Acoustic Source Location in a Three-Dimensional Space Using Crosspower Spectrum Phase
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Smart Dust - Hardware Limits to Wireless Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Maximum likelihood methods for bearings-only target localization
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
IEEE Transactions on Signal Processing
Scalable and low-cost acoustic source localization for wireless sensor networks
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
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One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) estimation algorithm is one of the estimation algorithms of target location. The ML estimation algorithm has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. The EM (Expectation Maximization) algorithm is proposed to reduce the complexity of the ML estimation algorithm. However, the EM algorithm sometimes traps into local minimum. These conventional algorithms to estimate target location use all the sensors' receiving signals. The transmission signal from the target is attenuated with distance. In particular, the effects of noise on the received signals of the sensors far apart from the target are large. The received signals thus do not help a lot to improve the estimation accuracy. In this paper, we propose the new algorithm to estimate a target location with a smaller amount of calculation than the ML estimation algorithm and higher estimation accuracy than the EM algorithm. Moreover, we propose the low complexity source localization method, where we use only the sensors' information with receiving energy higher than threshold. From the simulation results, we show that the proposed algorithm has a smaller amount of calculation than the ML estimation algorithm and higher estimation accuracy than the EM algorithm. We also show that proposed method can reduce the calculation amount while keeping the estimation accuracy by setting threshold appropriately in the ML estimation algorithm and the proposed algorithm.