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Register a new userHigh Accuracy Visible Light Positioning Based on Multi-target Tracking Algorithm
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Added by
Wentao Yang
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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In this paper, we propose a multi-target image tracking algorithm based on continuously apative mean-shift (Cam-shift) and unscented Kalman filter. We improved the single-lamp tracking algorithm proposed in our previous work to multi-target tracking, and achieved better robustness in the case of occlusion, the real-time performance to complete one positioning and relatively high accuracy by dynamically adjusting the weights of the multi-target motion states. Our previous algorithm is limited to the analysis of tracking error. In this paper, the results of the tracking algorithm are evaluated with the tracking error we defined. Then combined with the double-lamp positioning algorithm, the real position of the terminal is calculated and evaluated with the positioning error we defined. Experiments show that the defined tracking error is 0.61cm and the defined positioning error for 3-D positioning is 3.29cm with the average processing time of 91.63ms per frame. Even if nearly half of the LED area is occluded, the tracking error remains at 5.25cm. All of this shows that the proposed visible light positioning (VLP) method can track multiple targets for positioning at the same time with good robustness, real-time performance and accuracy. In addition, the definition and analysis of tracking errors and positioning errors indicates the direction for future efforts to reduce errors.
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This paper presents an approach for visible light communication-based indoor positioning using compressed sensing. We consider a large number of light emitting diodes (LEDs) simultaneously transmitting their positional information and a user device equipped with a photo-diode. By casting the LED signal separation problem into an equivalent compressed sensing framework, the user device is able to detect the set of nearby LEDs using sparse signal recovery algorithms. From this set, and using proximity method, position estimation is proposed based on the concept that if signal separation is possible, then overlapping light beam regions lead to decrease in positioning error due to increase in the number of reference points. The proposed method is evaluated in a LED-illuminated large-scale indoor open-plan office space scenario. The positioning accuracy is compared against the positioning error lower bound of the proximity method, for various system parameters.
Visible Light Communication (VLC) technology using light emitting diodes (LEDs) has been gaining increasing attention in recent years as it is appealing for a wide range of applications such as indoor positioning. Orthogonal frequency division multiplexing (OFDM) has been applied to indoor wireless optical communications in order to mitigate the effect of multipath distortion of the optical channel as well as increasing data rate. In this paper, we investigate the indoor positioning accuracy of optical based OFDM techniques used in VLC systems. A positioning algorithm based on power attenuation is used to estimate the receiver coordinates. We further calculate the positioning errors in all the locations of a room and compare them with those of single carrier modulation scheme, i.e., on-off keying (OOK) modulation. We demonstrate that OFDM positioning system outperforms its conventional counterpart.
The capability to achieve high-precision positioning accuracy has been considered as one of the most critical requirements for vehicle-to-everything (V2X) services in the fifth-generation (5G) cellular networks. The non-line-of-sight (NLOS) connectivity, coverage, reliability requirements, the minimum number of available anchors, and bandwidth limitations are among the main challenges to achieve high accuracy in V2X services. This work provides an overview of the potential solutions to provide the new radio (NR) V2X users (UEs) with high positioning accuracy in the future 3GPP releases. In particular, we propose a novel selective positioning solution to dynamically switch between different positioning technologies to improve the overall positioning accuracy in NR V2X services, taking into account the locations of V2X UEs and the accuracy of the collected measurements. Furthermore, we use high-fidelity system-level simulations to evaluate the performance gains of fusing the positioning measurements from different technologies in NR V2X services. Our numerical results show that the proposed hybridized schemes achieve a positioning error $boldsymbol{leq}$ 3 m with $boldsymbol{approx}$ 76% availability compared to $boldsymbol{approx}$ 55% availability when traditional positioning methods are used. The numerical results also reveal a potential gain of $boldsymbol{approx}$ 56% after leveraging the road-side units (RSUs) to improve the tail of the UEs positioning error distribution, i.e., worst-case scenarios, in NR V2X services.
Visible light communication (VLC) has become a promising research topic in recent years, and finds its wide applications in indoor environments. Particularly, for location based services (LBS), visible light also provides a practical solution for indoor positioning. Multipath-induced dispersion is one of the major concerns for complex indoor environments. It affects not only the communication performance but also the positioning accuracy. In this paper, we investigate the impact of multipath reflections on the positioning accuracy of indoor VLC positioning systems. Combined Deterministic and Modified Monte Carlo (CDMMC) approach is applied to estimate the channel impulse response considering multipath reflections. Since the received signal strength (RSS) information is used for the positioning algorithm, the power distribution from one transmitter in a typical room configuration is first calculated. Then, the positioning accuracy in terms of root mean square error is obtained and analyzed.
Unmanned vehicles often need to locate targets with high precision during work. In the unmanned material handling workshop, the unmanned vehicle needs to perform high-precision pose estimation of the workpiece to accurately grasp the workpiece. In this context, this paper proposes a high-precision unmanned vehicle target positioning system based on binocular vision. The system uses a region-based stereo matching algorithm to obtain a disparity map, and uses the RANSAC algorithm to extract position and posture features, which achives the estimation of the position and attitude of a six-degree-of-freedom cylindrical workpiece. In order to verify the effect of the system, this paper collects the accuracy and calculation time of the output results of the cylinder in different poses. The experimental data shows that the position accuracy of the system is 0.61~1.17mm and the angular accuracy is 1.95~5.13{deg}, which can achieve better high-precision positioning effect.
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