Geometrical positioning with selective hybrid RSSD/AOA weighting AML algorithm for NLOS localization

Localization is a key issue for wireless sensor network. The traditional localization methods achieve high location accuracy using received signal strength (RSS) in line of sight (LOS) environment. But the localization accuracy degrades significantly in nonline of sight (NLOS) environment. So the localization in NLOS environment is one of the most challenging for wireless sensor network. In this paper, we propose an RSS based localization algorithm in NLOS environment. A NLOS identification algorithm is firstly proposed. This algorithm does not need the parameters of the NLOS in prior. Then we correct the NLOS measurements by subtracting the mean of NLOS errors. Finally, the Kalman filter is employed to mitigate the process noise. In simulations, we demonstrate that the proposed algorithm achieves higher localization accuracy compared with other methods.

This paper considers the global localization of a pedestrian via an ultra-wideband (UWB) ranging aided inertial navigation system (INS) and aims to address the challenges involved in proper processing of UWB range measurements. Even though UWB offers a decimeter level accuracy in line-of-sight (LoS) ranging, its accuracy degrades significantly in non-line-of-sight (NLoS). This drop in accuracy is due to a significant unknown positive bias in the NLOS range measurements. Therefore, the measurement models used in UWB LoS and NLoS ranging conditions are different, and proper processing of NLoS measurements requires a bias compensation measure. Previous work on bias compensation for UWB ranging that is used to aid an INS based localization assumes that the LoS and NLoS measurements are identified and distinguished from each other with absolute certainty. However, in practice, this assumption is hard to satisfy, and identifiers that determine the type of UWB range measurements deliver their results with only some level of certainty. To take into account the probabilistic nature of the NLoS identifiers, in this paper, we propose an adaptive localization based on the first-order generalized pseudo Bayesian (GPB) method to seamlessly handle the measurement model switching between LoS and NLoS UWB range measurements. The effectiveness of our proposed method is demonstrated via an experiment for pedestrian geolocation using a shoe-mounted INS system aided by UWB range measurements with respect to beacons with known locations.

Due to the wide deployment of wireless local area networks (WLANs) and the easy acquirement of received signal strength (RSS), indoor localization based on RSS has attracted considerable attention in both academia and industry. In this paper, we extract the scatter factor from RSS and then propose a novel indoor localization scheme based on location fingerprinting and curve fitting techniques. The scheme is set up in two phases. In the offline phase, we create a fingerprint and apply the curve fitting technique to construct fitted RSS-distance functions in LOS (Line Of Sight) and NLOS (None Line Of Sight) condition, respectively. In online positioning phase, the proposed optimized fingerprinting-based localization algorithm (OFPL) uses trilateration technique to assist the fingerprinting localization in choosing the proper location of the receiver. Through conducting field and extensive experiments, we can draw the conclusion that our proposed algorithm can obtain some improvement in localization accuracy compared with the classical fingerprinting-based localization.

Recently, positioning services have received considerable attention. The primary source of the positioning error is non-line-of-sight (NLOS) propagation. To address this problem, we propose a novel NLOS mitigation scheme, in which the geometric relationship between a base station and a mobile station is used. This makes it possible to identify range measurements corrupted by NLOS errors, and the mobilestation can then estimate its position through line-of-sight (LOS)measurements. Moreover, the threshold of the NLOS detector is derivedvia a hybrid method using both the analytical derivationand computer simulation, which significantly reduces the difficultyof identifying thresholds. After identifying the NLOS measurements, a two-step weighted-least-squares algorithm is usedto obtain the localization, in which both range and anglemeasurements are considered. The simulation results revealthat the proposed algorithm yields a high identification probabilityof NLOS measurements, which results in improved localizationperformance.

Device-free passive (DFP) localization systems are a key solution for location-based services because they do not require any wireless device on a human body. Most of the existing DFP localization systems are based on the received signal strength (RSS) measurement only. However, the localization accuracy of RSS only-based systems is easily affected by the spatial and temporal variances of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP using signal subspace eigenvectors from an antenna array. We present a fingerprinting technique using multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We then evaluate the localization accuracy of our proposed system in different propagation environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement: centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared to the conventional RSS-only based system.

Visible light communication (VLC) is a subset of OWC. VLC deliberates line of sight (LOS) connections as well as different reflections from different paths. In a practical condition, LOS links could be affected due to the existence of the obstruction between LED transmitter and PD receiver. However, it is possible to create the wireless link in a non-line of sight (NLOS) scenario when the reflected light paths are present. Nevertheless, in terms of received signal strength indication (RSS), there is still a probability of demolishing the system performance. This paper presents a new and unique indoor visible light communication-based power maximization of received signal under the condition of NLOS. Based on a white light emitting diode, a photo-detector and evolutionary algorithm, genetic algorithm (GA) is a one type of evolutionary algorithm. In this paper, we have used GA which performs the maximization of the received power. Simulation result reflects the feasibility of the proposed methodology. The maximization of VLC-based power in NLOS condition can benefit smarter indoor communication in near-future VLC domain.KeywordsGenetic algorithmNon-line of sightOptical communicationVisible light communication

The location of mobile station is an important issue for wireless communication systems. A location estimation scheme using fuzzy-based Interacting Multiple Model (IMM) smoother is proposed in this paper. It combines the time-of-arrival (TOA) and the received signal strength (RSS) measurements to achieve high location accuracy. The fuzzy technique is used to interpolate several linear equations to approximate the nonlinear RSS measurement. The IMM is employed as a switch between the line-of-sight (LOS) and non-line-of-sight (NLOS) states which are considered to be a Markov process with two interactive modes. By integrating the fuzzy filtering and the IMM method for range estimation between the corresponding base station (BS) and mobile station (MS), the proposed robust scheme, in association with data fusion, can efficiently mitigate the NLOS effects on the measurement range error. Simulation results are given to confirm the performance of the proposed method.

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

Precise positioning of agent is a challenging task due to the non line-of-sight (NLoS) propagation in ad-hoc location-aware networks. In an effort to enhance location accuracy, we address a novel scheme to alleviate NLoS error based on the appealing feature of linear lines-of-position (LLoP) and taylor series-least square (TS-LS). This approach utilizes single measurements at a set of anchors and comprises three stages: combination-refine stage, line-of-sight (LoS) reconstruction and taylor-optimization. The second stage reconstructs the true ranges by subtracting the NLoS measurements from the maximum measurement residual. Simulation results and discussions are given to evaluate the performance in typical NLoS environments. The conclusions infer that the proposed algorithm outperforms the traditional algorithms in dense NLoS environment at a reasonable computational complexity.

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.

Previous work shows that Average Bit Error Probability (ABEP) of SM systems under Non Line of Sight (NLOS) scenario is lower than that under Line of Sight (LOS) scenario because of a lower spatial correlation with a given Signal to Noise Ratio (SNR). However, when transmit power is constrained as a constant, the SNR under LOS scenario is larger than that under NLOS scenario benefiting from the strong LOS ray. Spatial correlation and SNR are two contradictory factors which both impact the performance of SM systems. In order to analyze performances of SM systems with a constant power under LOS and NLOS indoor scenarios, 15150 complex MIMO channel matrices are measured in a typical building of Shenzhen Graduate School, Harbin Institute of Technology. Based on measured data and motivated by the Minimum Distance Lower Bound (MDLB) approach, Euclid distances between received symbols of SM systems, as a monotonic function of ABEP, is analyzed. It is found that the effect of the SNR is much more important than the spatial correlation in the indoor environment so that SM system under LOS scenario shows a better performance than NLOS when the transmitting power is a constant. Furthermore, it is observed that bottlenecks of ABEP under LOS and NLOS scenarios are different. Under the LOS scenario, the bottleneck of ABEP is the antenna index error. On the contrary, under the NLOS scenario, the bottleneck of ABEP is the joint error. Using two extreme cases, such phenomena is analytically explained in the paper.

Indoor localization system based on received signal strength (RSS) often operate under non-line-of-sight (NLOS) conditions that can cause ranging errors. To identify non-line-of-sight status and line-of-sight (LOS) status and improve the accuracy of indoor localization, a D-S evidence theory based NLOS identification algorithm was proposed. The algorithm is divided into two parts. In the first part, respectively extract a variety of RSS features in NLOS and LOS status and choose the appropriate filter for data processing; in the second part, RSS features fused by D-S evidence theory are used to identify NLOS and LOS status. In an actual test environment, the algorithm was applied to fingerprinting localization, then analyzed the advantages of fusing RSS features and the influence of RSS features selection on experimental results. Finally, compared with several commonly used NLOS and LOS identification algorithm, it shows the proposed algorithm can deal with the indoor localization environment with obstacles with a high localization accuracy and good stability.

This paper proposes a robust localization algorithm designed for UAV-assisted search-and-rescue scenarios in post-earthquake environments. In such conditions, mobile devices are often buried beneath rubble, and signal propagation is dominated by non-line-of-sight (NLOS) paths, creating significant uncertainty in received signal strength (RSS) measurements. We introduce an iterative second-order cone programming (SOCP) solution based on a least-square relative error (LSRE) framework, which jointly estimates the target’s location, transmit power, and path loss exponent without relying on prior calibration. The algorithm incorporates worst-case modeling to enhance resilience against environmental variability and is tested under fully obstructed NLOS conditions. Simulations leverage empirical path loss parameters obtained from physical ruins scenarios to ensure realism. Results demonstrate that the proposed method achieves high localization accuracy and stable convergence under highly challenging NLOS environments, offering practical value for emergency localization systems in disaster response operations.

For the mass demands of wireless communication services, the mobile location technologies have drawn much attention around the world. In wireless communication, one of the main problems with accurate location is nonline of sight (NLoS) propagation. To solve the problem, we present a new location algorithm with clustering technology by utilizing the geometrical feature of cell layout, time of arrival range measurements, and three base stations. The mobile location is estimated by solving the optimal solution of the objective junction based on the high density cluster. Simulations study was conducted to evaluate the performance of the algorithm for different NLoS error distributions and various upper bound of NLoS error. The results of our experiments demonstrate that the proposed algorithm is significantly more effective in location accuracy than range scaling algorithm, linear lines of position algorithm, and Taylor series algorithm, and also satisfies the location accuracy demand of E-911.

Light scattering on diffuse rough surfaces was long assumed to destroy geometry and photometry information about hidden (non line of sight) objects making ‘looking around the corner’ (LATC) and ‘non line of sight’ (NLOS) imaging impractical. Recent work pioneered by Kirmani et al. [1], Velten et al. [2] demonstrated that transient information (time of flight information) from these scattered third bounce photons can be exploited to solve LATC and NLOS imaging. In this paper, we quantify the geometric and photometric reconstruction limits of LATC and NLOS imaging for the first time using a classical linear systems approach. The relationship between the albedo of the voxels in a hidden volume to the third bounce measurements at the sensor is a linear system that is determined by the geometry and the illumination source. We study this linear system and employ empirical techniques to find the limits of the information contained in the third bounce photons as a function of various system parameters.