传感器网络中基于压缩感知的压缩域目标跟踪算法研究与应用

复杂环境下传感器网络目标跟踪,存在跟踪准确性与算法复杂性这对矛盾,需要考虑准确性、通信和计算能耗之间的折中。针对此问题,研究传感器网络中基于压缩感知的压缩域目标跟踪和压缩域数据融合,并提出了用稀疏的测量矩阵对haarlike特征压缩,压缩域特征送入朴素贝叶斯分类器,在压缩域直接实现目标跟踪的算法。然后通过配置传感器网络以生成多个层次类型不同的簇结构,压缩后的数据在网络中传输,并在各层簇头实现压缩域下的数据融合。该算法通过稀疏测量矩阵压缩表征原始图像信息和分类器的自我学习更新,提高了对压缩域目标特征分类的准确性,在复杂环境下有更好的鲁棒性。而压缩域直接进行目标跟踪,不需要重构图像,也减少了网络运算量和数据传输量。通过仿真实验和标准数据库测试对比以及在机器人足球赛实验平台中的应用表明,该算法在跟踪准确性,数据传输量及传输能耗上均有优势。

一种基于预测的目标跟踪传感器网络数据近似存储查询策略

节能问题是传感器网络设计的一个关键问题,在对目标跟踪传感器网络中的目标位置信息进行存储和查询处理时,也必须通过较低的通信代价来降低消息复杂度,从而达到节能的目的。分析了现存的一些数据存储和数据分发的方法,针对EASE方法查询误差较大的问题,提出了基于预测的近似查询的存储策略(Prediction-based Energyconserving Approximate StoragE,P-EASE),并就EASE方法查询未考虑选择恰当的存储节点的问题,提出了一种基于地理位置的择优查询(Optimal Query)策略。使用半随机步行移动模型和随机路点移动模型进行了仿真实验,比较了P-EASE与EASE算法的消息量、消息复杂度、平均消息复杂度和查询误差。仿真实验证明,P-EASE比EASE查询更精确、能量更高效。

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

Prediction-based energy-efficient target tracking protocol in wireless sensor networks

A prediction based energy-efficient target tracking protocol in wireless sensor networks(PET) was proposed for tracking a mobile target in terms of sensing and communication energy consumption.In order to maximize the lifetime of a wireless sensor network(WSN),the volume of messages and the time for neighbor discovery operations were minimized.The target was followed in a special region known as a face obtained by planarization technique in face-aware routing.An election process was conducted to choose a minimal number of appropriate sensors that are the nearest to the target and a wakeup strategy was proposed to wakeup the appropriate sensors in advance to track the target.In addition,a tracking algorithm to track a target step by step was introduced.Performance analysis and simulation results show that the proposed protocol efficiently tracks a target in WSNs and outperforms some existing protocols of target tracking with energy saving under certain ideal situations.

LOCALIZATION FOR MOBILE TARGET IN WIRELESS SENSOR NETWORKS

A precise localization for mobile target in wireless sensor networks is presented in this letter,where a geometrical relationship is explored to improve the location estimation for mobile target,in-stead of a simple centroid approach.The equations of location compensation algorithm for mobiletarget are derived based on linear trajectory prediction and sensor selective activation.The resultsbased on extensive simulation experiments show that the compensation algorithm gets better per-formance in metrics of quality of tracking and energy efficiency with the change of sensor sensing range,the ratio of sensing range and sensor activation range,and the data sampling rate than traditionalmethods,which means our proposing can achieve better quality-energy tradeoff for mobile target inwireless sensor networks.

OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.