Jianming Wei

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.

Improved DS acoustic-seismic modality fusion for ground-moving target classification in wireless sensor networks

An improved DS acoustic-seismic modality fusion framework based on cascaded fuzzy classifier (CFC) is proposed to implement ground-moving target classification tasks locally at sensor nodes in wireless sensor networks (WSN). The CFC consists of three and two component binary fuzzy classifiers (BFCs) in seismic and acoustic signal channel respectively. New basic belief assignment (bba) functions are defined for component binary fuzzy classifiers (BFCs) to give out evidences instead of hard decision labels for each unclassified pattern. Available evidences are then combined into a final node classification report using a modified DS method. M-fold cross-validation experiment results show that this implementation gives significantly better performance than the implementation with a majority-voting fusion and a DS fusion implementation with a linear bba function. Performances on different terrains are also given to validate its robustness.

Virtual field strategy for collaborative signal and information processing in wireless heterogeneous sensor networks

A novel collaborative signal and information processing (CSIP) method, which is based on virtual fields excited by sensor nodes, is proposed for wireless heterogeneous sensor networks. These virtual fields influence states and operations in sensor nodes located in their regions of influence (ROIs) and thus collaboration is implemented through interactions between surrounding virtual fields and sensor nodes. Described by a group of radial basis functions (RBFs), virtual fields have different magnitudes and ROIs due to different initial energy, communication ranges, sensing ranges and information processing capabilities in heterogeneous sensor nodes. Dynamic mobile agent itinerary decision and adaptive node active probability updating are studied with virtual field strategies in a heterogeneous sensor network using mobile-agent-based computing paradigm. Simulation results demonstrate that this approach can reduce energy consumption in sensor nodes. Information gain efficiency and network lifetime are also increased.

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.

Fast communication: Force-directed hybrid PSO-SNTO algorithm for acoustic source localization in sensor networks

As a smart combination of particle swarm optimization (PSO) and sequential number-theoretic optimization (SNTO), a new hybrid PSO-SNTO algorithm is proposed to handle the initialization dependence of basic PSO algorithm. We then apply the hybrid algorithm to the acoustic source localization problem in sensor networks, which is modeled as a maximum likelihood estimation problem. Furthermore, a heuristic method based on virtual force is used to direct the particles of PSO to the global optimum, which can efficiently speed up the algorithm convergence. Simulation results demonstrate that the hybrid algorithm can achieve robust convergence with sophisticated estimation performance, and the convergence rate can be largely enhanced with the assistance of the force-directed heuristics.

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.

An improved DS acoustic-seismic modality fusion algorithm based on a new cascaded fuzzy classifier for ground-moving targets classification in wireless sensor networks

A new cascaded fuzzy classifier (CFC) is proposed to implement ground-moving targets classification tasks locally at sensor nodes in wireless sensor networks (WSN). The CFC is composed of three and two binary fuzzy classifiers (BFC) respectively in seismic and acoustic signal channel in order to classify person, Light-wheeled (LW) Vehicle, and Heavywheeled (HW) Vehicle in presence of environmental background noise. Base on the CFC, a new basic belief assignment (bba) function is defined for each component BFC to give out a piece of evidence instead of a hard decision label. An evidence generator is used to synthesize available evidences from BFCs into channel evidences and channel evidences are further temporal-fused. Finally, acoustic-seismic modality fusion using Dempster-Shafer method is performed. Our implementation gives significantly better performance than the implementation with majority-voting fusion method through leave-one-out experiments.

APPLICATION OF DISTRIBUTED DETECTION METHOD 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 and its distributed detection algorithm were 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 the OS-CFAR is robust and flexible and distributed algorithm can improve the detection performance somewhat.

High Energy-Efficient Mobile Agent Based Information Aggregation in Wireless Sensor Networks

In this paper, we develop an improved energy-efficient mobile agent (EMA) paradigm for information aggregation in wireless sensor networks (WSNs). In EMA, destination node in each agent hopping keeps an instance of the arrival mobile agents processing code. The following mobile agent, if the same information aggregation algorithm is required, carries only much short size executable script instead of large size full processing code. Simulation results show that EMA brings more than 10% percent energy saving compared to normal mobile agent method when the size ratio of full processing code and script is greater than 25, which shows that this EMA is particularly energy efficient when big size sophisticated information aggregation algorithm is implemented. Our method can also provide energy efficient flexible in-network processing for adaptive on-demand applications in WSNs.

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.