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Cooperative localization plays a key role in locationaware service of wireless networks. However, the statistical-based estimator of network localization, e.g., the maximum likelihood estimator or the maximum a posterior estimator, is commonly non-convex due to nonlinear measurement function and/or non-Gaussian system disturbance, which complicates the localization of network nodes. In this presentation, a novel particle-assisted stochastic search (PASS) algorithm is proposed to find out the optimal node locations based on its non-convex objective function.

In this paper, the detection of shock wave that generated by supersonic bullet is considered. We present a new framework based on wavelet multi-scale products method. We analyze the method under the standard likelihood ratio test. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An optimal detector under the standard likelihood ratio test is proposed.

In this manuscript the application of a factor graph approach to the filtering problem for a mixed linear/nonlinear state-space model is investigated. In particular, after developing a factor graph for the considered model, a novel approximate recursive technique for solving such a problem is derived applying the sum-product algorithm and a specific scheduling procedure for message passing to this graph.

The problem of estimating the frequencies of sinusoidal components from a finite number of noisy discrete-time measurements has attracted a great deal of attention and still is an active research area to date, because of its wide applications in science and engineering. In this presentation, simple and accurate solutions for sinusoidal frequency estimation of 1D and 2D signals in the presence of additive white Gaussian noise are presented.

Finding the position of a target based on measurements from an array of spatially separated sensors has been an important problem in radar, sonar, global positioning system, mobile communications, multimedia and wireless sensor networks. Time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS) and direction-of-arrival (DOA) of the emitted signal are commonly used measurements for source localization. Basically, TOAs, TDOAs and RSSs provide the distance information between the source and sensors while DOAs are the source bearings relative to the receivers.

Analyzing the performance of estimators is a typical task in signal processing. Two fundamental performance measures in the aspect of accuracy are bias and mean square error (MSE). In this presentation, we revisit a simple technique to produce the bias and MSE of an estimator that minimizes or maximizes an unconstrained differentiable cost function over a continuous space of the parameter vector under the small error conditions. This presentation is a companion work of: H. C. So, Y. T. Chan, K. C. Ho and Y.

This theoretical paper aims to provide a probabilistic framework for graph signal processing. By modeling signals on graphs as Gaussian Markov Random Fields, we present numerous important aspects of graph signal processing, including graph construction, graph transform, graph downsampling, graph prediction, and graph-based regularization, from a probabilistic point of view.