目录
RSSI
LOS versus NLOS
Positioning, Mobility, and Tracking
Network Localization
RSSI
received signal strength indicator ( RSSI )
Similar to the TOA, in RSSI, multiple base nodes collaborate to localize a target node via triangulation (see Fig. 1.2 a). However, instead of measuring the TOA at base nodes, the estimation is carried out using the received signal strength (RSS) [3] . In this method, the strength of the received signal indicates the distance traveled by the signal. Assuming that the transmission strength and channel (or environment in which the signal is traveling) characteristics are known, for a coplanar case, three base nodes and three RSS measurements are required.
与TOA类似，在RSSI中，多个基节点通过三角测量协作来定位目标节点（参见图1.2a）。 然而，不是在基节点处测量TOA，而是使用接收信号强度（RSS）[3]来执行估计。 在该方法中，接收信号的强度表示信号行进的距离。 假设传输强度和信道（或信号传播的环境）特性是已知的，对于共面情况，需要三个基本节点和三个RSS测量。
LOS versus NLOS
line - of - sight ( LOS ) versus non - line - of - sight ( NLOS )
视距（LOS）与非视距（NLOS）
Compared with RSSI, the performance characteristics of TOA, DOA, and TDOA techniques are very sensitive to the availability of LOS [8 – 10] ; that is, in NLOS situations, the computed TOA, DOA, and TDOA are subject to considerable error.
However, the performance of the RSSI technique is altered only mildly by the lack of LOS: NLOS leads to a shadowing (random) effect in the power – distance relationship, which can be reduced using filtering techniques. Thus, many NLOS identification, mitigation, and localization techniques have been designed. Part IV of this handbook introduces the details of these techniques.
与RSSI相比，TOA，DOA和TDOA技术的性能特征对LOS的可用性非常敏感[8-10]; 也就是说，在NLOS情况下，计算出的TOA，DOA和TDOA会受到相当大的误差。
然而，由于缺乏LOS，RSSI技术的性能仅略微改变：NLOS导致功率 - 距离关系中的阴影（随机）效应，这可以使用滤波技术来减少。 因此，已经设计了许多NLOS识别，缓解和定位技术。
Positioning, Mobility, and Tracking
定位，移动和跟踪
The difficulty in achieving highly precise location estimates in many indoor and outdoor wireless environments has led a number of investigators to utilize parameter estimation techniques for positioning and tracking mobile targets. These techniques can be very beneficial, for example, in smoothing position tracks in mixed LOS/ NLOS situations. Kalman, Bayesian, or particle fi lters are widely used as state estimators. These state estimation methods can be applied with a variety of sensor technologies and positioning algorithms to improve positioning and tracking performance in many real - world environments.
在许多室内和室外无线环境中实现高精度位置估计的困难导致许多研究者利用参数估计技术来定位和跟踪移动目标。 这些技术非常有用，例如，在混合LOS / NLOS情况下平滑位置轨迹。 卡尔曼，贝叶斯或粒子滤波器被广泛用作状态估计器。 这些状态估计方法可以应用于各种传感器技术和定位算法，以改善许多真实环境中的定位和跟踪性能。
Part V of this handbook begins with a discussion of positioning as a state estimation problem and then discusses Kalman filtering and closely related techniques applicable in both indoor and outdoor applications.
Network Localization
Applications and services built upon wireless positioning can be implemented with different forms of infrastructure supporting the positioning function. GPS satellites, cellular base stations, and fixed wireless local area network (WLAN) access points are familiar infrastructures underlying many well - known applications and services, but for some applications, they cannot be provided for various economic and technical reasons. For some applications, there is no supporting infrastructure at all, and methods must be devised to implement location - based services without infrastructure. In other cases, fixed infrastructure cannot provide a complete solution, and this has led to the development of network - based localization techniques.
基于无线定位的应用和服务可以用支持定位功能的不同形式的基础设施来实现。 GPS卫星，蜂窝基站和固定无线局域网（WLAN）接入点是许多众所周知的应用和服务的常见基础设施，但是对于某些应用，由于各种经济和技术原因而无法提供它们。 对于某些应用程序，根本没有支持基础架构，必须设计方法来实现没有基础架构的基于位置的服务。 在其他情况下，固定基础设施无法提供完整的解决方案，这导致了基于网络的本地化技术的发展。
An important example of an application for wireless positioning systems is a wireless sensor network, comprising a number of geographically distributed autonomous sensors intended to cooperatively monitor some characteristics of their individual environments. Each sensor node is typically equipped with its application - specific sensors, a wireless transceiver, a microcontroller, and a power source, usually a battery. Accurate positioning information for each sensor is essential for support of the network ’ s application. Ideally, each sensor would have accurate knowledge of its own position, for example, from GPS. However, size and cost constraints lead, in turn, to constraints on power and computational capabilities in the individual sensor nodes.
无线定位系统的应用的一个重要示例是无线传感器网络，其包括多个地理上分布的自主传感器，旨在协作地监视其各个环境的某些特征。 每个传感器节点通常配备有特定应用的传感器，无线收发器，微控制器和电源，通常是电池。 每个传感器的准确定位信息对于支持网络应用至关重要。 理想情况下，每个传感器都可以准确了解自己的位置，例如GPS。 然而，尺寸和成本限制又导致对各个传感器节点中的功率和计算能力的约束。
Because of these constraints, a sensor network will typically be deployed with a small number of nodes, called anchor or reference nodes, having precise a priori location information, while a larger number of remaining nodes, called unlocalized nodes, will have no prior knowledge of their locations.
由于这些限制，传感器网络通常将部署有少量节点，称为锚点或参考节点，具有精确的先验位置信息，而大量剩余节点（称为未定位节点）将不具有它们位置的先验知识。
An unlocalized node, due to power limitations or signal blockage, may not be able to communicate with anchor nodes. Thus, the unlocalized nodes will estimate their locations by communicating with each other, and schemes must be used to propagate the location information throughout the network. Techniques for accomplishing this are known as collaborative position location , cooperative localization , and network localization .
由于功率限制或信号阻塞，未定位节点可能无法与锚节点通信。 因此，未定位的节点将通过彼此通信来估计它们的位置，并且必须使用方案来在整个网络中传播位置信息。 用于实现此目的的技术被称为协作位置定位，协作定位和网络定位。
Part VI of this handbook begins with a chapter on infrastructure - free tracking and then discusses several approaches to network localization.