Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

Image captions aim to generate human-like sentences that describe the image’s content. Recent developments in deep learning (DL) have made it possible to caption images for accurate descriptions and detailed expressions. However, since DL learns the relationship between images and captions, it constructs sentences based on commonly frequented words in the dataset. Although these generated sentences are highly accurate, they have low lexical diversity, unlike humans due to limited vocabulary. Therefore, in this paper, we propose a Part-Of-Speech (POS) guidance module and a multimodal-based image captioning model that determines the intensity of images and word sequences and generates sentences through POS to enhance the lexical diversity of DL. The proposed POS guidance module enables rich expression by controlling the information of images and sequences based on the predicted POS guidance to predict words. Then, the POS multimodal layer adds POS and output vector of Bi-LSTM using the multimodal layer to predict the next caption, considering the grammatical structure. We trained and tested the proposed model on the Flicker 30K and MS COCO datasets and compared them with current state-of-the-art studies. Also, we analyzed the lexical diversity of the caption model through the Type-Token Ratio (TTR) and confirmed that the proposed model generates sentences using several words.