Enhancing TDE-based Drone DoA Estimation with Genetic Algorithms and Zero Cyclic Sum

Abstract

This paper discusses a way to enhance an acousticbased approach to obtaining the direction of arrival (DoA) of a drones ego noise using a microphone array. We focus on obtaining better time delay estimations (TDE) from a set of possible candidates. Recently, a large number of works have been put forward to detect and classify drones with different techniques. However, more investigation is required to tackle the drone DoA estimation problem using the time difference of arrival between pairs of microphones for the case of strongly corrupted audio signals, possibly by noise and multipath. The main problem in a complex acoustic environment is accurately estimating the time difference of arrival. With a traditional approach, this task becomes nearly impossible without the line of sight assumption, that is, whenever the highest cross-correlation peak between signals does not correspond to the delay between them. This paper uses genetic algorithms to search for the correct delays between pairs of microphones among a set of possible delays (primary and secondary delays). We define a fitness function based on the concept of zero cyclic sum of closed loops, i.e., when forming a closed loop, the sum of all theoretical delays should equal zero. A drawback of closed loops is that incorrect delays may result in a zero-sum; we thus created a fitness function that considers all possible closed loops of a given array. We exploited different approaches to estimate the direction of arrival using the combination of genetic algorithms and zero cyclic sum. In our experiments, the method successfully found all correct delays in simulations, providing strong evidence of its effectiveness when a correct delay exists among multiple possible delays. Furthermore, in experimental trials, it significantly enhanced the number of correct delays detected, further validating its utility and potential in practical scenarios.

A Reduced Complexity Acoustic-Based 3D DoA Estimation with Zero Cyclic Sum

“…The research on acoustic-based drone DoA estimation carried out in [5] addresses the challenge of accurately estimating the DoA of a drone in acoustically complex environments using a seven-microphone array. The focus is on improving TDE from a set of time delay candidates, particularly when dealing with strongly corrupted audio signals affected by noise and multipath.…”

“…Encompassing critical domains such as defense, law enforcement, security [1], and surveillance [2], reliable and precise DoA estimation ensures safety, strategic decision-making [3], and operational effectiveness. Applications such as gunshot DoA estimation [4], drone DoA estimation [5], and automotive angle estimation [6] require highly accurate estimates for optimal functionality.…”

“…The generalized cross-correlation algorithm (GCC) [7] is a relevant technique that estimates DoA; one of its most prominent advantages is the reduced computational effort enabled by the time delay estimation (TDE) [5] between pairs of sensors. Utilizing the cross-correlation function of the signals, the GCC algorithm facilitates an accurate determination of DoA, making it a highly reliable tool in spatial signal processing.…”

“…Notably, its adaptability spans across various domains, including underwater acoustics [8], terrestrial acoustics [9], and radar [10] systems. The algorithm's remarkable capability to handle diverse signal types and its resilience in noisy environments depend on the precision of the TDE process [5].…”

“…Additionally, errors in the measurement systems, including calibration inconsistencies (attitude and geometry of the array) [14] or hardware imperfections, further contribute to inaccuracies in time delay estimation, subsequently impacting the precision of DoA calculations. The cumulative impact of these factors on the acoustic signals leads to multiple peaks within the cross-correlation [5], resulting in misleading time delay estimations when considering only the peak of the cross-correlations with the highest amplitude (primary peak). Consequently, this multitude of peaks affects the accuracy of DoA estimation.…”