压缩传感技术在多目标无线传感器网络中的无设备定位

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"LCS: 基于压缩传感的无设备本地化，用于传感器网络中的多个目标" 在无线传感器网络中，位置信息对于监控野生动物的行为和生态模式至关重要。考虑到野生动物的生活条件和环境，本地化技术需要满足特定的要求，如稀疏部署、无需设备、多目标定位以及可扩展性。这些需求构成了大规模稀疏部署约束下的无设备多目标定位问题。这篇研究论文“LCS：基于压缩传感的无设备本地化，用于传感器网络中的多个目标”提出了在稀疏部署场景下实现无设备定位方法的可能性。论文的主要观察是，对于任意一对节点，当目标位于不同位置时，接收到的信号强度（RSS，Received Signal Strength）会有所不同。这一现象为利用压缩传感理论进行无设备定位提供了基础。压缩传感是一种信号处理技术，它允许在远低于传统采样定理所需的采样率下捕获信号，并能恢复信号的原始信息。在传感器网络中，通过利用接收信号强度的变化，可以推断出目标相对于传感器节点的位置。在该论文中，作者Ju Wang, Dingyi Fang, Xiaojiang Chen, Zhe Yang, Tianzhang Xing 和 Lin Cai探讨了如何构建一个基于压缩传感的框架，以解决多个动态目标的无设备定位问题。他们可能提出了一种算法，该算法能够有效地估计目标的位置，同时考虑到网络中传感器节点的有限资源和计算能力。论文可能涉及以下几个关键点： 1. **信号模型**：建立一个描述RSS与目标位置关系的数学模型，这通常是通过统计学习或机器学习方法实现的。 2. **压缩感知理论应用**：介绍如何利用压缩感知的原理来减少采样数量，同时保持足够的定位精度。 3. **目标检测与跟踪**：讨论如何在动态环境中检测新出现的目标并持续跟踪其位置变化。 4. **算法设计**：描述为解决上述问题而设计的算法，包括数据采集、信号处理和位置估计步骤。 5. **性能分析与实验验证**：通过仿真或实地试验验证算法的性能，包括定位精度、计算复杂性和网络资源消耗等指标。这篇研究对无线传感器网络的无设备定位领域做出了贡献，为在实际环境中监测多个目标提供了新的思路和解决方案。通过压缩传感技术，可以克服传统定位方法的限制，实现高效且节省资源的定位服务。这不仅对野生动物监测有重要意义，也对其他需要无设备定位的领域，如智能家居、智能城市和物联网等，具有潜在的应用价值。

LCS: Compressive Sensing based Device-Free

Localization for Multiple Targets in Sensor Networks

Ju Wang

, Dingyi Fang

, Xiaojiang Chen

, Zhe Yang

, Tianzhang Xing

, Lin Cai

School of Information Science and Technology, Northwest University, Xi’an, China

University of Victoria, Victoria, BC, Canada

Email: wangju.nwu@foxmail.com

, {dyf, xjchen, XTZ}@nwu.edu.cn

, yangzhe2007@gmail.com

, cai@ece.uvic.ca

Abstract—Location information is important for monitoring and

finding out the behavioral and ecological patterns of wildlife.

According to the wildlife living condition and environment,

localization for wildlife must satisfy special requirements, such as

sparse deployment, device-free, multiple targets and scalability.

Those requirements are abstracted as a device-free multiple

target localization problem under the large-scale sparse

deployment constraint. In this paper, we present a sparse

deployment scenario under which device-free localization method

is feasible. The key observation is that given a pair of nodes, the

received signal strength (RSS) will be different when a target

locates at different locations. By comprehensive modeling and

analyzing the signal propagation properties, we are able to model

the interfered dynamic signals caused by the target as a function

of the location. Taking advantage of compressive sensing in

sparse recovery to handle the sparse property of the localization

problem, (i.e., the vector which contains the number and location

information of k targets is an ideal k-sparse signal), we propose a

scalable compressive sensing based multiple target counting and

localization method (LCS) and rigorously justify the validity of

the problem formulation. We have evaluated the performance of

LCS through extensive experiments in realistic sparse

deployment and large-scale simulations. The results illustrate the

effectiveness and advantages of the LCS method.

Keywords-sensor networks; compressive sensing; device-free

localization; sparse deployment

I. INTRODUCTION

With the expanding of human activity range, wildlife is

increasingly threatened. The key issue of protecting wildlife is

how to find out their behavioral and ecological patterns [1].

The lack of effective monitoring methods is one of the biggest

obstacles [2]. Fortunately, wireless sensor network (WSN)

shows great potential for zoologists to know more about the

behavioral and ecological patterns of wildlife. Many self-

organized WSN have been applied in the real wildlife

applications, e.g., observing the zebra’s daily activities [3],

monitoring the swallow’s micro-environment [4] and finding

the European Badgers’ living habits

[5].

One of the most important information for wildlife is the

location information. However the following four challenges

exist in the wildlife localization:

1) Sparse deployment: A basic requirement for monitoring

wildlife is that the artificial equipments do not disturb the wild

environment and the living habits of wildlife, thus one should

use as less devices as possible, i.e., sparse deployment. Sparse

deployment also benefits for reducing cost, collision space, etc.

2) Device-free localization (DFL): Many localization

methods are device-based, i.e., targets need to carry some

devices (e.g., RFID tag or GPS). But it is hard to attach such

devices on some animal body and zoologists do not favor to

do so. Thus the device-free localization method (i.e., targets

do not

carry any devices) is urgent for monitoring wildlife.

3) Multiple target counting and localization: Compared

with single wildlife’s location information, population

statistical number and location information deeply attract the

interest of zoologists since it is important for zoologists to find

out changes in population size and migration trajectory. Thus

the multiple target counting and localization method is desired

for monitoring wildlife population.

4) Scalability: Wildlife, such as the golden monkey and

zebra, live in the wild environment, and move around large-

scale areas. However, most of recent localization approaches

are small-scale localization method [7]-[14] due to the specific

application environment and the restriction of transmission

range. Thus a scalable localization method is needed.

One of the key problems arises: how to count and locate

multiple targets using a DFL method under a large sparse

deployment area?

In this paper, LCS, a compressive sensing based device-

free multiple target counting and localization method in sensor

networks has been proposed. In order to achieve high accuracy,

most of recent localization approaches are dense deployment

[10] [11]. In this work, we present a sparse deployment

scenario under which LCS can also achieve high localization

accuracy. One of the most popular DFL approaches is the RSS

based DFL method since it does not need additional devices for

WSN. The RSS interference caused by targets is essential for

the RSS based DFL method [9]-[16], so finding a practical

model that can be applied in the real world for DFL is still an

open issue. We give an RSS measurement model which can

accurately estimate RSS when a target is at different locations

of a particular link. According to the model, we have

successfully extracted the RSS dynamic measurements caused

by the target. In this paper, we consider target locations as

sparse signal and propose to reconstruct the signal using the

compressive sensing (CS) [6]. Here we assume that the targets

are sparse compared with the number of grids utilized to

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