A noise-constrained distributed adaptive direct position determination algorithm

In this work, we consider distributed localization of an emitter using a wireless senor network, where each sensor can, respectively, receive the signal transmitted by the emitter, estimate the noise variance and share information with its neighbors. We propose herein a noise-constrained distributed adaptive direct position determination (NCD-ADPD) algorithm by exploiting the a priori knowledge of the noise variance. The NCD-ADPD algorithm turns out to be a variable step-size extension of the recently proposed distributed adaptive direct position determination (D-ADPD) algorithm. Compared with its predecessor, the NCD-ADPD algorithm is endowed with markedly improved localization performance, which is validated by simulation results, at the cost of a slight increase of computational complexity.