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Standard linear diversity combining techniques are not eeective in combating fading in the presence of non-Gaussian noise. An adaptive spatial diversity receiver is developed for wireless communication channels with slow, at fading and additive non-Gaussian noise. The noise is modeled as a mixture of Gaussian distributions, and the expectation-maximization(More)
Passive sensing of acoustic sources is attractive in many respects, including the relatively low signal bandwidth of sound waves, the loudness of most sources of interest, and the inherent difficulty of disguising or concealing emitted acoustic signals. The availability of inexpensive, low-power sensing and signal-processing hardware enables application of(More)
We present a general approach for multi-modal sensor fusion based on nonparametric probability density estimation and maximization of a mutual information criterion. We apply this approach to fusion of vector-magnetic and acoustic data for classification of vehicles. Linear features are used, although the approach may be applied more generally with other(More)
—Multiple sensor arrays provide the means for highly accurate localization of the () position of a source. In some applications , such as microphone arrays receiving aeroacoustic signals from ground vehicles, random fluctuations in the air lead to frequency-selective coherence losses in the signals that arrive at widely separated sensors. We present(More)
We consider the problem of sensor node localization in a randomly deployed sensor network, using a mobile access point (AP). The mobile AP can be used to localize many sensors simultaneously in a broadcast mode, without a pre-established sensor network. We consider a multi-modal approach , combining radio and acoustics. The radio broadcasts timing, location(More)