# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 1 Introduction  
Bluetooth is one of the most widely used wireless communication technology standards in the world, with over 5.4 billion Bluetooth-enabled devices shipped in 2023 alone [1]. With the introduction of Bluetooth low energy (BLE) as a part of the Bluetooth 4.0 specification [2] in 2010, the applications of this communication standard have expanded into the Internet of Things (IoT), device networks, and location services. In recent years, the use of BLE devices has accelerated in location services like indoor navigation, asset tracking, point of interest information, and space utilization. A market study of Bluetooth estimates that there are approximately 200 million devices currently deployed for asset tracking and item finding solutions and expects the number to be doubled by 2027 [3]. These BLE devices, classified as Bluetooth beacons, perform proximity sensing by advertising their presence that is recorded by a scanner device like a smartphone or a tablet. Beacons are widely used to track lost items, and various manufacturers have implemented these devices, commonly known as Bluetooth tags. Some of the popular implementations include Tile(r) and Chipolo(r).  
Achieving the same results by localization of the Bluetooth signal has proven to be a challenge due to multipath fading. Some research has been carried out on estimating the DoA of the Bluetooth signal and on range. Techniques like trilateration, triangulation, andfingerprinting (reviewed in Section 2) have been used to identify the angle of arrival (AoA) of the Bluetooth signal. In some cases, researchers used an antenna array to calculate the difference in ToF to estimate the AoA in multipath fading environments. In other research works, multiple scanner devices were used to determine the location of the beacon.  
In this paper, we begin with a brief review of the related work in the field of Bluetooth signal localization focused on asset tracking and provide details on existing implementations of beacons for different localization applications in Section 2. The rest of the paper is organize as follows. In Section 3, we describe the problem statement in detail by considering the existing work conducted in this field. Section 4 provides details on the Bluetooth channel spectrum and features of a transmitted packet of BLE, such as advertising channels, RSS, etc. In Section 5, we discuss in detail the aspects of RSS measurements, evaluating the orientation and heading of the scanner device, data recording and denoising techniques, and measurement methods for estimation of the beacon's location. Section 6 describes the system design, including down-selection of the hardware used for the beacon and exploration of currently existing beacon implementations (e.g., iBeacon, Alt-beacons, Eddystone beacons, etc.) to adopt the one that supports our research. In Section 7, we show the results for sample experiments in a real-world scenario and provide data on the accuracy of our algorithm to identify the location of a beacon. Finally, the paper ends with a conclusion and potential further work in Section 8.  
Contributions of This ResearchIn summary, contributions of the research described in this paper include the following:  
* Provide a solution for estimating the DoA of a BLE device using a single and easily available measuring device such as a smartphone;
* Provide an algorithm to determine the DoA of a BLE device using RSS from the advertisement packet without channel ID information;
* Provide an alternative to the conventional way of asset tracking and item finding by locating BLE devices without the need for onboard beepers;
* Provide a new way of measurement using a single measuring device that gradually guides the user closer to the beacon.