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A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

机译：在通信受限的水下声传感器网络中基于支持向量学习的粒子滤波方案用于目标定位

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Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.
机译：目标定位旨在估计未知目标的位置，是水下声传感器网络（UASN）应用中的关键问题之一。但是，水下环境的受限属性（例如，传感器节点的通信能力受限和感测噪声）使目标定位成为一个具有挑战性的问题。本文基于分数传感器节点，针对通信受限的水声传感器网络中的定位问题，提出了一种基于支持向量学习的粒子滤波算法。利用节点选择策略来选择具有短距离模式的分数传感器节点，以在每个时间帧参与传感过程。随后，我们提出了一种基于最小二乘支持向量回归（LSSVR）的观测函数，通过迭代回归策略处理感测噪声引起的数据失真，提高了观测精度。同时，我们将观察结果进行整合以制定似然函数，从而有效地更新粒子的权重。因此，提高了粒子有效性，避免了“粒子退化”问题，并提高了定位精度。为了验证所提出的定位算法的性能，研究了两种不同的噪声情况。仿真结果表明，所提出的定位算法可以有效地提高定位精度。另外，节点选择策略可以有效地选择传感器节点的子集，以提高传感器网络的通信效率。

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