundamentals of Convolutional Coding
Occupancy grids: a probabilistic framework for robot perception and navigation
Occupancy grids: a probabilistic framework for robot perception and navigation
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
A probabilistic approach to inference with limited information in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
On the interdependence of sensing and estimation complexity in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Sensing capacity for discrete sensor network applications
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Large-scale detection problems, where the number of hypotheses is exponentially large, characterize many important sensor network applications. In such applications, sensors whose output is simultaneously affected by multiple target locations in the environment pose a significant computational challenge. Conditioned on such sensor measurements, separate target locations become dependent, requiring computationally expensive joint detection. Therefore there exists a tradeoff between the computational complexity and accuracy of detection. In this paper we demonstrate that this tradeoff can be altered by collecting additional sensor measurements, enabling algorithms that are both accurate and computationally efficient. We draw the insight for this tradeoff from our work on the sensing capacity of sensor networks, a quantity analogous to the channel capacity in communications. To demonstrate this tradeoff, we apply sequential decoding algorithms to a large-scale detection problem using a realistic infrared temperature sensor model and real experimental data. We explore the tradeoff between the number of sensor measurements, accuracy, and computational complexity. For a sufficient number of sensor measurements, we demonstrate that sequential decoding algorithms have sharp empirical performance transitions, becoming both computationally efficient and accurate. We provide extensive comparisons with belief propagation and a simple heuristic algorithm. For a temperature sensing application, we empirically demonstrate that given sufficient sensor measurements, belief propagation has exponential complexity and sequential decoding has linear complexity in sensor field of view. Despite this disparity in complexity, sequential decoding was significantly more accurate.