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Given the dense multipath propagation in typical ultra-wideband channels, traditional coherent receivers may become computationally complex and impractical. Recently, noncoherent UWB architectures have been motivated with simple implementations. Nevertheless, the rudimentary statistical assumption and practical information uncertainty inevitably results in a hardly optimistic receiving performance. Inspired by the nature processes, in this paper we suggest a noncoherent UWB demodulator based on the particle swarm intelligence which can be realized in two steps. Firstly, a characteristic spectrum is developed from the received samples. From a novel pattern recognition perspective, four distinguishing features are extracted from this characteristic waveform to thoroughly reveal the discriminant properties of UWB multipath signals and channel noise. Subsequently, this established multidimensional feature space is compressed to a two-dimension plane by the optimal features combination technique, and UWB signal detection is consequently formulated to assign these pattern points into two classes at the minimum errors criterion. The optimal combination coefficients and the decision bound are then numerically derived by using the particle swarm optimization. Our biological noncoherent UWB receiver is independent of any explicit channel parameters, and hence is essentially robust to noise uncertainty. Numerical simulations further validate the advantages of our algorithm over the other noncoherent techniques.

Particle Swarm Optimization Based Noncoherent Detector for Ultra-Wideband Radio in Intensive Multipath Environments