Simulated Annealing Mechanic Based Noncoherent Signal Detection for Ultra-wideband Sensor Networks

Ultra-wideband (UWB) sensors have extensive commercial and military applications. Unfortunately, coherent signal detection in the presence of intensive multipath propagations may generally become impractical due to complicated realization algorithms and hardware requirements. In this article, we deal with noncoherent UWB signal detection within a promising biological framework, which can also be generalized to the binary-hypothesis target-detection problem, i.e. identification of the presence or absence of a target. Through the developed characteristic representations, signal detection is firstly formulated as a two-group pattern (or target) classification problem in a 2-D feature plane, in which an optimal decision bound can be numerically derived given the supervised training instances. This optimization problem is addressed by using the nature-inspired simulated annealing algorithm (SA), which essentially emulates the physical annealing process of forming the freeze state with the minimum energy. In sharp contrast to traditional optimization techniques, by probabilistically permitting search movement towards worse solutions, SA algorithm can converge to the global optimal with an asymptotical probability of 1. The numerically derived detection performance demonstrated that our present technique is much superior to the existing noncoherent schemes, which provides the appealing signal/target detection architecture for the emerging UWB sensor networks.