Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments

Scanning laser range sensors provide range data consisting of a set of point measurements. The laser sensor URG-04LX has a distance range of approximately 0.02–4 m and a scanning angle range of 240°. Usually, such an image range is acquired from one viewpoint by “moving” the laser beam using rotating mirrors/prisms. The orientation of the laser beam can easily be measured and converted into the coordinates of the image. This article conducts localization using virtual labels with data about distances in the environment obtained from 2D distance laser sensors. This method puts virtual labels on special features and points which are along the mobile robot’s path. The current location is calculated by combining the virtual label and the range image of the laser range finder.     

Multi-sensor data fusion methods for indoor localization under collinear ambiguity

Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer–Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR.     

撒布型无线传感器网络节点定位算法

针对撒布型无线传感器网络提出了基于非度量多维标度的NMDS-MAP算法及NMDS-MAP(P)算法,两种方法采用TDOA等测距技术测量节点间距,利用非度量多维标度技术对未知节点进行定位,前者是集中式算法,后者是分布式算法。理论分析与仿真实验表明,两种算法具有较高的定位精度与健壮性。     

基于Cricket传感器网络室内定位系统的设计与实现

在高精度传感器网络室内定位系统中,基于到达时间差的定位系统得到了越来越普遍的研究。以Cricket传感器为载体,根据射频和超声波信号的传输特性以及信标布局的特点设计了一种改进的通信机制,不但提高了传感器网络通信质量的而且也降低了传感器节点的能量消耗。并提出了一种与传感器工作机制相关且误差限制在1cm以内的计算距离的方法;最后根据信标节点与接收器之间的几何关系,实现了满足室内环境下接收器移动性需要的位置计算算法。     

A swarm-based approach to real-time 3D indoor localization: Experimental performance analysis

In this paper, the problem of indoor localization in wireless networks is addressed relying on a swarm-based approach. We assume to know the positions of a few number of sensor nodes, denoted as anchor nodes (ANs), and we aim at finding the position of a target node (TN) on the basis of the estimated distances between each AN and the considered TN. Since ultra wide band (UWB) technology is particularly suited for localization purposes (owing to its remarkable time resolution), we consider a network composed of UWB devices. More precisely, we carry out an experimental investigation using the PulsOn 410 ranging and communication modules (RCMs) produced by time domain. Using four of them as ANs and one of them as TN, various topologies are considered in order to evaluate the accuracy of the proposed swarm-based localization approach, which relies on the pairwise (AN-TN) distances estimated by the RCMs. Then, we investigate how the accuracy of the proposed localization algorithm changes if we apply to the distance estimates a recently proposed stochastic correction, which is designed to reduce the distance estimation error. Our experimental results show that a good accuracy is obtained in all the considered scenarios, especially when applying the proposed swarm-based localization algorithm to the stochastically corrected distances. The obtained results are satisfying also in terms of software execution time, making the proposed approach applicable to real-time dynamic localization problems.     

High accuracy localization method using AoA in sensor networks

In sensor networks, several applications such as habitat monitoring and moving objects tracking, require the knowledge of nodes positions. Position estimation most often includes errors due to the measurements of distance and incoming angles between neighbors. Erroneous positions are propagated from a node to other nodes exacerbating the degree of errors in the estimation of the positions of these nodes. In this paper, we propose a new localization method, called HA-A2L (High Accuracy localization based on Angle to Landmark); it consists of (a) a new protocol that allows nodes to exchange information pertinent to the localization process; and (b) a localization algorithm that uses estimation of distances and incoming angles to locate nodes in sensors networks. Compared, via simulations, to previous methods, such as APS and A2L, HA-A2L considerably increases the number of located nodes with far better accuracy.     

Multi-target indoor localization and tracking on video monitoring system in a wireless sensor network

The development of wireless sensor networks (WSNs) has greatly encouraged the use of sensors for multi-target tracking. The high efficiency detection and location monitoring are critical requirements for multi-target tracking in a WSN. In this paper, we present an indoor tracking model using IEEE 802.15.4 compliant radio frequency and video monitoring system to monitor targets in a special way. Our motivation is to manipulate the erratic or unstable received signal strength indicator (RSSI) signals to deliver the stable and precise position information in the indoor environment. We propose a localization algorithm based on statistical uncorrelated vectors and develop a smoothing algorithm to minimize the noise in RSSI values. We also present a solution combining the WSN with the Ethernet technology to decrease the RSSI interference by buildings. The developed system can realize the functions of multi-target detection and tracking, and specific target inquiries, alarms and monitoring. The system architecture, hardware and software organization, as well as the solutions for multiple targets tracking, RSSI interference and localization accuracy have been introduced in details.     

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.     

Path loss exponent estimation for wireless sensor network localization

The wireless received signal strength (RSS) based localization techniques have attracted significant research interest for their simplicity. The RSS based localization techniques can be divided into two categories: the distance estimation based and the RSS profiling based techniques. The path loss exponent (PLE) is a key parameter in the distance estimation based localization algorithms, where distance is estimated from the RSS. The PLE measures the rate at which the RSS decreases with distance, and its value depends on the specific propagation environment. Existing techniques on PLE estimation rely on both RSS measurements and distance measurements in the same environment to calibrate the PLE. However, distance measurements can be difficult and expensive to obtain in some environments. In this paper we propose several techniques for online calibration of the PLE in wireless sensor networks without relying on distance measurements. We demonstrate that it is possible to estimate the PLE using only power measurements and the geometric constraints associated with planarity in a wireless sensor network. This may have a significant impact on distance-based wireless sensor network localization.     

Sensor virtualization for underwater event detection

Distributed event detection is a popular application in Underwater Wireless Sensor Networks (UWSNs). The Base Station (BS) collects the measurements from multiple sensor nodes, and makes a decision based on the sensors’ reports. However, due to the unpredictable moving of underwater sensor nodes and interference among multiple events, it is difficult to guarantee the accuracy of event detection. In this paper, we propose a sensor virtualization approach to deal with the event detection problem in UWSNs. The final decision making at the BS will be implemented with the reports of multiple virtual sensors. Although the events may happen in a large scale, the locations where the events happen are relatively sparse in the underwater environment. Consider the sparse property of events, we employ the technique of compressive sensing to recover the original signal from the correlated sensors’ measurements. Through a proper signal reconstruction, the accurate event detection can be reached with a remarkable low sensing overhead. We implement the sensor virtualization based on the compressive sensing technique. Our approach is suitable for the high dynamic topology of UWSN, and it can improve the accuracy of event detection and reduce energy consumption in UWSNs.