| Paper: | SAM-P5.4 | ||
| Session: | Sensor Networks | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Sensor Array and Multichannel Signal Processing: Signal detection and estimation | ||
| Title: | DETECTION IN DECENTRALIZED SENSOR NETWORKS | ||
| Authors: | Saeed Aldosari; Carnegie Mellon University | ||
| José Moura; Carnegie Mellon University | |||
| Abstract: | Advances in integrated technologies are making networks of many inexpensive deployable autonomous sensors a reality. Individually, each sensor may not accomplish much, but working cooperatively they have for example the potential to monitor large areas, detect the presence or absence of targets, or track moving objects. These sensors operate under constraints imposed by scarce power and other limited resources like bandwidth or computing capacity. The paper considers detection in such a distributed sensors environment. We investigate the impact on detection performance, as measured by the probability of error, of such parameters as number of sensors, number of quantization levels at each sensor, or signal to noise ratio, under a rate constraint on the common access communications channel. We optimize the local detectors when the number of sensors is large. We show that the performance loss due to quantization decays exponentially fast as the number of bits per sensor increases and that the choice between hard versus soft local detectors depends not only on the noise distribution and the quantization rate, but also on the SNR under which the sensors operate. | ||
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