Improving accuracy of an AoA-based Wi-Fi indoor localization using kalman filter

Indoor location-based system (Indoor LBS) has increasingly attracted attentions in research and industrial community in the recent years. However, the adoption of indoor LBS is still slow due to many obstacles, in particular, its low accuracy performance. Many real-world applications require challenging performance targets such as real-time operation, high accuracy, and energy efficiency. In order to meet the requirements, fast and accurate positioning methods are necessary. However, noise from interference and multipath in the indoor environment is one of the most important factors preventing accurate and stable positioning. Since it is difficult to make changes to the sensor technologies especially in the hardware, improving the accuracy of indoor positioning by removing noise from the positioning measurement offers an effective solution to the accuracy problem for indoor LBS. We propose a Kalman Filter method which could be applied to the measurements of the indoor LBS. The result from the experiment shows that the positioning accuracy has improved for over 40 percent for static positioning.