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Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks

机译:使用声学和图像混合无线多媒体传感器网络进行目标跟踪的轨迹预测

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摘要

Wireless multimedia sensor networks (WMSN), with self-organizing and high fault tolerant characteristics, have achieved great advantages in target tracking region. However, the capabilities of these tiny devices are limited by their battery power, storage capacity, computational ability and communication bandwidth. In this paper, hybrid wireless multimedia sensors networks composed of acoustic and image sensors are proposed for target tracking. When the target appears in the detection area, it may change the environment parameters nearby, so acoustic sensors are used to gather target signal firstly. Then, a target location method is executed based on the strength of the received acoustic signal. Furthermore, to achieve energy-efficient target tracking with high reliability and robust, image sensors are used as supplements to the acoustic sensors. This approach also reduces the power consumption communication burden of the whole networks. In order to decrease the number of active nodes, Gauss Markov mobility model is also adopted to predict the target trajectory and minimize the tracking region with considering of vehicular kinematics. Simulation results verify that, compared with other algorithms, our scheme can reduce the energy consumption and improve tracking accuracy.
机译:具有自组织性和高容错性的无线多媒体传感器网络(WMSN)在目标跟踪区域具有很大的优势。但是,这些微型设备的功能受到电池电量,存储容量,计算能力和通信带宽的限制。本文提出了一种由声和图像传感器组成的混合无线多媒体传感器网络,用于目标跟踪。当目标出现在检测区域中时,可能会改变附近的环境参数,因此首先使用声传感器来收集目标信号。然后,基于接收到的声信号的强度执行目标定位方法。此外,为了实现具有高可靠性和鲁棒性的节能目标跟踪,图像传感器被用作声传感器的补充。这种方法还减轻了整个网络的功耗通信负担。为了减少主动节点的数量,还考虑了运动学,采用高斯马尔可夫移动模型来预测目标轨迹并最小化跟踪区域。仿真结果表明,与其他算法相比,该方案可以降低能耗,提高跟踪精度。

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