Signal processing methods for the detection & localization of acoustic sources

Harvey, Brendan (2018) Signal processing methods for the detection & localization of acoustic sources. Doctoral (PhD) thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (9MB)

Abstract

This thesis provides an in-depth examination of utilizing acoustic sensing to form the basis of a non-cooperative collision avoidance system for Unmanned Aerial Vehicles (UAVs). Technical challenges associated with the development of such a system in the areas of acoustics, kinematics, statistics, and digital signal processing are clearly identified, along with the requirements of such a system to be commercially viable. Theoretical developments in the areas of adaptive filtering, signal enhancement, signal detection, and source localization are proposed to overcome current limitations of the technology and ultimately establish a practical and viable sensing system. Each of the proposed methods were also evaluated using both computer generated and experimental data. A number of techniques to adaptively filter harmonic narrowband noise without using any reference signal or producing any phase distortions are proposed. These included: 1) A distortionless FIR notch filtering method via the use of a second-order IIR notch filter prototype, 2) A distortionless notch filtering method via the use of FIR Comb filters, and 3) Multichannel adaptive filtering methods for systems containing multiple harmonic noise sources. Several signal processing techniques to enhance the detection of continuous harmonic narrowband signals are proposed. These methods included: 1) A generalized spectral transform to exploit the periodic peak nature of harmonic signals in the frequency domain, 2) A series of processors which exploit the phase acceleration properties of continuous periodic signals, and 3) Modifications to the generalized coherence function for multichannel systems to include phase acceleration information. In addition to the proposed signal enhancement processors, Constant False Alarm Rate (CFAR) detection relationships for unknown signals residing in noise with fixed bandwidth regions and unknown properties are also provided. These include: 1) The establishment of distribution-free CFAR relationships for non-independent testing scenarios, 2) Development of a distribution-free CFAR detector through frequency tracking of consecutive windowed spectra, 3) Development of a Robust Binary Integration scheme to better facilitate the detection of non-stationary signals, 4) A CFAR-Enhanced Spectral Whitening technique to facilitate the accurate use of distribution-free CFAR detectors with non-identically distributed noise. An examination of the statistical and kinematic requirements to establish a reliable UAV collision avoidance system is also provided. This includes a brief analysis to determine minimum required detection probability rates, and the development of an analytical model to approximate minimum required detection distances. A beamforming method is proposed to enhance the localization accuracy of harmonic continuous source signals via the Steered Response Power (SRP) method. In addition, algorithms are developed to reduce computational loads associated with the SRP localization technique. These include a Crisscross Regional Contraction method, and an adaptive approach which utilizes the steepest ascent gradient search. In addition, it was also shown that by performing signal detection prior to beamforming, greatly reduced computational loads and increased localization accuracy can be obtained. Finally, a number of experiments were conducted to establish the overall viability of an acoustic-based collision avoidance system and verify the performance of the proposed signal processing techniques. These included: 1) The detection of a continuous ground-based stationary source from a moving fixed-wing UAV, 2) The detection and localization of a moving unmanned aircraft from a moving fixed-wing UAV, and 3) The detection and localization of a moving manned aircraft via a moving multi-rotor UAV. Based on the results obtained, it was found that both manned and unmanned aircraft were detected and localized with sufficient range and accuracy to avoid a collision. Thus, it was finally concluded that acoustic sensing does in fact appear to be viable technology to establish a non-cooperative collision avoidance system for UAVs.

Actions (login required)

View Item