An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Method of Using a Scaling Factor <s> Two design techniques for more accurate and more convenient hybrid positioning system with visible light communication (VLC) and ad-hoc wireless network infrastructure are proposed, in order to overcome the problems of high estimation error, high cost, and limited service range of the conventional positioning techniques. First method is based on a non-carrier VLC based hybrid positioning technique for applications involving of low data rate optical sensing and narrow-range visible light reception from transmitter, and long-range positioning. The second method uses a 4 MHz carrier VLC-based hybrid positioning technique for a high data rate optical sensing and wide-range visible light receiving from transmitter, and midrange positioning applications. In indoor environments with obstacles where there are longrange 77.314m and mid-range 23.68m distances between an observer and a target respectively, the hybrid positioning systems developed with two design techniques are tested, and measured results are analyzed and presented in this paper. <s> BIB001 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Method of Using a Scaling Factor <s> An indoor hybrid localization system is realized and experimented with, using a five-hop Zigbee wireless network and visible light communications in order to improve positioning accuracy and achieve a long-range localization. <s> BIB002 </s> An in-Depth Survey of Visible Light Communication Based Positioning Systems <s> Method of Using a Scaling Factor <s> Visible Light Positioning (VLP) provides a promising means to achieve indoor localization with sub-meter accuracy. We observe that the Visible Light Communication (VLC) methods in existing VLP systems rely on intensity-based modulation, and thus they require a high pulse rate to prevent flickering. However, the high pulse rate adds an unnecessary and heavy burden to receiving devices. To eliminate this burden, we propose the polarization-based modulation, which is flicker-free, to enable a low pulse rate VLC. In this way, we make VLP light-weight enough even for resource-constrained wearable devices, e.g. smart glasses. Moreover, the polarization-based VLC can be applied to any illuminating light sources, thereby eliminating the dependency on LED. This paper presents the VLP system PIXEL, which realizes our idea. In PIXEL, we develop three techniques, each of which addresses a design challenge: 1) a novel color-based modulation scheme to handle users? mobility, 2) an adaptive downsampling algorithm to tackle the uneven sampling problem of wearables? low-cost camera and 3) a computational optimization method for the positioning algorithm to enable real-time processing. We implement PIXEL?s hardware using commodity components and develop a software program for both smartphone and Google glass. Our experiments based on the prototype show that PIXEL can provide accurate realtime VLP for wearables and smartphones with camera resolution as coarse as 60 pixel x 80 pixel and CPU frequency as low as 300MHz. <s> BIB003
With the single view geometry approach, using collinearity condition equations might be the most straightforward way to obtain the position of the camera. Given the world coordinates of mobile stations, one just needs to process the image to obtain the image coordinates of base stations and formulate collinearity equations like Equation BIB001 . When the number of equations is sufficient, the world coordinate of the camera can be determined within one step. Some studies, however, might convert the geometric relations into other representations and use them to determine the position of the camera through a number of steps. For example,  BIB003 determined the world coordinate of the camera in two steps. Firstly, the camera coordinates (x , y , z ) of base stations were determined based on scaling factors. Then the equations of the camera transformation were solved to determine the world coordinate of the camera. Suppose a point has the world coordinates (X, Y, Z), camera coordinates (x , y , z ), and image coordinates (x, y). A scaling factor k expresses the relation between the camera coordinates and the image coordinates as: where f is the focal length of the lens. Suppose that (X 1 , Y 1 , Z 1 ), (x 1 , y 1 , z 1 ) and (x 1 , y 1 ) are respectively the world coordinate, the camera coordinate, and the image coordinate of point P. Similarly, (X 2 , Y 2 , Z 2 ), (x 2 , y 2 , z 2 ) and (x 2 , y 2 ) are respectively the world coordinate, camera coordinate, and image coordinate of Q. The pairwise distance between P and Q , denoted as d 2 PQ , can be written as follows: With three or more equations like Equation (24), the scaling factors can be found and then the camera coordinates of base stations are determined based on Equation BIB002 . From camera coordinates and world coordinates of base stations, multiple camera transformation equations like Equation (17) are formulated. Then the least squares method can be used to determine unknown parameters, including the translation vector t, which is also the world coordinate of the camera. In  , multiple LED base stations transmitted their world coordinates to the camera in a smart phone. The signals from different LEDs were modulated in OOK at different frequency. Then the rolling shutter effect of the camera was utilized to receive the light signal. The experiment results showed that the accuracy was within 10 cm at 90% of the tested locations. A light-weight indoor positioning system for a wearable device was proposed in BIB003 . Using the proposed polarization based modulation, a light signal can be modulated at low pulse rate without causing flicker. Therefore, any illuminating light sources, not necessarily LEDs, can be used for transmitting signals. In the experiment, fluorescent lamps were used to transmit beacon signals containing their world coordinates. The camera on an Android smart-phone was used as the receiver. Such necessary information as the focal length and the size of the image sensor was obtained from the EXIF of the image. The world coordinate of the camera, which is also the position of the smart phone, was determined based on a geometric scaling factor. The results showed that the distance errors of less than 30 cm were obtained in 90% of the test cases.