Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods

Source motion via a time-frequency representation at a single sensor

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.

Small UAS Online Audio DOA Estimation and Real-Time Identification Using Machine Learning

The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system's efficacy for reliable UAS detection at distances exceeding 70 m.

Acoustic detection of unmanned aerial vehicles using biologically inspired vision processing

Robust detection of acoustically quiet, slow-moving, small unmanned aerial vehicles is challenging. A biologically inspired vision approach applied to the acoustic detection of unmanned aerial vehicles is proposed and demonstrated. The early vision system of insects significantly enhances signal-to-noise ratios in complex, cluttered, and low-light (noisy) scenes. Traditional time-frequency analysis allows acoustic signals to be visualized as images using spectrograms and correlograms. The signals of interest in these representations of acoustic signals, such as linearly related harmonics or broadband correlation peaks, essentially offer equivalence to meaningful image patterns immersed in noise. By applying a model of the photoreceptor stage of the hoverfly vision system, it is shown that the acoustic patterns can be enhanced and noise greatly suppressed. Compared with traditional narrowband and broadband techniques, the bio-inspired processing can extend the maximum detectable distance of the small and medium-sized unmanned aerial vehicles by between 30% and 50%, while simultaneously increasing the accuracy of flight parameter and trajectory estimations.

Doppler chirplet transform for the velocity estimation of a fast moving acoustic source of discrete tones

An extended chirplet transform method termed as Doppler chirplet transform is proposed to estimate the velocity of a discrete tone source in uniform linear motion. This method directly uses the relation of the observed instantaneous frequency to the source velocity as the kernel of the chirplet transform. It is tested on a set of 30-s truck noise recordings and also on simulated data from a statistical perspective. The results show that the Doppler chirplet transform significantly reduces the run time that the polynomial chirplet transform [Xu, Yang, and Yu, J. Acoust. Soc. Am. 137(4), EL320-EL326 (2015)] costs to produce similarly accurate estimates of the source velocity.

Acoustic Detection of a Fixed-Wing UAV

The following paper presents results obtained from experiments conducted to investigate the viability of acoustic sensing to form the basis of a non-cooperative aircraft collision avoidance system. An unmanned aerial vehicle (UAV) fitted with two microphones was flown in the vicinity of another airborne UAV to determine the maximum distance at which the intruding aircraft could be detected. A two-dimensional analytical model to approximate the minimum detection distance required to facilitate an avoidance maneuver for a given spatial configuration is presented. A method to increase detection distances by exploiting the harmonic nature of acoustic signals generated by propeller-driven aircraft is also presented. The method significantly increases the detection distances compared to the commonly used incoherent spectral mean. It was found that a small gasoline-powered UAV could be detected at distances up to 678 m, which is more than double the minimum required to avoid a head-on collision.

Flight parameter estimation using instantaneous frequency and direction of arrival measurements from a single acoustic sensor node

When an airborne sound source travels past a stationary ground-based acoustic sensor node in a straight line at constant altitude and constant speed that is not much less than the speed of sound in air, the movement of the source during the propagation of the signal from the source to the sensor node (commonly referred to as the "retardation effect") enables the full set of flight parameters of the source to be estimated by measuring the direction of arrival (DOA) of the signal at the sensor node over a sufficiently long period of time. This paper studies the possibility of using instantaneous frequency (IF) measurements from the sensor node to improve the precision of the flight parameter estimates when the source spectrum contains a harmonic line of constant frequency. A simplified Cramer-Rao lower bound analysis shows that the standard deviations in the estimates of the flight parameters can be reduced when IF measurements are used together with DOA measurements. Two flight parameter estimation algorithms that utilize both IF and DOA measurements are described and their performances are evaluated using both simulated data and real data.

Flight parameter estimation using instantaneous frequency and time delay measurements from a three-element planar acoustic array

The acoustic signal emitted by a turbo-prop aircraft consists of a strong narrowband tone superimposed on a broadband random component. A ground-based three-element planar acoustic array can be used to estimate the full set of flight parameters of a turbo-prop aircraft in transit by measuring the time delay (TD) between the signal received at the reference sensor and the signal received at each of the other two sensors of the array over a sufficiently long period of time. This paper studies the possibility of using instantaneous frequency (IF) measurements from the reference sensor to improve the precision of the flight parameter estimates. A simplified Cramer-Rao lower bound analysis shows that the standard deviations in the estimates of the aircraft velocity and altitude can be greatly reduced when IF measurements are used together with TD measurements. Two flight parameter estimation algorithms that utilize both IF and TD measurements are formulated and their performances are evaluated using both simulated and real data.

Estimation of low-altitude moving target trajectory using single acoustic array

An acoustic-signature based method of estimating the flight trajectory of low-altitude flying aircraft that only requires a stationary microphone array is proposed. This method leverages the Doppler shifts of engine sound to estimate the closest point of approach distance, time, and speed. It also leverages the acoustic phase shift over the microphone array to estimate the direction of arrival of the target. Combining these parameters, this algorithm provides a total least square estimate of the target trajectory under the assumption of constant target height, direction, and speed. Analytical bounds of potential performance degradation due to noise are derived and the estimation error caused by signal propagation delay is analyzed, and both are verified with extensive simulation. The proposed algorithm is also validated by processing the data collected in field experiments.

The feasibility of unmanned aerial vehicle-based acoustic atmospheric tomography

A technique for remotely monitoring the near-surface air temperature and wind fields up to altitudes of 1 km is presented and examined. The technique proposes the measurement of sound spectra emitted by the engine of a small unmanned aerial vehicle using sensors located on the aircraft and the ground. By relating projected and observed Doppler shifts in frequency and converting them into effective sound speed values, two- and three-dimensional spatially varying atmospheric temperature and wind velocity fields may be reconstructed using tomography. The feasibility and usefulness of the technique relative to existing unmanned aerial vehicle-based meteorological techniques using simulation and trials is examined.

Flight parameter estimation using time delay and intersensor multipath delay measurements from a small aperture acoustic array

A ground-based acoustic sensor array can be used to estimate the complete set of flight parameters of a jet aircraft or other airborne source of broadband sound in transit by measuring the differential time of arrival (DTOA), or the time delay, of the direct path signal at each sensor pair of the array over a sufficiently long period of time. This paper studies the possibility of using intersensor multipath delay measurements to improve the precision of the flight parameter estimates for a small aperture array, without increasing the array's intersensor spacing or the observation time for more time delay measurements. Intersensor multipath delay is defined as the DTOA of the direct path signal at one sensor and the ground-reflected path signal at another sensor. The flight parameter estimation algorithm is formulated and a simplified Cramer-Rao lower bound error analysis is presented, which shows that the standard deviations in the flight parameter estimates are greatly reduced when intersensor multipath delay measurements are used together with time delay measurements. The effectiveness of the proposed flight parameter estimation method for small aperture arrays is verified using both simulated and real data.