Enhanced sensing error probability estimation for iterative data fusion in the low SNR regime

In this paper we consider a network of distributed sensors which simultaneously measure a physical parameter of interest, subject to a certain probability of sensing error. The sensed information at each of such nodes is channel-encoded and forwarded to a central receiver through parallel independent AWGN channels. In this scenario, several recent contributions have shown that the end-to-end Bit Error Rate (BER) performance can be dramatically improved if the decoders associated to each received signal and the data fusion stage exchange soft information in an iterative Turbo-like fashion. In order to achieve optimum performance, the probability of sensing error must be known (or estimated) at the receiver. In this work we describe a novel method for estimating such sensing error probability by properly weighting likelihoods output from the Soft-Input Soft-Output decoders (SISO), which is shown to outperform other estimation methods based in hard-decision comparisons, specially in the low SNR regime.