RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

An important requirement for many novel location based services, is to determine the locations of people, equipment, animals, etc. The accuracy and response time of estimation are critical issues in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed semi-supervised location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between Received Signal Strength fingerprint and their respective location in a subsystem. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. On each subsystem at first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed Distributed Semi-Supervised Locally Linear Embedding scheme has the advantage of robustness, scalability, useful in large site application and is easy in training and implementation. We have compared our results with Distributed Subtract on Negative Add on Positive (DSNAP) and benchmark method RADAR. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site as well as with DSNAP and benchmark method RADAR.     

A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network

In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also the coordinates of the anchor points (APs). The localization accuracy depends on the errors in the distance estimation with the RSS measurements and the size of the polygon composed of the APs used for the lateration. Since errors are inevitably involved in the RSS measurement, we focus on reducing the size of the polygon to increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon composed of the APs hearing the target. Once the relative position is estimated, only the APs covering the area are used for localization. We implement the localization method and evaluate the accuracy of the proposed method in various radio propagation environments. The experimental results show that the proposed method improves the localization accuracy and is robust against the dynamically changing radio propagation environments over time.     

Least square identification of alias components of linear periodically time-varying systems and optimal training signal design

A least-squares (LS) method for identifying alias components of discrete linear periodically time-varying (LPTV) systems is proposed.The authors apply a periodic input signal to a finite impulse response (FIR)--LPTV system and measure the noise-contaminated output.The output of this LPTV system has the same period as the input when the period of the input signal is amultiple of the period of the LPTV system.The authors show that the input and the output can be related by using the discrete Fourier transform. In the frequency domain, an LS method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR--LPTV systems.The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)--LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.     

基于朗伯模型参数估计的VLC室内定位优化算法

为实现高精度室内定位,本文设计了一种可见光 通信(VLC)室内定位系统,并通过 结合优化的朗伯模型、码分多址技术(CDMA)、三边定位算法而有效提升了定位精度和系统 扩展性。首先,每个发光二极管(LED)的ID信息经过直接序列调制后加载到LED驱动电路上 ,LED发出带有自身ID信息的灯光信号。在接收端通过光电探测器(PD)接收灯光信号,并 根据扩频码的正交性恢复出ID信息及接收信号强度(RSS),以此提高信道容量并增强系统 抗干扰能力。然后,根据朗伯光源模型,由三边定位算法得出待定位点的定位估计坐标。为 进一步提高精度,引入k最近邻(KNN)思想,采集适当的指纹点并由指纹点信息对每盏灯在 定位估计坐标处的朗伯光源模型参数进行估计,由优化后的朗伯模型计算出精度更高的定位 坐标。在1m×1m×1.35 m的空间区域中,进行本VLC室内定位系统 的实验测试。结果表明,提 出的高精度VLC室内定位系统的平均定位误差降低至2cm左右,其定位精度相比于传统三边 定 位算法提升了30%。此外,该系统方案所采用基于指纹点信息优化朗 伯模型参数的方法具备良好的实用扩展性,可实现广阔的应用场景。     

An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy

In recent years, the indoor positioning technologies have been recognized as core technologies for realizing smart space, a ubiquitous society, context awareness, and various location-based services. There are several approaches for positioning with radio signals, but the received signal strength (RSS)-based technology is considered a promising scheme because of its simplicity and practicality in implementation. In this paper, the positioning performance of the RSS value-based scheme is analyzed with respect to the location of access points (APs) and the number of APs in an indoor environment. An adaptive AP selection scheme and a base AP changing scheme are then proposed to enhance the positioning accuracy. In order to estimate the RSS characteristics, RSS values are measured as the distance between the AP and the receiver increases. The positioning performance is evaluated with differing AP numbers, which form a triangle or a quadrilateral. The performance of the proposed schemes is evaluated via experiments using wireless local area network APs. Results show that the performance of proposed schemes is enhanced compared to that of conventional scheme.     

Deployment of RSS-Based Indoor Positioning Systems

Location estimation based on Received Signal Strength (RSS) is the prevalent method in indoor positioning. For such positioning systems, a massive collection of training samples is needed for their calibration. The accuracy of these methods is directly related to the placement of the reference points and the radio map used to compute the device location. Traditionally, deploying the reference points and building the radio map require human intervention and are extremely time-consuming. In this paper we present an approach to reduce the manual calibration efforts needed to deploy an RSS-based localization system, both when using only one RF technology or when using a combination of RF technologies. It is an automatic approach both to build a radio map in a given workspace by means of a signal propagation model, and to assess the system calibration that best fits the required accuracy by using a multi-objective genetic algorithm.     

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.     

基于连续聚类算法的5G站址敏捷规划方案

目前5G站址规划方法主要基于人工规划或者简单的密度聚类算法输出,人工规划方法虽然准确度较高,但是需投入大量人力资源,较大程度依赖规划设计人员的经验和学识等,耗时长且过程繁琐.因此人工规划方法只能适用于小范围(补点)的站址规划,无法适用于5G大范围及全网的站址规划.针对上述问题,本文提出一种基于栅格密度的连续聚类算法,通...     

Improved RSS Data Generation Method Based on Kriging Interpolation Algorithm

The traditional method of constructing RSS fingerprint database costs a large amount of time and human resource due to adopting the point-by-point method to sample RSS value, and consequently the positioning method based on RSS fingerprint model is difficult to be widely applied. In this paper, a RSS data generation method is proposed based on Kriging spatial interpolation algorithm. The proposed method firstly selects the model of variogram according to the properties of field, and subsequently solves the variogram by using the observation points with the restriction of unbiased estimation and minimum estimation variance, finally calculates RSS data for the estimation points. The experimental results show that the proposed method accurately acquires the RSS data of estimation points while the required reference points are much less than that of conventional point-by-point method.

     

RSS协助的ray-tracing室内定位算法

针对室内定位,当信号受到非视距(non-line-of-sight, NLOS)和多径传播的影响时,本文提出一种接收信号强度(Received Signal Strength, RSS)协助的Ray-tracing室内定位算法,改进已经提出的基于虚拟基站方法的信号到达时间 (Time of Arrival, TOA)和信号到达角度(Direction of Arrival, DOA)室内无线信号Ray-tracing模型,利用信号RSS测量值优化算法,实现TOA、DOA和RSS协同定位,提高室内多径及非视距环境下,无线定位的精度,降低算法复杂度,提高算法处理信号多重散射的能力并降低了对基站的依赖性适用环境更为广泛。首先通过RSS得到信号源可能存在的位置,随后利用Ray-tracing原理并使用虚拟基站,将非视距路径定位问题转化为视距路径定位问题,利用TOA和DOA对直射、透射、反射和绕射情况进行分析建模,最后使用最小二乘法对可能的位置进行筛选,得到信号源的最终位置。仿真结果表明,本算法较改进前拥有更高的定位精度。      

On the accuracy of a cellular location system based on RSS measurements

We discuss the performance of a cellular location system based on received signal strength (RSS) measurements. Each mobile station (MS) collects RSS measurements of the downlink control channels transmitted by the surrounding base stations. It is assumed that there is one-to-one mapping between the RSS and the MS location. Hence, these measurements can be used to obtain the MS location. We examine the accuracy of this method by deriving the Cramer-Rao bound, the concentration ellipse, and the circular error probability (CEP) of this method. In addition, we obtain an analytic expression that predicts the point at which accuracy deviates significantly from the bound (the threshold point). The accuracy of the method does not meet the FCC E911 requirement, but it is an attractive solution for less-demanding location-based services.     

Multitone Feedback Through Demodulating Log Detector for Detection of Spurious Emissions in Software Radio

This paper provides an analysis of a log detector in order to determine its response to a multitone input for detection of spurious emissions in a radio frequency transmitter. Treatment is given to the single tone response of the log detector and extended to a two-tone log detector system, where a large signal and a small signal are present. The large signal is observed to experience logarithmic processing with an output at zero frequency. The small signal produces an output at the difference frequency of the large signal frequency and small signal frequency that is approximately proportional to the ratio of the small signal voltage to the large signal voltage. The two-tone results are generalized to an m-tone input. Experimental results are presented to show the accuracy of the model. The log detector circuit analyzed is able to detect a spurious emission within 45 MHz of the main signal with plusmn1 dB of accuracy.     

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.     

The Cramer-Rao bounds of hybrid TOA/RSS and TDOA/RSS location estimation schemes

The Cramer-Rao bound (CRB) on location estimation accuracy of two different hybrid schemes, time of arrival/received signal strength (TOA/RSS) and time difference of arrival/received signal strength (TDOA/RSS), is computed. For short-range networks, the hybrid schemes offer improved accuracy with respect to conventional TOA and TDOA schemes, particularly in the proximity of the reference devices.     

基于遗传算法优化BP神经网络的可见光定位

采用接收信号强度(RSS)方法的室内可见光定位 ,因受多径效应及噪声的影响,对距离估计不准确, 定位精度不高。为提高定位精度,本文提出了一种采用遗传算法优化BP神经网络(GA-BP) 的距离估计方法。 先通过遗传算法优化BP神经网络的初始权值,经过优化后的BP神经网络收敛速度快,不易 限于局部最优。 再利用GA-BP神经网络对收发端之间的距离进行修正,使其接近于真实距离。最后使用最 小二乘法解算待 定位点坐标,同时在不同定位范围和不同定位位置下,与传统RSS加权质心方法的可见光定 位结果进行对 比。仿真结果表明,在5m×5m×3m的定位场景中,平均定位误差可以达到0.642 cm。与传统RSS加权质 心方法相比,平均定位精度提高了约96.4%。且在不同定位范围和不 同定位位置下,平均定位误差稳定在 毫米级,尤其不随定位范围的扩大而扩大。有效地提高了室内定位精度和系统应用的普适性 。     

基于WiFi的自适应匹配预处理WKNN算法

针对基于接收信号强度（Received Signal Strength,RSS）的WiFi室内定位技术中,传统加权K邻近（Weighted K-nearest Neighbor,WKNN）算法不能自适应获取WLAN中有效接入点（Acess Point,AP）且参考点匹配准确度不高的问题,本文提出了自适应匹配预处理WKNN算法。该算法中每个实时定位点自适应地根据网络状况对AP的RSS均值由大到小排序,然后选择RSS均值较大的前M个AP,与参考点中对应的M个AP一起参与匹配预处理计算,从而优化了传统的指纹定位算法。同时将室内定位和室内地图相结合,使参考点和定位结果直观地展示在地图上,并通过使用地图数据大幅度简化了离线训练过程。此外,本文设计并实现了基于Android平台的室内定位系统,通过该系统验证了本文所提算法在单点定位和移动定位中的有效性。实验结果表明,该算法可获得30%以上的定位误差改善,有效提高了定位精度和定位稳定性。      

PN sequence blind estimation in multiuser DS‐CDMA systems with multipath channels based on successive subspace scheme

In this paper, we proposed a new method based on expanding subspace algorithm and finite alphabet characteristics, for blind estimation of the users' spreading sequences in the multiuser direct sequence code division multiple access system in the presence of the multipath channels. In the proposed scheme, we show that the estimation of the users' overall channels in the direct sequence code division multiple access system is equivalent to the impulse response estimation of the multi‐input multi‐output finite impulse response channels. Our proposed approach is based on the successive estimation of the columns of the equivalent multi‐input multi‐output finite impulse response channels from the lowest degree columns to the highest degree ones. Accordingly, each user's overall channel that is the convolution of the original multipath channel and the spreading sequence is estimated. Then we extract PN sequences from the overall channel using finite alphabet characteristics of the spreading sequence chips for each user. According to simulation results, our proposed scheme outperforms the conventional methods in that it does not require symbol synchronization and does not have channel constraints (for example, AWGN and single user system) in the multipath channels.     

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.     

基于TDOA的网络化定位系统自适应站点优选算法

网络化无源多点时差定位系统具有部署灵活、易于扩展及无电磁辐射等特点,特别适合航天发射场等大规模场所的部署,实现对低空机动目标的定位。但当网络站点数量较多时,对所有站点进行信号采集传输将造成大量能量及网络资源的浪费。针对不同站点数量和站点位置对定位精度产生的影响,结合当前各站点无人机信号识别结果,提出了一种自适应站点优选算法,该算法基于Cramer-Rao界均值最小化原则,利用K均值聚类算法动态调整当前目标定位空间,可在密集部署的传感器站点中快速选择出符合定位要求的站点集。仿真结果表明,提出的自适应站点优选算法可有效提高网络定位精度。     

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of location-based services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, wireless local area network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods