Broad banded acoustic vector sensors
for outdoor monitoring propeller driven aircraft
Hans-Elias de Bree1,2, Jelmer Wind1, Subramaniam Sadasivan3
1
2
Microflown Technologies, The Netherlands, Email: debree@microflown.com
HAN University, Department of Vehicle Acoustics, Arnhem, The Netherlands Email: HE.deBree@han.nl
3
Former Scientist – G, ADE, Bangalore, India Email:subramaniamsadasivan@hotmail.com
Introduction
Measurement case
Permanent and semi-permanent sound level measurements
are an increasingly common way to determine
environmental noise levels. If the specific causes of sound
could be identified, the measurements could be used to
improve noise insulation, to tax noise polluters, and to detect
possible threats. To achieve this goal, this article proposes
acoustic vector sensors. These sensors measure all three
components of the sound intensity and the pressure. This
system is able to detect and localize noise sources in the
entire frequency range. This article shows that these sensors
make it possible to classify the sources and also to estimate
their location.
Propeller driven aircraft flying at night-time can cause
nuisance in an urban environment. To be able to address a
complaint it is necessary to know what aircraft
(classification) was flying where and in what direction.
Traditionally, only sound pressure transducers were used in
acoustics. Directionality can be obtained by deploying a
number of spatially positioned sound pressure transducers.
The distance between the sensors determines the frequency
where such a system has optimal sensitivity.
For several years now, acoustic particle velocity sensors
have been commercially available. Acoustic vector sensors
can measure both the sound pressure and the 3D acoustic
particle velocity in a single point. Such a system has the
capabilities to detect noise sources across the entire audible
frequency range in a 3D space.
In this paper the detection, classification, localization and
tracking of propeller driven aircraft is investigated. The
acoustic vector sensor is able to determine the direction of
arrival (DOA) of the noise that is produced by the aircraft.
That information is combined with the Doppler curve, found
in the time-frequency representation of the signals, in order
to find the velocity, height and heading of the aircraft.
Acoustic Vector Sensors
An acoustic vector sensor (AVS) consists of a sound
pressure transducer (a microphone) and either two or three
orthogonally placed particle velocity sensors (Microflowns
[1]). If the AVS is placed at a rigid surface, i.e hard ground,
the three sensor version is used. This is because the particle
velocity normal to the rigid surface is zero. Thus, one AVS
has either three or four outputs. Acoustic vector sensors have
come to play an increasingly significant role with
applications focused on environmental monitoring, border
control, harbour protection, gunshot localization, and
situation awareness. As compared to sound pressure sensors,
acoustic vector sensors have advantages acoustic bandwidth,
reduced system size, low data transmission between nodes,
and set up times. These benefits have led to a steep increase
in practical usefulness of AVS with adoption of simple but
powerful algorithms [2], [3], [4], [5], [6], [7].
The acoustic detection, classification, localization and
tracking of aircraft are checked with a Mode-S receiver that
decodes the radio signals that are transmitted by most
aircraft. This cross check ensures the robustness of the
algorithms, finds the aircrafts without Mode-S transponders
and it is now possible to quantify the acoustic noise each
individual plane produces on the ground: contribution of
other sources (cars etc.) are excluded.
An algorithm has been developed that discriminates between
moving and non-moving (static) sources. This algorithm is
used to filter static acoustic sources. Other sources such as
cars and trucks are also acoustic sources that move. These
sources are also detected, classified and discarded on the
basis of their speed, sound level and altitude.
Outdoor monitoring system
An acoustic vector system is placed outdoors, measuring
three acoustic channels with a sample frequency of 48kHz at
a 16 bit resolution. The AVS is geo-referenced with a
built-in GPS for localization, a 3D magnetic compass and a
3D accelerometer for orientation.
The AVS module is connected by a cable to the power
module; a watertight box that includes a 12V battery, a 4
channel data acquisition and an ultra miniature PC with
wireless (WIFI) access. The power module measures
25×18×11cm and 18×18×11cm for the AVS module,
comprising the AVS and Mode-S receiver.
Figure 1: Working prototype of the outdoor AVS
measurement system (Velp, NL).
Detection of a tonal acoustic source
Detection is the first step in the procedure to find propeller
driven airplanes. The use of an acoustic vector sensor is
beneficial compared to a sound pressure microphone. The
relevant parameters (such as heading of aircraft) are
measured in a straightforward manner from the signals of the
acoustic vector sensor. The cross spectrum (the real part
being the sound intensity) for example can be used as the
input signal for the detection.
A better conditioned input signal leads to an increased
detection range and less misdetection.
The data rate of the measurement system is high: 23Mb per
minute. The expectation is that there are only a few planes
per night so the first step of the processing is the detection of
a possible aircraft. It is also possible to power up the system
with a trigger signal. This will be included for production
versions but has not yet been integrated at this stage of
R&D.
A propeller driven aircraft has an acoustic signature that has
a tonal character in a specific frequency band. This feature is
used to detect potential aircraft. Other sources with a tonal
character such as cars, motorbikes, trucks, etc. are also
detected. These misdetections are discarded in the
classification algorithm.
Doppler algorithm: Example I
A well conditioned and well described dataset is used to be
able to check the Doppler algorithm and to verify a novel
Doppler/DOA algorithm [4]. In the following examples
more complex scenario’s are described also.
The Doppler frequency shift is a known phenomenon. When
a sound source is approaching a listener the sound is of a
higher frequency than if a source is moving away from the
listener. In the time-frequency representation of the sound
pressure this effect can be seen clearly. The signals of a three
channel AVS caused by a low flying propeller driven aircraft
are shown in the time-frequency representation in Figure 2.
The upper graph shows the sound pressure signal below that
the two lateral velocity signals. The harmonics of the
propeller can be clearly seen as horizontal lines. The
harmonics lower in frequency at 15 seconds due to the
Doppler effect: the aircraft is passing.
In general the sound pressure signal is cluttered with
reflections, this is less observed with the velocity signals (in
this example it is not seen very clearly). The sound pressure
and particle velocity signals are correlated and are much
better conditioned than the sound pressure signal alone. This
is advantageous for the signal processing as it makes it more
robust.
With the equation of the Doppler shift: a) the closest point of
aircraft the aircraft path; b) the true source acoustic
frequency (to be able to identify it); and c) the speed of the
aircraft, can all be determined.
The acoustic (sound pressure) signal is not directional and it
is therefore not possible to determine the heading of the
aircraft.
Figure 2: Measurement location (Teuge, NL). Time
frequency visualization of the sound emitted by a low
flying propeller driven aircraft. Upper: sound pressure.
Middle and lower: lateral particle velocity, from [4].
Algorithms in relation with Doppler and
acoustic vector sensors: Doppler/DOA
It is not easy to curve-fit the Doppler shift from the sound
pressure signals only. It is necessary to obtain both the
highest and lowest frequencies of the Doppler shift to find a
robust fitting algorithm. However, it is not possible to
measure these frequencies because the sound source
(airplane) is (in theory) infinitely far away, thus the signal to
noise ratio at the measurement location is poor.
Enhancement in relation with Doppler/DOA
Detection and classification is required to be able to extract
relevant information out of acoustic signals. Detection is a
first step in filtering algorithms.
The Doppler shift is measured three times concurrently with
the AVS, (lateral particle velocities and sound pressure).
With signal processing (e.g. cross spectral techniques) the
signal to noise ratio increases. And of course, a clear signal
increases the accuracy of any algorithm. This also increases
the detection range.
With an AVS the direction of arrival (DOA) from the source
sound wave is directly obtained, see the upper graph of
Figure 3. So the DOA of the aircraft is known regardless of
the Doppler frequency shift. This is a different set of
physical data (frequency for the Doppler versus signal
strength ratio for the DOA algorithm) describing the speed
of the aircraft combination with the aircraft-AVS distance.
An algorithm which uses the combination of the Doppler
information and the DOA information is more robust than an
algorithm based simply on Doppler information alone.
The moment when the time derivative of the angle is
maximal is the closest point of the aircraft to the AVS along
its trajectory, see lower graph of Figure 3. This timestamp is
very important for the Doppler fitting algorithm and can thus
also be derived from the angle (DOA) measurement.
The heading of the aircraft is directly obtained: it is
perpendicular to the angle measured at the moment of the
closest point of the aircraft trajectory.
N
W
E
S
Figure 4: The DOA as a function of time (obtained from
the data shown in Figure 3). Upper: raw data, middle:
filtered response. Lower: the colour represents the
angle; the brightness of the colour indicates the signal
strength.
The detected signal is analyzed and checked for a Doppler
shift and a variation in the DOA. If these are not found in a
reasonable time it can be concluded that the signal is from a
static, i.e. non-moving source (e.g. a generator switched on).
As can be seen in Figure 2, a continuous tone is found at
approximately 115Hz coming from W-SW. A result of the
static source filter is shown in Figure 4 (middle). As can be
seen, the sources with a constant frequency (and angle) are
removed to a large extent.
In situ auto-calibration of the AVS
Figure 3 Upper: the DOA as function of time (obtained
from the data shown in Figure 3). Lower: the time
derivative of the fitted angle. The maximum of this
value determines the heading of the aircraft directly.
Asymptotically the DOA of a passing aircraft converges to a
180 degree rotation and at these limits elevation is zero.
These properties can be used to calibrate AVS in situ. A
passing car has zero elevation and reaches a 180 degree
rotation while the spectrum of the signal is broad banded. A
passing car therefore can be suitable for in situ calibration.
Classification of a propeller driven aircraft
DOA Border control: Example I
Once a source is detected it is analyzed in the time frequency
domain. From the Doppler/DOA algorithm the speed, the
closest point of aircraft and the heading of the source aircraft
are determined. The speed, distance and duration of the
signal is used for classification. A passing car can have a
speed in the order of e.g. a low flying aircraft at takeoff but
the duration of such signal is very short. In other words, a
plane makes a lot more noise than a car and can therefore be
detected for a longer time duration.
The AVS can be used for border control. With two lateral
velocity probes and a collocated sound pressure microphone
it is possible to determine the sound intensity in any
direction [8]. If the source passes an AVS border control
sensor, the sign of the intensity perpendicular to the border
flips [4], see Figure 6. This feature provides direct tactical
information.
Figure 6: The intensity flips in sign if an aircraft passes
a preset border [4] (obtained from the data shown in
Figure 3). The upper curve represents the positive
intensity and the lower curve the negative intensity.
Elevation of the aircraft: Example II, Helicopter
There are three independent methods of calculating the
elevation of an aircraft. Firstly, one can compare the output
of the particle velocity sensors to the sound pressure
element. This idea is presented in [9] and is explained below.
Figure 5: The Doppler/DOA algorithm is used to find
the speed, heading and the closest point of aircraft.
Upper: the time-frequency representation. Lower: the
time-frequency-DOA representation.
In this example an aircraft at take-off is used, this is a data
set with a good signal to noise ratio (the same data set as
reported in [4]).
The speed of aircraft, from the Doppler algorithm, is found
to be: 116km/h and the closest point of the aircraft is 125m.
If the Doppler/DOA algorithm is used a speed is found:
121km/h; and the distance is calculated to be 121m.
The values of both algorithms are nearly identical indicating
that the new Doppler/DOA is in agreement with the Doppler
algorithm as reported in [4].
Combining the output of the Doppler and DOA algorithms
however produces more (relevant) information about the
aircraft: the heading and height of the aircraft.
1) The particle velocity sensors of the acoustic vector sensor
system have a figure-of-eight directionality. The lateral
velocity sensors have a maximal sensitivity parallel to the
ground and zero sensitivity normal to the ground. The sound
pressure element on the other hand is omni-directional. The
sensitivity parallel to the ground is therefore the same as the
sensitivity normal to the ground. The transfer function
between the sound pressure element and the particle velocity
sensors therefore provides a direct determination for the
elevation.
For lower elevation angles the normalized sensitivity of the
velocity probe is almost equal to the sound pressure element.
For larger elevation angles (say, more than 30 degrees) the
normalized sensitivity of the velocity probe is considerably
less than the sound pressure element. Therefore, the
measurement of lower elevation angles is sensitive. The insitu auto calibration procedure is therefore helpful.
2) Another technique to estimate the elevation is to compare
the Doppler curve and the DOA curve. The first curve
provides the 3D closest point of the aircraft and the latter
gives the 2D closest point of aircraft. The elevation can then
be derived out of this.
3) Another idea that is reported in [4] is to use the sound
level of the aircraft. The sound level should roughly fall 6dB
each time as the source-to-AVS distance doubles. This
property, in combination with the Doppler/DOA algorithm,
can be used to roughly extract the elevation of the aircraft.
Example III: Propeller driven aircraft
measured in very unfavourable conditions
The previous example was well conditioned and used to
verify the algorithms. As a more challenging example an
aircraft is detected during a measurement on a shooting
range (shooting noise mainly south west, SW and NW). A
truck with idling engine was located 40m SW from the
measurement location. The measurement setup is located in
a corridor through dense forest.
Figure 7 Upper: time-frequency-DOA representation of
a helicopter. Lower: the angle derived out of the broad
band signal.
In the previous example the plane is taking off and the DOA
has a low elevation. In this paragraph a helicopter is passing
closely so the elevation of the DOA is high.
In Figure 7 the time-frequency-DOA representation of the
passing helicopter is shown. The spectrum is tonal and broad
banded. The purple lines in 0-17s, 50-200Hz (continued to
42s) are caused by a propeller plane waiting to takeoff.
From the Doppler signal, a velocity of 83km/h, a closest
passing distance of 92m, a heading of 6 degrees and an
elevation of 89m are calculated.
Using the sound level
In this paper the measured sound level is not used. The
property is however useful [4].
With the sound level one can determine the relative change
in distance: if the sound level is increased 6dB the distance
is halved. This is true under the assumption that the source is
not directional and there are no reflective surfaces such as
buildings.
In practice it can be assumed that a plane has a directional
sound radiation. Hence it is possible to extract this
directionality.
The sound level can be used to determine if an aircraft is
approaching after detection. And once the aircraft is passed
it is possible to give a real time check if the aircraft is
following the (out of the Doppler/DOA) predicted track.
From the Doppler algorithm one can derive the closest point
of aircraft and the speed of aircraft. If the Doppler algorithm
cannot be used (e.g. for a jet aircraft, a passing car), a
combination of the sound level and the DOA as function of
time can be used to estimate the elevation angle not to
extract the closest point of aircraft. So with DOA and sound
level the position of the aircraft cannot be determined.
Figure 8: Time-frequency-level representation of a
sound
pressure
signal;
time-frequency-DOA
representation of an AVS signal (no signal enhancement
processing); same but now with gunshots removed;
same but now non moving sources removed.
Some results are shown in Figure 8. In the upper graph the
sound pressure level is shown in a time-frequency
representation. Below that the time-frequency-DOA result of
an acoustic vector sensor is shown. In the time-frequencyDOA representation, the aircraft signal shows up clearly as
the blue and pink signal with the Doppler signature as it was
passing S-SE to N-NE. The green horizontal line at 110Hz is
the truck in idle and the vertical lines (mostly green coming
from the same direction as the truck) are gunshots.
In the graph below the gunshots are removed and in the
lowest graph the static sources are removed from the time
signals. The Doppler/DOA remains in noise with a
reasonable white spectrum. This signal is well conditioned.
The DOA measurement is inaccurate because of the sensor
location in the trench.
A Doppler analysis outcome is that the propeller driven
airplane has a speed of 355km/h and was passing (CPA) at
1616m distance.
Example IV: Passing car
A passing car is detected and processed. As can be seen in
Figure 9 (upper) most of the signal is found in the
100Hz-2kHz bandwidth and almost no Doppler shift is
noticed.
Only at low frequencies tonal components are found
however no Doppler curve could be fitted. With an audio
check it was found that the rpm of the passing car was not
constant: the car was accelerating.
This is an example where the Doppler algorithm can not be
applied but the DOA algorithm is not affected.
Conclusion
In this paper a standalone single outdoor acoustic vector
sensor system is presented. The system is used for the
detection and classification of low flying propeller driven
aircraft.
A novel Doppler/DOA (direction of arrival) algorithm is
presented. From the sound pressure based Doppler signal
analysis alone one can derive a) the speed of aircraft, b)
closest point of aircraft and c) the true source frequency.
With the novel Doppler/DOA one can derive:
a) speed of aircraft;
b) closest point of aircraft;
c) true source frequency;
d) the heading of the aircraft; and
e) the height/elevation of the aircraft.
The airplane elevation and especially the aircraft heading are
of course valuable information.
References
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Figure 9 upper from left to right: time-frequency-DOA
representation of a passing car (colour legend same as in
Figure 5). Middle: same but now in a 50Hz-100Hz
bandwidth. Right: angle from the broad band signal.
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