Recently, several positioning systems based on visible light communications (VLC) have been proposed, and most of them are based on received signal strength (RSS) because of its simplicity and high accuracy. In order to improve the accuracy further, a differential detection-based positioning algorithm is proposed. It can reduce positioning instability caused by light intensity fluctuation and the area outside the light-emitting diode (LED) cell can also be positioned. Experimental results show that the proposed method can improve the positioning accuracy from 10.0 to 4.0 cm and reduce the standard deviation from 9.0 to 2.5 cm. Meanwhile, the positioning accuracy outside the cell can reach 10.0 cm or less.

High Accuracy VLC Indoor Positioning System With Differential Detection

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays.The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim–Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values.

/pdf/an-indoor-variance-based-localization-technique-utilizing-1vxfnqwbq5.pdf

An Indoor Variance-Based Localization Technique Utilizing the UWB Estimation of Geometrical Propagation Parameters

Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution.

Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

/pdf/parameter-selection-and-performance-comparison-of-particle-2v6tzorkyu.pdf

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Graphical abstractDisplay Omitted 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.

A swarm-based approach to real-time 3D indoor localization

An Accurate Indoor-Localization Scheme with NLOS Detection and Elimination Exploiting Stochastic Characteristics

Collecting location information of persons or things in sensor networks enables a variety of services and applications. For such location information, time of arrival (TOA) or angle of arrival (AOA) of radio waves is often used. Moreover, a hybrid use of them, called hybrid-TOA/AOA (HTA), has also been proposed for performance improvement. However, when the direct wave is blocked by obstacles in the non-line-of-sight (NLOS) environment, the estimation accuracy is severely deteriorated. To coordinate this problem, an iterative minimum residual (IMR) scheme has been proposed for TOA-based estimation. In the IMR scheme, the NLOS nodes are sequentially detected and eliminated, and the estimation performance is improved. However, the IMR scheme for HTA measurements has not been considered. Therefore, in this paper, we propose a new NLOS detection scheme based on IMR scheme for HTA localization and evaluate its improved performances by computer simulations.

An improved NLOS detection scheme for hybrid-TOA/AOA-based localization in indoor environments

To provide a location-based service (LBS), it is needed to obtain an exact location of communication terminals in sensor networks. Because the signal of global positioning system (GPS) cannot be received indoors, a triangulation-based location estimation using ultra-wide band (UWB) signals between more than three reference terminals and the target node is widely used. In particular, a time of arrival (TOA)-based least square (LS) estimation is popular because the balanced performance in terms of calculation complexity and the accuracy is obtained. However, when the height of reference terminals and the target node is close, the three-dimensional LS-based estimation tends to fall into a local-minimum solution and it needs an accurate initial value of search to keep the estimation performance, resulting in the calculation complexity increase. Therefore, in this paper, we adopt a particle swarm optimization (PSO) method which effectively searches in wide-area space and propose an LS-based localization scheme using the combination of PSO and Newton-Raphson method achieving lower calculation complexity. The improved performances are shown by computer simulations.

/pdf/particle-swarm-optimization-based-low-complexity-three-44lmw3d1os.pdf

Particle swarm optimization-based low-complexity three-dimensional UWB localization scheme

Indoor localization scheme using sensor networks is expected to be applied in various fields, and the localization scheme using time of arrival (TOA) is well-known. However, the estimation accuracy of TOA localization is severely deteriorated in non-line-of-sight (NLOS) environments, and the NLOS mitigation scheme such as iterative minimum residual (IMR) scheme is required. The IMR scheme is often applied because of its lower calculation complexity. However, when an increased number of NLOS nodes exist, the NLOS detection errors increase in the IMR scheme and the estimation accuracy deteriorates. Therefore, in this paper, we propose a new scheme exploiting rough NLOS detection based on stochastic characteristics before the application of IMR scheme to improve the localization accuracy. The improved performance is shown by computer simulations.

/pdf/an-improved-nlos-detection-scheme-using-stochastic-5ggp277yjd.pdf

An improved NLOS detection scheme using stochastic characteristics for indoor localization

We propose a low-complexity indoor localization scheme using a hybirid of particle swarm optimization (PSO) and Newton-Raphson (NR) search. The signal of global positioning system (GPS) can be utilized outdoors only, and other schemes are needed for indoor localization. A triangulation-based location estimation using ultra-wide band (UWB) signals between more than three reference terminals and the target node is widely used for centimeter-order localization. In particular, a time of arrival (TOA)-based least square (LS) estimation is popular because the balanced performance in terms of calculation complexity and the accuracy is obtained. However, when the height of reference terminals and the target node is close, the three-dimensional LS-based estimation tends to fall into a local-minimum solution and it needs an accurate initial value of search to keep the estimation performance, resulting in the calculation complexity increase. Therefore, in this paper, we adopt a particle swarm optimization (PSO) method which effectively searches in wide-area space and propose an LS-based localization scheme using the combination of PSO and NR method achieving lower calculation complexity. The improved performances are shown with comparing to conventional search schemes by computer simulations.

/pdf/low-complexity-indoor-uwb-localization-scheme-using-particle-3azlsawfoe.pdf

Toshiko Shinohara

Papers

An Accurate Indoor-Localization Scheme with NLOS Detection and Elimination Exploiting Stochastic Characteristics

An improved NLOS detection scheme for hybrid-TOA/AOA-based localization in indoor environments

Particle swarm optimization-based low-complexity three-dimensional UWB localization scheme

An improved NLOS detection scheme using stochastic characteristics for indoor localization

Low-complexity indoor UWB localization scheme using particle swarm optimization