Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation

Abstract

Received Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance-based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons-based indoor positioning systems provide an easy and energy-efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in-depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real-time experimental analysis using BLE modules, there is an average 1.32-m position estimation error in the presence of 10-dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables

“…) is computed according to (14). A key point in the analysis of the IAR measurement as a random variable regards the effects of nonlinearity of (17) on the modeled input random variables (i.e., pseudoranges).…”

“…According to Section 2.3.1, asynchronous GNSS receivers provided observables and positioning data at different epochs depending on their acquisition, tracking, and Position Velocity and Time (PVT) computation algorithms. The time misalignment, ∆t, between the measurements epochs was hence compensated, according to (14). Detrended pseudorange measurements have been assumed as ergodic random processes; therefore, their variance was reliably estimated by means of a second-order discrete derivative [47].…”

“…Technologies such as Bluetooth [14][15][16], Ultra-Wide Band (UWB) [17], ZigBee [18], and Radio-frequency identification (RFID) [19] have attracted the attention of the research community for their capability of providing ranging measurements between users in dynamic contexts. These ranging technologies are based on Received Signal Strength Indicator (RSSI) or time-based measurements (e.g., time-of-arrival, time-difference-ofarrival, round-trip time) [3].…”

“…The results presented in this section were obtained by selecting output inter-receiver ranges characterized by a reasonable time difference ∆t between the measurements of the two collaborating receivers. By considering high values of ∆t, indeed the correction provided through (14) does not effectively compensate for the time-inconsistency of the data. Therefore, the maximum ∆t considered for the experiment was 50 ms, which guarantees a considerable margin for the misalignment of different high-rate positioning solutions provided by collaborating COTS receivers [48].…”

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

“…In the fingerprinting-based approaches, a radio map is generated and then a pattern-matching algorithm is used to match the RSSI patterns with already-stored RSSI patterns in the training set or database. These two types of traditional localization techniques have some limitations [9]. Their localization accuracy depend on environmental factors and the modeling of RSSI for distance estimation, which is a challenging task.…”