Received Signal Strength Algorithm Research Articles

A Dual-Branch Convolutional Neural Network-Based Bluetooth Low Energy Indoor Positioning Algorithm by Fusing Received Signal Strength with Angle of Arrival

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology.

Indoor receiving signal strength based visible light positioning enabled with equivalent virtual lamps.

Visible light positioning (VLP) has become one of the most potent alternative methods in indoor positioning scenarios. However, the system performance of VLP is largely related to the characteristics of the wireless optical channel. Limited access resulting from shadowing, obstacles, and multipath propagation between transmitters and photodetectors might lead to positioning failure. In this paper, we propose a mirror-assisted VLP method based on the received signal strength algorithm. By introducing specular reflection into the system model, multipath propagation can be effectively alleviated, making it possible to implement VLP with limited accessibility of LEDs. Simulation results show that the proposed approach, based on the assumption of equivalent virtual lamps, can mitigate the non-line-of-sight interference in a classical four-lamp room. Average positioning performance improves by 25% with the assistance of a mirror. In addition, trilateration positioning can be realized when only two light-emitting diodes are available in the target room. The positioning accuracy of the two-LED VLP can reach ∼30c m under scenarios considering non-line-of-sight reflections.

Hybrid Positioning Algorithm for Tilted Receiver Using RSS and TDOA with Gaussian Process

In the visible light positioning (VLP) system, the received signal strength (RSS) algorithm has a better signal noise ratio performance than the time difference of arrival (TDOA) algorithm, while the RSS algorithm needs to work under the condition that the transmitter and receiver are strictly parallel. However, the receiver is prone to tilt due to environmental disturbances, which reduces the accuracy of the RSS algorithm. For the tilted receiver, the TDOA algorithm has a higher positioning accuracy than the RSS algorithm. In order to take full advantage of the two algorithms, we propose a hybrid positioning algorithm to locate the tilted receiver by using a Gaussian process (GP). The scheme separately uses RSS and the distance difference as the inputs of the GP model to estimate the position of the receiver. Then, according to the proposed positioning selection strategy, the more credible estimated position in our opinion is selected as the final estimated position. In addition, RSS information in the hybrid algorithm is extracted from the TDOA signal, which allows the hybrid algorithm to prevent an increase in the complexity of the VLP system. During the training and testing, RSS is normalized to meet the order-of-magnitude requirements of the GP model on the input data. Simulation results validate the hybrid algorithm based on a two-dimensional positioning system for the tilted receiver. When the standard deviations of the azimuth angle and elevation angle are 1°, the positioning accuracy of the hybrid algorithm is 53.7% higher than that of the RSS algorithm using an artificial neural network, and 49.9% higher than that of the RSS algorithm using a GP. The localization error under 1° standard deviations of azimuth and elevation angles is 20.2% lower than that under 20° standard deviations of the two angles.

On the time–frequency-based two-light-emitting diode positioning in visible light communication

Conventional visible light positioning (VLP) schemes usually require at least three light sources, making positioning accuracy sensitive to shadowing, a limited number of light sources, or sparse light-emitting diode (LED) layout. We proposed a mirror-assisted VLP system, where two-LED positioning can be realized by adopting specular reflection and time–frequency combination method. The scheme is based on the concept of virtual lamps and the received signal strength algorithm. Specular reflection power from one of the virtual lamps is integrated into the effective positioning power of its corresponding real lamp to obtain increased received power and mitigate the impact of diffuse reflection. Meanwhile, the other virtual lamp is independently used as the third light source for positioning, which means that the system becomes a “pseudo” three-LED positioning system, so that positioning can be realized with only two real lamps available. The corresponding positioning accuracy evaluated numerically shows an upward trend as the distance between the receiver and mirror decreases. By reducing the distance between the transmitters and the mirror, the positioning error can be limited to ∼12.4 cm.

CSI-based sliding window fingerprinting method tailored for a signal blocking environment in VLP systems.

In visible light indoor positioning systems, the localization performance of the received signal strength (RSS)-based fingerprinting algorithm would drop dramatically due to the occlusion of the line-of-sight (LOS) signal caused by randomly moving people or objects. A sliding window fingerprinting (SWF) algorithm based on channel state information (CSI) is put forward to enhance the accuracy and robustness of indoor positioning in this work. The core idea behind SWF is to combine CSI with sliding matching. The sliding window is used to match the received CSI and the fingerprints in the database twice to obtain the optimal matching value and reduce the interference caused by the lack of the LOS signal. On this premise, in order to reflect the different contributions of various paths in CSI to the calculation of match values, a weighted sliding window fingerprinting (W-SWF) is also proposed for the purpose of further improving the accuracy of fingerprint matching. A 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to evaluate the positioning performance. The simulation results reveal that the mean errors of the proposed method are 0.20 cm and 1.43 cm respectively when the LOS signal of 1 or 2 LEDs is blocked. Compared with the traditional RSS algorithm, the weighted k-nearest neighbor (WKNN) algorithm, and the adaptive residual weighted k-nearest neighbor (ARWKNN) algorithm, the SWF algorithm achieves over 90% improvement in terms of mean error and root mean square error (RMSE).

Indoor High-Precision 3D Positioning System Based on Visible-Light Communication Using Improved Whale Optimization Algorithm

Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.

Camera Assisted Received Signal Strength Ratio Algorithm for Indoor Visible Light Positioning

A novel camera assisted received signal strength ratio (CA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of CA-RSSR is to utilize visual information captured by the camera first, to analyze the geometric relations among the light-emitting diodes (LEDs) and the receiver. Based on the visual information, the photodiode (PD) can locate the receiver by the signal strength of visible lights. Due to the combination of visual and strength information of visible light signals, CA-RSSR can mitigate the receiver orientation limitation in conventional received signal strength (RSS) algorithms. In addition, the positioning accuracy of CA-RSSR is significantly better than perspective-n-point (PnP) algorithms, which achieves positioning function based on visual information only. Simulation results show that the proposed approach can achieve an 80th percentile accuracy of around 12 cm over 85% indoor area regardless of the receiver orientations, which is better than the conventional RSS and PnP algorithms.

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning.

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

A Novel Hybrid RSS and TOA Positioning Algorithm for Multi-Objective Cooperative Wireless Sensor Networks

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

Recursive algorithm of the passive location in sensor networks based on measurement of the received signal strength

Introduction. Broad application for monitoring and control of surrounding space is found by sensor networks. For the passive location of radio sources (RS) in the sensor networks uses the method of RSS (received signal strength). It brings together a group of procedures, the hallmark of which is the application to determine the location of RS of the measured strength values of a received signal. In the known RSS algorithms received on the basis of the least squares method, the error of measurement of the ranges, calculated on the measured values of power isn't considered. At the same time definitions of location of RS is performed after receipt of the measurements from all sensors.Statement of the problem. It is believed that during the propagation of the signal from RS to sensor network, its coordinates don’t change. It is required to synthesize an algorithm which after formation of entry conditions on the basis of measurements of power of signals from three sensors, allowed to specify recurrently location of RS in process of receipt of measurements from other sensors in which the error of measurement of power is considered.The main part. The vector of the estimated parameters includes coordinates of position of a RS on the plane. The recurrent algorithm of estimation is received on the basis of the extended Kalman filter and belongs to the class of quasi-optimal algorithms with Gaussian approximation of a posteriori density of probability. The initial vector of an assessment of coordinates of RS and correlation matrix of an error of assessment are defined on the basis of the method of least squares in the presence of three measurements of the signal power.Analysis of the effectiveness of the algorithm. The analysis of efficiency of recurrent algorithm and its comparison with the known algorithms are carried out by means of statistical modeling. Sensor network includes eight sensors. The RS settles down on a circle with a radius of 600 m. Comparison of precision characteristics of the considered algorithms with the lower bound of Rao-Cramer is carried out.Conclusions. The algorithm is developed belongs to the class of RSS algorithms, which after formation of initial conditions on the basis of measurements of power of signals from three sensors, allows recurrently clarify the location of RS in process of receipt of measurements from other sensors. For the considered example, the values of the circular MSD of an error of assessment of location of RS the developed algorithm is close to potentially achievable values of circular MSD of lower limit of the Cramer-Rao, and less than those of known algorithms on 36-44.4 %. The received algorithm can be also easily extended to a case of a trajectory filtration of a RS at which parameters of his movement are estimated.

Investigation of a new handover approach in LTE and WiMAX.

Nowadays, one of the most important challenges in heterogeneous networks is the connection consistency between the mobile station and the base stations. Furthermore, along the roaming process between the mobile station and the base station, the system performance degrades significantly due to the interferences from neighboring base stations, handovers to inaccurate base station and inappropriate technology selection. In this paper, several algorithms are proposed to improve mobile station performance and seamless mobility across the long-term evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) technologies, along with a minimum number of redundant handovers. Firstly, the enhanced global positioning system (GPS) and the novel received signal strength (RSS) prediction approaches are suggested to predict the target base station accurately. Then, the multiple criteria with two thresholds algorithm is proposed to prioritize the selection between LTE and WiMAX as the target technology. In addition, this study also covers the intercell and cochannel interference reduction by adjusting the frequency reuse ratio 3 (FRR3) to work with LTE and WiMAX. The obtained results demonstrate high next base station prediction efficiency and high accuracy for both horizontal and vertical handovers. Moreover, the received signal strength is kept at levels higher than the threshold, while maintaining low connection cost and delay within acceptable levels. In order to highlight the combination of the proposed algorithms' performance, it is compared with the existing RSS and multiple criteria handover decision algorithms.

Adaptive RLS-Received Signal Strength Algorithm in Wireless Network Area for Multi-Mobile Nodes Location Estimation System

the current Received Signal Strength based positioning systems have been designed to monitor the location information of mobile nodes and Patients. The positioning systems are designed with different wireless communication technologies and adapted algorithms in wireless network area. The adaptive algorithms improve the accuracy of location of mobile nodes in RSS (Received Signal Strength) based positioning systems. The proposed work introduced adaptive Recursive Least Square - Received Signal Strength (RLS-RSS algorithm) algorithm to reduce the effect of multipath propagation in Trilateration Localization system. In the proposed work the entire three reference node receives the RSS-values from the multi-mobile nodes and then RLS- RSS algorithm is used to estimate the RSS-values at each reference node. The estimated RSS-value provides the coordinates of multi-mobile nodes. From the simulation results it is shown that the accuracy of the coordinate's point of the multi-mobile nodes is improved for different environments.