## 6 AI-Assisted Wireless Localization Technology
### Enhancing Performance Itself via Miscellaneous Methods  
In addition to the methods that solve limitations caused by external factors, there are also active research efforts to improve the performance of localization technologies themselves. Many attempts are possible, but mainly, methods using RSSI with various AI algorithms are being studied. If there is a sufficient RSSI dataset that can be used as training data, it may be possible to estimate the location of the device with simple classification tasks.  
First, there is a method using \(K\)-NN [39]. After obtaining RSSI fingerprinting data within the area to be located, the data are used as input to apply \(K\)-NN and complete the rough localization calculation based on the RSSI data received by the module to be located, the RSSI fingerprint database, and environmental noise parameters. This approach can improve accuracy by applying the Kalman filter.  
Next, the RF algorithm can be an appropriate solution in the case of classification [40]. This method labels the RSSI for multiple anchors communicating with the device and repeats the process of randomly selecting some of them as independent trials to create a training dataset. They are used as input for the RF algorithm. It can be confirmed that the approach using ensemble techniques shows better performance compared to other algorithms using a single classification process.  
While there are situations where the training dataset is sufficient as above, there are also cases where the training dataset is insufficient for using localization technology. In such cases, there are ways to supplement it. Among them, there is a method of estimating the location by generating virtual datasets based on GAN [41]. This method collects RSSI fingerprinting data from the existing training dataset and generates fake data based on real labeled collected data in order to boost localization performance. Accordingly, it can be confirmed that localization accuracy can be increased by generating fake data to supplement insufficient training datasets. In addition, there is a VAE [42] method of estimating location by utilizing video data instead of RSSI data. In other words, this approach uses image data obtained through a camera rather than a wireless signal. In this method, the VAE network is trained by database images. After this, the VAE network generates global features of the database images and queries images for subsequent image retrieval tasks. At the same time, local features of database images are applied to structure-from-motion (SFM) to reconstruct a 3D model. For the localization process, given a query image, this method generates the global features through the trained VAE network and subsequently performs a global retrieval to determine similar images in the database.  
Specifically in range-based methods, the distance measurement can have the entire above-mentioned limitations depending on the specifications of the system. Therefore, if the measured distance is appropriately processed through correction, it can improve the performance of localization. The method of the noise-learning-based denoising autoencoder, nIDAE for short [43], is an algorithm that focuses on noise learning to restore the original for input with added noise, and is one type of denoising autoencoder (DAE). Unlike existing methods that focus on finding the original for the input, it focuses on noise learning rather than the distribution of true distance, making it relatively simple and more efficient with low uncertainty in many scenarios. In localization technology, it is used for error correction through noise removal in the measured distance that appears as a mixed value of error factors, including the actual distance and obstacles.