Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning

Abstract

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.

A Robust Steered Response Power Localization Method for Wireless Acoustic Sensor Networks in an Outdoor Environment

“…For a spatial point x such that x = ∞, let K ∈ N be the total number of sensor pairs c p such that x ∈ Λ p τ 0 p (x s ), T R (a s ) . According to Equation (15) and Inequality (22), it follows that…”

“…Particularly, WASN-based sound source localization has captured researchers’ attention in the last two decades [ 1 , 2 , 3 , 4 , 5 ]. The existing methods available for passive source localization in WASNs include (1) the received energy-based approaches [ 6 , 7 , 8 , 9 ]; (2) the direction of arrival (DOA)-based approaches [ 10 , 11 ]; (3) the time of arrival (TOA)-based approaches [ 12 ]; (4) the time difference of arrival (TDOA)-based approaches [ 13 , 14 , 15 ] and (5) the steered response power (SRP)-based approaches [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Comparison of Direct Intersection and Sonogram Methods for Acoustic Indoor Localization of Persons

“…The former offers low-complexity systems with cheap hardware [ 14 , 15 ], whereas with the latter one, higher accuracy may be achieved [ 16 ]. The other main concept employed in indoor presence detection is using ultrasonic waves, which are applied in active trackers indoors [ 17 , 18 ] and even underwater [ 19 , 20 ]. An entirely passive approach, as in [ 21 ], generally analyzes audible frequencies, which can include speech and potentially violate privacy regulations, similar to vision-based approaches.…”

A performance-based tabular approach for joint systematic improvement of risk control and resilience applied to telecommunication grid, gas network, and ultrasound localization system

“…The third example case is an indoor ultrasound localization system (Bordoy et al 2020) (Ens et al 2015) (Hoeflinger et al 2015), which offers high localization accuracy when compared to alternative technologies, e.g., Wi-Fi-based fingerprinting approaches (Tiku et al 2020), Bluetooth, ZigBee, Ultra Wide Band (UWB), vision and acoustic-based (Zafari et al 2019). It localizes ultrasound transmitters on objects (e.g., goods, transport systems, robots) using receivers on the ceiling by application of time difference of arrival algorithms (TDOA) for the case of known receiver positions.…”