# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 2 Literature Review
### LoRa Localization
However, these methods still have significant errors or require auxiliary information, such as satellite remote sensing, making them less suitable for general deployment. In terms of suppressing multipath interference and noise, an improved RSSI ranging output algorithm based on Wienerfiltering is proposed, effectively reducing noise interference and achieving more accurate ranging and positioning [35]. To combat multipath interference, an amplitude-based anti-multipath method is proposed in reference [36], achieving 4.6 m accuracy in indoor settings, but requiring specially designed antennas. To avoid multipath interference, a large-scale outdoor fingerprint database is built in reference [37], enabling high-precision positioning, but facing challenges such as fingerprint aging and deployment migration. In reference [38], the authors achieve high-precision positioning with an error less than 10 m in a 50 m \(\times\) 50 m \(\times\) 22 m outdoor 3D space, combining RSSI fingerprints and path loss models. However, this method also requires extensive prior information for building the fingerprint database. Overall, RSSI-based LoRa positioning technology is susceptible to multipath effects, and its accuracy depends on the number and deployment locations of anchor nodes. The above-mentioned localization techniques are based on LoRa sub-GHz development, and their positioning accuracy is limited due to the restricted signal bandwidth. In recent years, a series of localization techniques based on LoRa 2.4 GHz have been developed, which utilize high-precision TOF ranging to reduce outdoor positioning errors to below 5 m [19; 39]. However, these LoRa 2.4 GHz-based localization techniques still struggle to overcome the interference caused by multipath effects. Currently, most of the research on LoRa positioning is based on a small number of LoRa positioning devices, resulting in limited coverage of outdoor areas. With the demand for outdoor wide-area positioning, a positioning system would require a large number of LoRa positioning devices, which should be interconnected wirelessly through the LoRa network. However, the application of LoRa networks has been mainly focused on data transmission, and the research on providing positioning services using the LoRa network is still immature. LoRaWAN, as the mainstream LoRa network technology, has been discussed by a few researchers for outdoor positioning systems. In reference [40], a LoRaWAN system consisting of 68 positioning devices was built, covering the city area of Antwerp. The positioning was achieved through fingerprinting, with an average positioning error of 398.4 m. The RSSI test dataset of this system has been made open-source, and some researchers used deep learning and fingerprinting methods to reduce the average positioning error to 381.8 m [41] and 340 m [42], respectively. In reference [43], a fusion of RSSI and timestamp data reduced the average error to 322.6 m using the same dataset. In experiments with a public outdoor LoRaWAN network in the Netherlands, a TDOA-based positioning error of 150 m is obtained [44]. Similarly, in the Netherlands' public LoRaWAN network experiment, an average positioning accuracy of 82 m is achieved using a TDOA-based method [23]. Furthermore, TDOA multipolarization positioning in a 2 \(\times\) 3 km area using four positioning devices is realized with an average error of about 100 m [21]. Additionally, experiments using four positioning devices in an open outdoor area of 340 m \(\times\) 340 m is conducted with an average error of 24.1 m [45]. Although the outdoor positioning accuracy based on LoRaWAN is continuously improving, the existing research's positioning precision still falls short of meeting the practical positioning tasks' requirements. This is because LoRaWAN's signal power is relatively low, making it susceptible to strong signal attenuation, resulting in significant system positioning errors [10].