### Abstract

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.

Original language | English |
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Title of host publication | PE-WASUN'05 - Proceedings of the Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks |

Editors | M. Ould-Khaoua, M. Takai |

Pages | 130-136 |

Number of pages | 7 |

DOIs | |

Publication status | Published - 2005 |

Event | PE-WASUN'05 - Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks - Montreal, QB, Canada Duration: 2005 Oct 10 → 2005 Oct 13 |

### Other

Other | PE-WASUN'05 - Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks |
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Country | Canada |

City | Montreal, QB |

Period | 05/10/10 → 05/10/13 |

### Fingerprint

### Keywords

- EM Algorithm
- ML Estimation Algorithm
- Sensor networks
- Source localization

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*PE-WASUN'05 - Proceedings of the Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks*(pp. 130-136) https://doi.org/10.1145/1089803.1089977

**Low complexity source localization algorithms in sensor networks.** / Shirahama, Junichi; Ohtsuki, Tomoaki; Kaneko, Toshinobu.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*PE-WASUN'05 - Proceedings of the Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks.*pp. 130-136, PE-WASUN'05 - Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, Montreal, QB, Canada, 05/10/10. https://doi.org/10.1145/1089803.1089977

}

TY - GEN

T1 - Low complexity source localization algorithms in sensor networks

AU - Shirahama, Junichi

AU - Ohtsuki, Tomoaki

AU - Kaneko, Toshinobu

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

KW - EM Algorithm

KW - ML Estimation Algorithm

KW - Sensor networks

KW - Source localization

UR - http://www.scopus.com/inward/record.url?scp=31844441596&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=31844441596&partnerID=8YFLogxK

U2 - 10.1145/1089803.1089977

DO - 10.1145/1089803.1089977

M3 - Conference contribution

AN - SCOPUS:31844441596

SN - 1595931821

SP - 130

EP - 136

BT - PE-WASUN'05 - Proceedings of the Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks

A2 - Ould-Khaoua, M.

A2 - Takai, M.

ER -