# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 1 Introduction  
With the rapid development of the Internet of Things (IoT) and 5G, high-precision positioning has attracted much attention because of its widespread location-based services (LBS). Although the space-based global positioning system (GPS) has been widely used for high accuracy in outdoor scenarios, it is difficult to apply the common GPS-based localization system for indoor positioning systems because of the dependence on the line-of-sight (LOS) communication of radio signals [1]. High-precision positioning in urban canyons and indoor scenarios still face enormous challenges due to their complex environments with varying shapes and moving objects [2]. Recently, significant research efforts have been devoted to indoor positioning. Indoor positioning plays an important role in various LBS applications, such as emergency personal navigation [3], context awareness [4], health monitoring [5], indoor parking lots [6], and smart homes [7]. Wi-Fi [8], Bluetooth [9], ultra-wideband (UWB) systems [10], radio-frequency identification tags (RFID), and other technologies have been widely adopted for indoor positioning. In addition, typical ranging techniques based on time of arrival (ToA) [11], time of flight (ToF) [12], and time difference of arrival (TDoA) [13] have been proposed which utilize time ranging and signal information among base stations. In addition, angle of arrival (AoA) [14] and angle of departure (AoD) [15] adopt the positioning principle based on phase angle measurement to calculate the arrival angle of signals by using phase difference on different array elements, which further improve the positioning accuracy of close distance. Traditional indoor positioning techniques are highly dependent on high-precision time synchronization between base stations, and are often susceptible to non-line-of-sight (NLOS) errors, which makes it difficult to further improve the positioning accuracy. In recent years, the development of machine learning (ML) technology has brought new inspiration to many applications, such as face recognition, image processing, recommendation systems, speech recognition, and so on. Many traditional domains have been revolutionized by the embedding of deep learning (DL). For example, thanks to the advent of RCNN, end-to-end object detection can reach speeds of more than 20 FPS on 1080 Ti GPU [16]. Furthermore, the learning-based cost estimators can replace traditional cost estimators in the database community [17]. It was natural to think about how to maximize the use of deep learning to design a high-precision positioning model to achieve the same improvement effect. However, high-precision positioning depends on feature mapping from the amplitude, power, and channel difference of channel state information (CSI), which means that the typical DL model cannot be directly used in high-precision positioning. For example, although both the ResNet18[18] and GoogLeNet[19] models can achieve sub-meter accuracy, two issues remain: (1) the unique characteristics of the localization data, such as channel differences and feature mapping disconnection caused by noise, are not fully considered; (2) large network scale and a long training time mean that it is difficult to use these models to carry out practical applications [20]. To overcome the above drawbacks, [PERSON] et al. [20] proposed a novel deep learning model, named MPRI, inspired by the convolutional neural network Inception [21], which brought the average error of positioning precision to 0.28 m in indoor environments. [PERSON] et al. [22] adopted the orthogonal frequency division multiplexing (OFDM) multiple-input and -output techniques to select appropriate input characteristics from the channel response by eliminating the effects of multipath subcarriers. Then, high-precision positioning was achieved by using support vector machine (SVM)-based machine learning positioning prediction. However, the existing deep learning models still face the following challenges in the application of high-precision positioning:  
1.