In this paper, we propose a hybrid indoor localization scheme with image sensor-based visible light positioning and pedestrian dead reckoning. The architecture and algorithm of the proposed positioning scheme are analyzed. We build and demonstrate two different positioning prototypes with single/multiple cells, which are based on a commercial light-emitting diode lamp and off-the-shelf mobile phone equipped with an image sensor and inertial measurement unit. Experimental results show that the proposed visible light positioning scheme can achieve both cell recognition and three-dimensional positioning with a single captured image of a single light source, and the positioning error is only several centimeters. Moreover, with the proposed hybrid positioning algorithm consisting of visible light positioning and pedestrian dead reckoning, we achieved real-time decimeter-level indoor location tracking continuously and robustly with sparse light-source beacons in an actual indoor scenario.

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.

This contribution demonstrates the interaction between the fiber Bragg grating (FBG) and the ultrasound. When the grating length is much longer than the ultrasonic wavelength, the conventional wavelength-modulation method is invalid. In order to find out an effective sensing method in this case, a novel sidelobe-modulation has been proposed and investigated. When grating length is much longer than the ultrasonic wavelength, the Bragg wavelength is not shifted, however, the reflectivity of sidelobe is modulated by ultrasound, periodically, which can be utilized as an effective ultrasound sensing method. In order to optimize the performances of this method, the characteristics of the sidelobes are further analyzed for different ultrasonic displacement amplitude, frequency and grating length, respectively. The analysis results indicate that a proper choice of the grating length would enable the sidelobe-modulation to be utilized for detecting the ultrasound with appropriate range of ultrasonic frequency and amplitude. This method is preliminarily validated in the experiment, and the experimental results show that our proposed sidelobe-modulation method is effective to detect the 7.8 MHz ultrasonic wave by a simple intensity-demodulation using a 5 mm FBG, where the wavelength-modulation is invalid.