A statistical modeling approach to location estimation

Some location estimation methods, such as the GPS satellite navigation system, require nonstandard features either in the mobile terminal or the network. Solutions based on generic technologies not intended for location estimation purposes, such as the cell-ID method in GSM/GPRS cellular networks, are usually problematic due to their inadequate location estimation accuracy. In order to enable accurate location estimation when only inaccurate measurements are available, we present an approach to location estimation that is different from the prevailing geometric one. We call our approach the statistical modeling approach. As an example application of the proposed statistical modeling framework, we present a location estimation method based on a statistical signal power model. We also present encouraging empirical results from simulated experiments supported by real-world field tests.     

Location Estimation via Support Vector Regression

Location estimation using the global system for mobile communication (GSM) is an emerging application that infers the location of the mobile receiver from multiple signals measurements. While geometrical and signal propagation models have been deployed to tackle this estimation problem, the terrain factors and power fluctuations have confined the accuracy of such estimation. Using support vector regression, we investigate the missing value location estimation problem by providing theoretical and empirical analysis on existing and novel kernels. A novel synthetic experiment is designed to compare the performances of different location estimation approaches. The proposed support vector regression approach shows promising performances, especially in terrains with local variations in environmental factors     

Joint mobility tracking and handoff in cellular networks via sequential Monte Carlo filtering

We consider the application of sequential Monte Carlo (SMC) methodology to the problem of joint mobility tracking and handoff detection in cellular wireless communication networks. Both mobility tracking and handoff detection are based on the measurements of pilot signal strengths from certain base stations. The dynamics of the system under consideration are described by a nonlinear state-space model. Mobility tracking involves an online estimation of the location and velocity of the mobile, whereas handoff detection involves an online prediction of the pilot signal strength at some future time instants. The optimal solutions to both problems are prohibitively complex due to the nonlinear nature of the system. The SMC methods are therefore employed to track the probabilistic dynamics of the system and to make the corresponding estimates and predictions. Both hard handoff and soft handoff are considered and three novel locally optimal (LO) handoff schemes are developed based on different criteria. It is seen that under the SMC framework, optimal mobility tracking and handoff detection can be implemented naturally in a joint fashion, and significant improvement is achieved over existing methods, in terms of both the tracking accuracy and the trade-off between service quality and resource utilization during handoff.     

Data fusion of power and time measurements for mobile terminal location

The location of mobile terminals in cellular networks is an important problem with applications in resource allocation, location sensitive browsing, and emergency communications. Finding cost effective location estimation techniques that are robust to non-line of sight (NLOS) propagation, quantization, and measurement noise is a key problem in this area. Quantized time difference of arrival (TDoA) and received signal strength (RSS) measurements can be made simultaneously by CDMA cellular networks at low cost. The different sources of errors for each measurement type cause RSS and TDoA measurements to contain independent information about mobile terminal location. This paper applies data fusion to combine the information of RSS and TDoA measurements to calculate a superior location estimate. Nonparametric estimation methods, that are robust to variations of measurement noise and quantization, are employed to calculate the location estimates. It is shown how the data fusion location estimators are robust, provide lower error than the estimators based on the individual measurements, and have low implementation cost.     

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.     

Locating GSM mobiles using antenna array

The authors consider the problem of accurate estimation of the position of a GSM mobile station based on measurements of the mobile signal made at a single receiving site equipped with an antenna array. Direction of arrival and time of arrival measurements are used to estimate the location of the mobile transmitter. A statistical filtering technique is applied to the location estimates to minimise the effects of multipath fading. Experimental results with a DCS-1800 system are presented which achieve an r.m.s. location error of 133 m, with the error being <250 m for 98% of the time     

Reducing the Calibration Effort for Probabilistic Indoor Location Estimation

WLAN location estimation based on 802.11 signal strength is becoming increasingly prevalent in today's pervasive computing applications. Among the well-established location determination approaches, probabilistic techniques show good performance and, thus, become increasingly popular. For these techniques to achieve a high level of accuracy, however, a large number of training samples are usually required for calibration, which incurs a great amount of offline manual effort. In this paper, we aim to solve the problem by reducing both the sampling time and the number of locations sampled in constructing a radio map. We propose a novel learning algorithm that builds location-estimation systems based on a small fraction of the calibration data that traditional techniques require and a collection of user traces that can be cheaply obtained. When the number of sampled locations is reduced, an interpolation method is developed to effectively patch a radio map. Extensive experiments show that our proposed methods are effective in reducing the calibration effort. In particular, unlabeled user traces can be used to compensate for the effects of reducing the calibration effort and can even improve the system performance. Consequently, manual effort can be reduced substantially while a high level of accuracy is still achieved     

基于最大置信度的多目标检测算法

针对混合运动模式下目标数量及目标运动速度范围等多项先验信息缺乏状况下的复杂航迹起始问题,提出一种基于最大置信度的多目标检测算法。该算法借鉴动态规划技术中的能量积累思想,并充分利用了传感器信号强度信息。在粗起始阶段利用扩展搜索算法生成候选航迹,并利用模型粗匹配的方法将候选航迹大致分为直线运动及曲线运动两种类型。在航迹确认阶段,采用基于信号强度信息的概率多假设跟踪算法,通过计算最优状态估计值获得量测点属于某一目标的最大置信度,并依据最大置信度确认目标量测。仿真实验结果表明,该方法实时性强,不仅能对多目标航迹准确起始,也可以有效避免概率多假设跟踪算法由于初值质量差而导致的错误跟踪现象。      

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.     

A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation

In this paper, a new methodology for extracting motion patterns is applied to optical flow estimation in the presence of multiple motions. The proposed approximation deals with the problem in two stages. In the first one, the most important motions are segmented; in the second one, the optical flow is estimated on the basis of the motions detected in the previous stage. To extract relevant motions, a new approach based on a spatio-temporal filtering is presented. The approach groups together parts of a moving object that have been separated into various filter responses because of the object's spatial structure, thereby avoiding the spatial dependency problem associated with a representation based on spatio-temporal filters. The proposed model, therefore, generates one "motion pattern" for each motion detected in the sequence. To obtain an optical flow estimation, which is able to represent multiple velocities, the gradient constraint is applied to the output of each filter so that multiple estimations of the velocity at the same location may be obtained. For each "motion pattern" detected in the previous stage, the velocities at a given point corresponding to the same motion are then combined using a probabilistic approach. In the application to optical flow estimation, the use of "motion patterns" allows multiple velocities to be represented, while the combination of estimations from different filters helps reduce the aperture problem. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies