An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> LEDs have many advantages compared with the conventional lamps. Therefore LEDs are expected to be the next generation of lamps that will be widely installed to replace conventional lamps. Due to their rapid response times, LED's can be modulated at very high-speeds, which allows the possibility of simultaneously providing communication while illuminating. Considering these facts, a system can be designed to receive (using a single photodiode or a camera) and analyze LED signals. Furthermore, such a system should be able to facilitate position estimation tasks either for people or vehicles, especially in indoor navigation tasks where the LED positions are known. Herein two methods — single photo-detector and array photo-detector (camera) — are introduced. The single photo-detector approach is used to detect the existence and the specific identity of an LED. The array photo-detector approach measures the angle-of-arrival of the LED signal. Navigation methods based on this measured information is considered. <s> BIB001 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> A two phase hybrid algorithm for estimating the location of a mobile node, which has the capability of measuring signal strength, azimuth, and elevation, in a smart space environment over the visible light channel is proposed. In contrast to conventional triangulation approaches which are performed in a simplified plane, the smart room architecture requires a non-planar solution due to the illumination requirement. Furthermore, conventional triangulation approaches can at times produce numerically ill-defined solutions, thereby prohibiting a notion of target location. Instead of solely relying on triangulation, the mobile node estimate their locations through a two phase approach in which they firstly exploit the signal strength observables with unique IDs to establish a coarse estimate, and secondly use the azimuth and elevation observables to establish a fine estimate. In many cases, the fine estimate will improve upon the coarse estimate; however when triangulation fails, the algorithm yield the coarse estimate rather than a localization failure. Since the environment model relies on the primary requirement of adequate illumination, the number of LED anchors and transmit power for communication functions are determined. Simulation results confirm the effectiveness of the hybrid two phase localization approach in a smart space indoor environment by having a median coarse phase accuracy of 34.88 cm and a fine phase median accuracy of 13.95 cm <s> BIB002 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> A range-free visible light communication (VLC) based positioning method is proposed for indoor wireless sensor networks (WSN). Then the proposed VLC based positioning method and the conventional received signal strength indication (RSSI) based positioning methods are merged into a hybrid positioning method for indoor WSN to get more accuracy position message with lower power consumption. Computer simulations demonstrate that in a Rician fading environment, the accuracy of positioning can be effectively improved with the proposed hybrid positioning method and also longer battery life can be expected since the lower power consumption. <s> BIB003 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> LED's offer multiuse opportunities related to illumination, communication, and navigation. These features enable reliable feature association for feature aided navigation. We introduce an aided navigation system that estimates the position and pose of the vehicle moving in a 2D plane using LED's and a high speed camera. No prior knowledge of the vehicle state is required due to initialization at the first time instant that provides measurements of at least two LED's. The data association problem is solved using the LEDs' unique IDs. The IDs are modulated respectively onto the LEDs' illumination and recovered by processing a sequence of images obtained by a high speed camera. <s> BIB004 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> The growth of light emitting diodes (LED's) for illumination enables new approaches both for communication and navigation. One of the challenges when LED's are used in the navigation systems is how to extract and capitalize effectively on the encoded LED ID. A single photo detector (PD) provides the simplest hardware approach and could communicate with LED's at a very high data rate, but only offers the most basic level of positioning. Cameras provides much more informative position measurements; however, there are challenges to achieving high rate LED-to-camera data communications due to the current hardware architectures of cameras. Alternatively, linear PD arrays allow high sample rates, high accuracy and low cost. This article investigates the issues (observability, extrinsic parameter calibration, and vehicle state initialization) related to implementation of a positioning and communications system built on a linear optical sensor array. <s> BIB005 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> A novel concept is proposed for integrating optical wireless visible light communications with 3-D indoor positioning using a single transmitter and multiple tilted optical receivers. We modeled a channel link, which was based on the transmitter and receiver characteristic data obtained in this experiment. The proposed 3-D positioning algorithm is based on gain difference, which is a function of the angle of arrival and the received signal strength. Our demonstration shows that the proposed algorithm can determine accurate positions, including height, without intercell interference. <s> BIB006 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> This paper deals with localization problem of mobile robots in indoor environment based on Visible Light Communications (VLC). We propose an integrated AOA-RSS localization method of Angle of Arrival (AOA) and Received Signal Strength (RSS) methods to find out position of a mobile robot using an array of photodiodes (PDs). Performance of the integrated localization method depends on the number of PDs mounted on the mobile robot. In order to achieve more accurately in positioning the mobile robot's location, we propose a combination of the proposed AOA-RSS localization method and the Extended Kalman Filter (EKF) algorithm. The efficiency of this combination is proved through computer simulation. The EKF also addresses a significant problem in robot navigation that is to control the mobile robot moving on the specified path in a certain activity. <s> BIB007 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> An indoor navigation system based on visible light communication (VLC) technology is proposed in this paper. Indoor positioning is realized by a received-signal-strength(RSS)-based method using modulated light-emitting-diodes (LEDs). In addition to the simple positioning, we use Kalman and particle filters to give the system tracking capability. Simulation results show that particle filtering converges to the true track faster than Kalman filtering, at the price of higher computational complexity. <s> BIB008 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> Visible light communications (VLC) based on illuminative LED lamps are attracting increasing attentions due to its numerous advantages. Compared with conventional lamps, LED illumination sources have long operational life, low power consumption, and are mechanically robust. Therefore LEDs are expected to be the next generation of lamps that will be widely installed to replace conventional lamps. Besides, LEDs can be modulated at very high speeds, which allows the possibility of simultaneously providing communication while illuminating. The light modulation frequency is sufficiently high that it is undetectable by the human eye, yet detectable by arrays of photodiodes. Considering these facts, a system can be designed to receive (using a single photodiode or a camera) and analyze LED signals. Furthermore, such a system should be able to facilitate position estimation tasks either for people or vehicles.Two kinds of sensors - single photo-detector and array photo detector (camera or linear array) C can be used to detect the LEDs. The single photo-detector approach is used to detect the existence and the specific identity of an LED. The array photo-detector approach measures the angle-of-arrival of the LED signal. A single photo detector provides the simplest hardware approach and could communicate with LED's at a very high data rate, but only offers the most basic level of positioning. Cameras provides much more informative position measurements; however, there are challenges to achieving high rate LED-to-camera data communications due to the current hardware architectures of cameras. Alternatively, linear PD arrays allow high sample rates, high accuracy and low cost. This dissertation will investigates the issues (observability, extrinsic parameter calibration, and vehicle state initialization) related to implementation of a positioning and communications system built on a linear optical sensor array.The VLC based navigation methods require recovering the LED ID from a sequence of photo-detector scans. This ID will help the data association in the navigation system or the data communication in the VLC system. Recovering LED data (ID) requires identifying each LED's projection position and on-off status in each photo-detector scan. Identifying the LED projection is challenging because: 1) Clutter and noise corrupt the measurements; 2) The LED status will be off in some frames; 3) The predicted projection location sequence depends on the estimated vehicle state trajectory, which is uncertain. This dissertation presents two new methods determining the most probable data and LED position sequences simultaneously, using Bayesian techniques by maximizing posterior probabilities. The first method is based on Viterbi algorithm and developed under the assumption that the frame rate of the array sensor is high relative to the rover motion bandwidth. The second one is based on multiple hypothesis tracking (MHT) techniques with no assumption assumed. Both the two methods are analyzed theoretically and demonstrated by experimental results. <s> BIB009 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> The need allowing indoor access to the Internet services requires to reduce the electromagnetic pollution. Recently, the scientific community is looking for alternatives to the use of radio frequency communication, leading to propose and study the new paradigm of visible light communications. In order to present a system implementing transmission and access procedures, we propose a logical stream starting from user localization, so as to decide which LEDs can provide access, thus basically performing a space-division multiple access by scheduling data on the basis of the bits to be transmitted. For this aim, a two-bands access scheme is proposed, and implemented through the use of a separated channel where signaling specifies where, in the wavelength domain, the reception must be operated by the different users. A multiple-input multiple-output scheme is considered for data transmission, in order to increase rate with a reliable error rate performance. Performance is evaluated for what concerns localization and access tasks, so as to show the effectiveness of the proposed scheme. Performance comparison with the recent literature has been also reported. <s> BIB010 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Hybrid Algorithm <s> In positioning systems there is a fundamental tradeoff between accuracy and latency. A latency constrained optimization algorithm is proposed to be employed in conjunction with a prior published two-phase algorithm for estimating the location of a mobile node over the visible light channel. Instead of solely relying on triangulation, the mobile nodes estimate their locations through a two phase algorithm in which they firstly exploit the signal strength observables with unique IDs to establish a coarse estimate, and secondly use the azimuth and elevation observables to establish a fine estimate. In many cases, the fine estimate will improve upon the coarse estimate; however when triangulation fails, the algorithm yields the coarse estimate rather than a localization failure. Since the environment model relies on the primary requirement of adequate illumination, the number of LED anchors, and transmit power for communication functions are determined. The advantage of the two-phase algorithm is that it provides a framework to provide rapid RSS based positional estimates with accuracies bounded to within the illumination projection radius (48 cm median) of the distributed luminaries (anchor nodes) within less than 17 nanoseconds while requiring only a single luminaire in its field of view. However, when there are a sufficient number of luminaires in view the positioning algorithm can further improve the accuracy (14 cm median) through application of triangulation via angle of arrival measurements, at the expense of latency (upto tenths of seconds). The proposed framework allows a fundamental trade of accuracy and latency to be made depending on the observable parameters known to the positioning algorithm at run time. <s> BIB011
In BIB010 an RSS-TDOA hybrid positioning system using VLC was proposed. The distances from the mobile device to base stations were determined based on RSS. Then the RSS-based distance differences were calculated. The TDOA information was obtained by detecting local highest peaks of the received signal. Then each TDOA was translated into a TDOA-based distance difference. The final distance differences were calculated as the weighted sums of the RSS-based and TDOA-based distance differences. Then Newton method was used to solve the equations formulated from the final distance differences, to obtain the position. The reason behind the weighted sum is that the estimations based on both RSS and TDOA contain errors. While RSS estimation suffers from reflected light, TDOA estimation is more sensitive to the fluctuations of the received power due to noises. Simulation results showed that the accuracy of the position determined by TDOA only was higher than that of RSS. More importantly, choosing proper weights, which were 0.34 for RSS and 0.66 for TDOA, yielded the smallest error distance, which was below 5 mm. The fingerprinting algorithm proposed in BIB006 was extended in BIB007 by using an extended Kalman filter (EKF) algorithm to track the position of a moving robot. An array of PDs was mounted on the robot to obtain both RSS and AOA information. Simulation results showed that the error got smaller when the number of PDs increased. For example, increasing the number of PDs from 8 to 16 reduced the error from 41 cm to 20 cm. The results also showed that adding the EKF reduced the average error and removed the cumulative errors. In BIB002 , RSS and AOA were used in two phases to calculate the positions of mobile devices in indoor environments. In the first phase, the coarse positions of the mobile devices were determined based on the maximum possible RSS. In the second phase, first, the angles between the PD and LEDs were estimated based on the ratio between the actual received power and the theoretical power that the receivers would receive if they are placed right under the LEDs (i.e., when the incident angle of LED light is zero). This theoretical received power can be calculated from known parameters of LED and PDs. Then from the estimated AOA, the distances from the PD to the LEDs are calculated and finally quadratic programming was used to determine the fine position. The simulation results reported that an accuracy of 14 cm was achieved. The work in BIB002 was extended in BIB011 by considering the mobility of receiver and the latency of position estimation. The paper applied a latency optimization to the system proposed in BIB002 to guarantee at least a position-coarse or fine, a position will be outputted as the result of the algorithm. More specifically, if the available information is insufficient (i.e., when triangulation fails) and the latency exceeds a threshold, the algorithm yields the coarse estimate rather than a localization failure. Simulation showed that in any case, a coarse RSS based estimated position with 48 cm of accuracy was obtained rapidly within 17 nanoseconds and in most cases a fine AOA based estimated position with 14 cm of accuracy was achieved. A proximity-RSS hybrid algorithm was proposed in BIB003 . In the first step, the coarse position of the mobile device was determined upon receiving the signal IDs of LED base stations. In the second step, a minimum mean square error (MMSE) algorithm was used to solve the RSS equations to find the refined position of the mobile device. Since the possible position of the mobile device was limited to the proximity area found in the first step, the MMSE algorithm only searched in much smaller area and thus consumed less power. In practice, errors in positioning are likely to introduce disconnected estimated positions as the mobile device moves. In these cases, statistical methods such as a Kalman filter can be used to obtain smooth position tracking results. In the VLC based indoor positioning system proposed in BIB008 , RSS was used to determine the position. And the tracking capability was enabled using particle and Kalman filters. The simulation results indicated that although it required longer time for processing, the particle filter yielded smoother results than the Kalman filter. An indoor tracking system based on VLC and dead reckoning was proposed in  . At the beginning the position of the mobile device was determined based on VLC. After that, a dead reckoning process was performed using a Kalman filter and information from geomagnetic and gyro sensors attached to the mobile device. The experiment was conducted with a robot moving at 0.6 m/s and the reported angular and positioning errors were 6 • and 10.5 cm, respectively. The authors of BIB001 introduced two methods for tracking the position of a robot. In the first method, a PD was used to receive the signal sent from LEDs at different frequencies. It was assumed that the robot has the prior knowledge about the location of all LEDs. Then by detecting the frequency of the received signal, the robot found its proximity. After that, a particle filter algorithm was applied to improve the positioning accuracy. In the second method, a camera and single view geometry algorithm were used to determine the position of the robot. The simulation results showed that the higher positioning accuracy was achieved by using camera. In BIB004 , a high speed camera and single view geometry algorithm were used for the initialization of a vehicle tracking system. Then an Extended Kalman filter was used for tracking the position of the vehicle. An experiment was conducted in an indoor environment with 8 LED placed in front of the vehicle. The result showed that the maximum distance error obtained in the initialization was less than 0.5 m. In BIB005 BIB009 , a linear PD array installed horizontally on a vehicle was used for receiving the signal transmitted from LED traffic light. A convex-cylindrical lens was attached in front of the PD array to spatially separate different LED. An extrinsic parameter calibration was used to find the position of the vehicle. Since the linear photo array can sense the spatial information of the LEDs in one dimension, at least 4 LEDs must be in the view of the PD array to enable positioning. After this initialization step, an extended Kalman filter was used to track the position of the vehicle. Experimental result showed that the maximum distance error was less than 0.2 m.