This paper presents an experimental analysis of different solutions for improving the performance of BLE-based indoor localization systems. The study focuses on the use of Bluetooth Low Energy (BLE) 5.0 technology with the Received Signal Strength Indicator (RSSI) for distance estimation. The aim is to identify the most effective solution for enhancing the accuracy and reliability of BLE-based indoor localization. The analysis considers various techniques, including multichannel transmission, RSSI signal aggregation, filtering, distance estimation using empirical models or machine learning (ML), and numerical optimization or ML-based positioning approaches. The experimental campaign was conducted in a complex indoor environment with moving staff and numerous obstacles. The results show that the use of multichannel transmission with RSSI signal aggregation significantly improves the localization performance, reducing the positioning error from 1.5 m to about 1 m. Other solutions, such as RSSI filtering, distance estimation using empirical models, or ML-based approaches, showed a lesser impact on performance improvement, with positioning errors varying between 2% and 23% depending on the combination of solutions used. The analysis highlights the importance of using multiple transmission channels and aggregation techniques to enhance the accuracy of BLE-based indoor localization. The study also demonstrates that the use of ML-based approaches, such as neural networks, can provide better performance compared to traditional methods. The results indicate that the best performance is achieved when combining multichannel transmission with RSSI signal aggregation and ML-based approaches. The findings can help designers of indoor localization systems to choose the most suitable solutions based on their specific performance requirements. The methodology proposed in this paper can be applied to various indoor localization scenarios to achieve accurate and reliable positioning.This paper presents an experimental analysis of different solutions for improving the performance of BLE-based indoor localization systems. The study focuses on the use of Bluetooth Low Energy (BLE) 5.0 technology with the Received Signal Strength Indicator (RSSI) for distance estimation. The aim is to identify the most effective solution for enhancing the accuracy and reliability of BLE-based indoor localization. The analysis considers various techniques, including multichannel transmission, RSSI signal aggregation, filtering, distance estimation using empirical models or machine learning (ML), and numerical optimization or ML-based positioning approaches. The experimental campaign was conducted in a complex indoor environment with moving staff and numerous obstacles. The results show that the use of multichannel transmission with RSSI signal aggregation significantly improves the localization performance, reducing the positioning error from 1.5 m to about 1 m. Other solutions, such as RSSI filtering, distance estimation using empirical models, or ML-based approaches, showed a lesser impact on performance improvement, with positioning errors varying between 2% and 23% depending on the combination of solutions used. The analysis highlights the importance of using multiple transmission channels and aggregation techniques to enhance the accuracy of BLE-based indoor localization. The study also demonstrates that the use of ML-based approaches, such as neural networks, can provide better performance compared to traditional methods. The results indicate that the best performance is achieved when combining multichannel transmission with RSSI signal aggregation and ML-based approaches. The findings can help designers of indoor localization systems to choose the most suitable solutions based on their specific performance requirements. The methodology proposed in this paper can be applied to various indoor localization scenarios to achieve accurate and reliable positioning.