Mobile Location Estimation Research Articles

TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

Robust Localization in Cellular Networks via Reinforced Iterative M-Estimation and Fuzzy Adaptation

We consider robust mobile location estimation using range measurements from multiple base stations under probabilistically mixed line-of-sight (LOS)/non-LOS (NLOS) conditions in a cellular network, where the NLOS statistics and occurrence probabilities (NOPs) are unknown. We develop new algorithms for achieving desirable performance under these challenging conditions. First, we propose a measurement preprocessing (MPP) scheme for dynamically bounding the NLOS influence on raw range measurements. The mobile positioning problem is cast as the state estimation of an MPP-based system model. Next, we introduce extended Kalman filter (EKF)-based reinforced robust regression and develop a gradient descent-type iterative M-estimation procedure with ensured stability, resulting in a reinforced M-estimation-based robust EKF (M-REKF). Subsequently, we present a fuzzy-tuning M-REKF (FTM-REKF) incorporating adequate fuzzy adaptation of measurement noise covariances in the M-REKF. A performance analysis provides insight into how adequate fuzzy rules can be established methodically for the FTM-REKF. Simulations demonstrate that despite varying NOPs and NLOS statistics, the FTM-REKF and M-REKF stably yield satisfactory location estimates of moving and stationary targets, with the former outperforming the latter, and they can meet the Enhanced 911 positioning requirements. Moreover, the proposed algorithms significantly outperform several salient iterative M-estimation-based EKFs, and their MPP-based counterparts.

An algorithm of NLOS error identification and mitigation in mobile location estimation

For the purpose of improving positioning accuracy of mobile station in the complex environment, a real-time non-line-of-sight (NLOS) identification and elimination method is proposed based on the c...

An algorithm of NLOS error identification and mitigation in mobile location estimation

An algorithm of NLOS error identification and mitigation in mobile location estimation

Mobile Location Estimation Based on Received Signal Strength using Circular Approach

Mobile Location Estimation Based on Received Signal Strength using Circular Approach

Detection of the First Component of the Received LTE Signal in the OTDoA Method

In a modern world there is a growing demand for localization services of various kinds. Position estimation can be realized via cellular networks, especially in the currently widely deployed LTE (Long Term Evolution) networks. However, it is not an easy task in harsh propagation conditions which often occur in dense urban environments. Recently, time-methods of terminal localization within the network have been the focus of attention, with the OTDoA (Observed Time Difference of Arrival) method in particular. One of the main factors influencing the accuracy of location estimation in the OTDoA method is the nature of the propagation channel that affects the ease of isolating the signal component travelling from the transmitter to the receiver through the shortest path. To obtain the smallest possible localization error, it is necessary to detect the first received component of the useful signal. This aim could be achieved by using a proper algorithm within the receiver. This paper proposes a new algorithm for effective detecting of the first component of the LTE downlink signal in the multipath environment. In a mobile terminal location estimation process, CSRS (Cell Specific Reference Signal) signals were used instead of dedicated PRS (Positioning Reference Signal) signals. New solution was verified during the measurement campaign in a real LTE network.

A Novel Hybrid Location Algorithm Based on Chaotic Particle Swarm Optimization for Mobile Position Estimation

Location estimation is significant in mobile and ubiquitous computing systems. Considering the influence of measurement error caused by time difference of arrival (TDOA)/angle of arrival (AOA) hybrid location algorithm and the nonlinear optimization problem encountered in the location estimation, in this paper, a particle swarm optimization (PSO) algorithm based on the chaos theory is proposed for the hybrid location of mobile location estimation. Taking the TDOA/AOA hybrid location algorithm for mobile location estimation as the object, the proposed algorithm greatly improves the location performance and accuracy of mobile location estimation. First, the estimation function of the mobile station is obtained by the maximum likelihood method, and then the initial population of PSO is generated by using the estimation function of the mobile station as a fitness function. The chaotic optimized particle swarm optimization algorithm (CPSO) is used to solve the optimal solution of the optimal position of the population and obtain the optimal mobile location position estimation, which makes the TDOA/AOA location algorithm have better location performance. The simulation results have demonstrated that the performance of the proposed method compared with the traditional Chan algorithm, the Taylor algorithm, and the TDOA/AOA hybrid location algorithm, the proposed algorithm can reduce the impact of error on the location accuracy, achieve a balance of global and local search capabilities, and have a faster convergence speed and more accurate positioning accuracy.

A novel time difference of arrival localization algorithm using a neural network ensemble model

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

Combination linear lines of position and neural network for mobile station location estimation

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

Artificial Neural Network Approach to Mobile Location Estimation in GSM Network

AbstractThe increase in utilisation of mobile location-based services for commercial, safety and security purposes among others are the key drivers for improving location estimation accuracy to better serve those purposes. This paper proposes the application of Levenberg Marquardt training algorithm on new robust multilayered perceptron neural network architecture for mobile positioning fitting for the urban area in the considered GSM network using received signal strength (RSS). The key performance metrics such as accuracy, cost, reliability and coverage are the major points considered in this paper. The technique was evaluated using real data from field measurement and the results obtained proved the proposed model provides a practical positioning that meet Federal Communication Commission (FCC) accuracy requirement.

Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information.

Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis.

Multi Linear Regression Model for Mobile Location Estimation in GSM Network

Background/Objectives: Recently, several researchers are directed in the area of mobile location estimation in GSM network. The major interests in the research include improving the accuracy of location estimation. Methods/Statistical analysis: Therefore, this paper present the first application of Multiple Linear Regression (MLR) analysis for mobile location estimation in a GSM network and without pre-processing or manipulating the Location Dependent Parameter (LDP) - Received Signal Strength Indicator (RSSI). The proposed model was developed and evaluated using Received Signal Strength (RSS) and geographical coordinates obtained from drive tests. Findings: The results show that, 67% of the calls, the positioning error is less than 64 m and 95% of the calls will result in positioning error less than 115 m while the maximum error is 275m for the urban area. Application/Improvements: Results show improved accuracy in location estimation. This model can be adopted for any mobile location application including the emergency call services (E-911) that requires very high accuracy level.

Robust Urban Wireless Localization: Synergy Between Data Fusion, Modeling and Intelligent Estimation

In this paper, we present a viable Bayesian estimation alternative to mobile localization enhancement in mixed line-of-sight (LOS)/non-LOS (NLOS) urban areas. The development of the proposed approach relies on a synergistic combination of valid aggregate measurements, NLOS bias modeling and estimation, and computational intelligence. For reliable wireless positioning, we first introduce valid range measurements in which the effect of the NLOS range bias due to small-/large-scale multipath fading is limited. Subsequently, we propose a hybrid system framework with Markovian state transitions, data fusion of valid range and signal power, NLOS bias modeling, and fuzzy inferences for modeling the dynamics of a mobile station (MS) with respect to each base station (BS). The proposed framework enables us to develop a selective fuzzy-tuned extended Kalman filtering based interacting multiple-model (SFT-IMM-EKF) algorithm for each BS to perform mobile location estimation. We show that due to the synergistic effects, the proposed SFT-IMM-EKF can remarkably improve the IMM, the SFT-IMM and the IMM-EKF. The result is substantiated by numerical simulations. As well, it is demonstrated that the proposed algorithm can robustly leverage against the adverse impacts of severe NLOS errors and MS mobility variations.

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

A Novel Mobile Localization Method for Distributed Sensor Network with Non-Line-of-Sight Error Mitigation

Mobile localization using distributed sensor network has attracted significant research interest in recent years. One of the main challenges for mobile location estimation is the presence of the non-line-of-sight (NLOS) propagation. It results in erroneous measurements containing NLOS error due to the reflection and diffraction. The NLOS error could seriously reduce the localization accuracy. This paper presents a novel mobile localization method for distributed sensor network in NLOS propagation scenarios. The aim of the method is to mitigate the NLOS error and enhance the localization accuracy. The transition between the LOS and NLOS is described by two-state Markov model. The interacting multiple model frame is employed to estimate the position of unknown node. The probability data association algorithm is used to filter the estimated location. The simulation results show that the proposed method outperforms the traditional filter method in NLOS scenarios.

A Systems Engineering Approach to Improving the Accuracy of Mobile Station Location Estimation

Enhanced 911 is a first line of assistance for practically every emergency situation, and many cell phone users today expect the same results from an emergency call no matter where they are-whether on the side of the road, in the woods, or in a building. It is a vital part of our nation's emergency response and disaster preparedness system. In the context of 911 service, demand for providing reliable and accurate mobile station (MS) location estimation has become a high priority and has gained momentum in recent years. A major challenge in mobile station location estimation is locating an emergency caller within desired accuracy in an adverse environment where non-line-of-site (NLOS) propagation exists. This paper develops a methodology to improve the accuracy of mobile station location estimation in an NLOS environment. A unique feature of this methodology development, compared to other approaches in the literature, is the application of the systems engineering process. While there are many definitions, systems engineering as applied here is an approach and process for developing the preferred solution to a set of requirements. The methodology consists of two stages. In the first stage, a series of time-of-arrival range measurements are made from each base station (BS) to the MS. Binary hypothesis testing on the standard deviation of the range measurements at a given BS is used to determine if the measures are taken under NLOS conditions. Then, if possible, any BS deemed to be NLOS is eliminated from the estimation in the second stage, in which the selected time measurements of several BSs are combined through least squares to estimate the location of the mobile station. Based on a simulation study, the methodology appears to have the potential to significantly improve the accuracy of location estimates in certain situations.

Indoor Mobile Localization in Wireless Sensor Network under Unknown NLOS Errors

Localization is one of the key techniques in wireless sensor network. One of the main problems in indoor mobile localization is non-line-of-sight (NLOS) propagation. And the NLOS effects will lead to a large localization error. So the NLOS problem is the biggest challenge for accurate mobile location estimation in WSN. In this paper, we propose a likelihood matrix correction based mixed Kalman and H-infinity filter (LC-MKHF) method. A likelihood matrix based correction method is firstly proposed to correct the LOS and NLOS measurements. This method does not need the prior information about the statistical properties of the NLOS errors. So it is independent of the physical measurement ways. And then a mixed Kalman and H-infinity filter method is proposed to improve the range measurement. Simulation results show that the LC-MKHF algorithm has higher estimate accuracy in comparison with no-filter, Kalman filter, and H-infinity filter methods. And it is robust to the NLOS errors.

Location of Mobile Station With Maneuvers Using an IMM-Based Cubature Kalman Filter

In this paper, we consider the problem of mobile location estimation in a rough wireless environment. To mitigate the non-line-of-sight (NLOS) effects, the transition between the line of sight and the NLOS is described by a discrete-time Markov chain. The multiple-model approach is applied first time for locating a mobile station (MS), and a location estimation algorithm is developed by employing the basic interacting-multiple-model approach to a jump Markov system with two independent switching parameters. The data fusion of the time of arrival and the received signal strength measurements is utilized, and the cubature Kalman filter is applied for nonlinear estimation. Simulations are presented to illustrate the effectiveness of the proposed algorithm involving locating an MS with maneuvers.

Robust Mobile Location Estimation Using Hybrid TOA/AOA Measurements in Cellular Systems

Estimating a location of mobile station is considerable interest in wireless cellular systems. This letter deals with a hybrid time-of-arrival (TOA)/angle-of-arrival (AOA) mobile location estimation under relatively low signal-to-noise ratio and moderately nonline-of-sight (NLOS) scenarios. To reduce location estimation errors, a robust estimator for hybrid TOA/AOA location systems is proposed by using the minimax mean-squared error (MSE) criterion. Simulation results demonstrate that the proposed estimator significantly outperforms the other least squares estimators.

Mobile location estimation scheme in NLOS environment

A Kalman-based hypothesis testing (KHT) algorithm is proposed for mobile location estimation in non-line of sight (NLOS) environments. Hypothesis testing is employed to detect whether the environment is NLOS or LOS along with time of arrival (TOA) and received signal strength (RSS) measurements. And then an extended Kalman filter is used to nonlinear estimation. Simulation results show that the Kalman-based hypothesis testing algorithm has higher estimate accuracy in comparison with extended Kalman filter.