# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 2 Literature Review
### LoRa Localization  
Due to LoRa's characteristics of low power consumption, low cost, wide coverage, and large connection, many researchers have applied LoRa to location-based tasks which are not sensitive to time delay. Muller et al. [19] has made statistics on the ranging error of the LoRa signal and pointed out that LoRa has the ranging capability within kilometers. A comprehensive characterization of the LoRa signal and an evaluation of its real location service is presented in reference [20]. On this basis, a series of positioning methods based on LoRa are proposed, which can be roughly divided into two categories: Time Difference of Arrival (TDOA) based positioning methods and RSSI based positioning methods. The principle of the TDOA-based positioning method is to determine the location of a device by measuring the time difference between the arrival of the same signal at multiple gateways. There are currently three main methods for extracting LoRa signal timestamps: (1) reading timestamps provided by LoRaWAN gateways, (2) estimating arrival times in the frequency domain, and (3) estimating arrival times in the time domain. However, due to the 1-microsecond resolution of current LoRa hardware and software stacks, these methods can only achieve microsecond-level timestamp precision. As a result, their positioning performance is relatively poor since radio signals can propagate approximately 300 m in 1 microsecond in free space, making these methods insufficient for precise localization. Many TDOA-based LoRa positioning systems rely on timestamps provided by LoRa gateways, leading to suboptimal positioning accuracy [21; 22; 23]. On this basis, Lam et al. [24] remove bad anchor nodes or selects well-performing anchor nodes in the positioning process to reduce the impact of non-Gaussian noise on the LoRa network, and proves that the positioning accuracy of the LoRa system can be comparable to that of the GNSS with a sufficient number of anchor nodes. In order to improve the precision of LoRa positioning, some researchers have utilized highly customized LoRa devices to achieve nanosecond-level timestamp accuracy, thereby achieving meter-level accuracy in TDOA positioning [25]. However, considering cost constraints and the need for widespread adoption, TDOA positioning systems developed based on generic LoRa hardware still cannot address the aforementioned challenges. Different from TDOA-based methods, the principle of the Received Signal Strength Indication (RSSI)-based localization method is to establish a path loss model based on the measured RSSI at different distances from the signal source. In the RSSI-based ranging algorithm, nodes use a known signal propagation model to characterize the relationship between RSSI and distance. They estimate the distance to the signal source based on RSSI measurements. Finally, by utilizing distance information obtained from multiple positioning anchor nodes, the coordinates of the target node are calculated [26]. Many RSSI-based LoRa positioning products have been developed [26; 27; 28; 29; 30]. To improve positioning accuracy, researchers have conducted studies in areas such as correcting the RSSI path loss model, mitigating multipath interference, and noise suppression. Regarding path loss model correction, He et al. [31] model RSSI in indoor and outdoor environments using Gaussian processes and then develop a localization method through maximum likelihood estimation. In reference [32], terrain parameters are introduced to correct the accuracy of the traditional RSSI model. In reference [33], DeepLoRa employs remote sensing to automatically identify land cover types along LoRa links, and then utilizes deep learning to estimate the path loss in complex environments. In reference [34], SateLoc uses satellite images to recognize land cover types and generates virtual fingerprint databases for each gateway based on the path loss parameters corresponding to different land cover types, enabling positioning through fingerprint matching.