## Abstract

We consider the problem of localizing a single source using received signal strength measurements gathered at a number of sensors. We assume that the measurements follow the standard path loss model and are corrupted by additive white Gaussian noise. Under this model, the maximum likelihood solution to the source localization problem involves solving a non-linear least squares optimization problem. We study the convergence property of a normalized incremental gradient method for solving this problem. Remarkably, despite the fact that the problem is non-convex, the normalized incremental gradient method generates a sequence of iterates which are attracted to the global optimum under some mild conditions.

Original language | English (US) |
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Title of host publication | Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 |

Pages | 1417-1421 |

Number of pages | 5 |

DOIs | |

State | Published - 2012 |

Event | 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States Duration: Nov 4 2012 → Nov 7 2012 |

### Other

Other | 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 |
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Country | United States |

City | Pacific Grove, CA |

Period | 11/4/12 → 11/7/12 |

## ASJC Scopus subject areas

- Signal Processing
- Computer Networks and Communications