| Summary: | We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.
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