Zhiqiang Liang

The Statistical Meaning of Kurtosis and Its New Application to Identification of Persons Based on Seismic Signals

This paper presents a new algorithm making use of kurtosis, which is a statistical parameter, to distinguish the seismic signal generated by a persons footsteps from other signals. It is adaptive to any environment and needs no machine study or training. As persons or other targets moving on the ground generate continuous signals in the form of seismic waves, we can separate different targets based on the seismic waves they generate. The parameter of kurtosis is sensitive to impulsive signals, so its much more sensitive to the signal generated by person footsteps than other signals generated by vehicles, winds, noise, etc. The parameter of kurtosis is usually employed in the financial analysis, but rarely used in other fields. In this paper, we make use of kurtosis to distinguish person from other targets based on its different sensitivity to different signals. Simulation and application results show that this algorithm is very effective in distinguishing person from other targets.

Comparison and Analysis of Distributed Detection Algorithms for Ground Moving Targets in Sensor Networks

This paper considers the problem of detecting ground moving targets in a wireless sensor network for military surveillance application. The sensor nodes are severely constrained in both power and computational performance, and the battle field environments are commonly very complicated. A modified order statistic constant false alarm rate (OS-CFAR) detection algorithm was chosen to be implemented on sensor nodes. The algorithm includes background estimate and adaptive threshold. The experimental results with real acoustic and seismic data indicate that this algorithm is robust and flexible.

Energy efficient and robust CSIP algorithm in distributed wireless sensor networks

This paper develops an energy efficient and robust collaborative signal and information processing (CSIP) algorithm and applies it to vehicle classification applications. The conventional algorithms collaboratively process all the time-series data from every node in the network. This signal-noise ratio (SNR)-based CSIP algorithm (SNRCSIP) collaboratively processes only the extracted features from part of the nodes. This algorithm efficiently reduces the energy consumption compared with the conventional algorithms by reducing the traffic. Apart from the energy efficiency, we demonstrate the robustness of the SNRCSIP algorithm by giving the high correct recognition ratio between the tracked vehicle and the wheeled vehicle with the acoustic features extracted by an improved form of mel filter bank (MFB), which is rarely applied in vehicle classification applications. Experimental results show that the SNRCSIP algorithm greatly reduces the energy consumption and achieves quite satisfied correct recognition ratio with the features extracted by the improved MFB.

Effective improvement on traditional filter to reduce envelope delay

This paper describes an approach which can reduce envelope delay effectively to improve traditional filter. In some applications, traditional filter is applied to get the envelope of signal, but there is long envelope delay using traditional filter which is not suitable for real time systems, such as ground moving target detection in wireless sensor network. This paper presents a weighted filter approach to reduce envelope delay.