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.

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