2 Department of Electrical and Computer Engineering, University of Wisconsin, Madison. ABSTRACT. Acoustic signatures of aircraft engine and propeller noise.
TARGET TRACKING USING ACOUSTIC SIGNATURES OF LIGHT-WEIGHT AIRCRAFT PROPELLER NOISE Jianfei Tong1, Yu-Hen Hu2, Fellow, IEEE, Ming Bao1, Wei Xie1 1 Institute of Acoustics, Chinese Academy of Sciences 2 Department of Electrical and Computer Engineering, University of Wisconsin, Madison experiment setup and parameters are reported. Experiment results and discussions are reported in sections 4 and 5. This is followed by conclusion in section 6.
ABSTRACT Acoustic signatures of aircraft engine and propeller noise are investigated in the context of target tracking. Harmonics in spectrograms of light airplanes collected by a stationary microphone array on the ground are analyzed. Using a model of Doppler frequency shifts, the target speed and height may be estimated from the spectrogram of recorded acoustic signal. These initial results highlight the potential of using the acoustic signatures for precision moving target detection and tracking.
2. BACKGROUND
z c v, f 0
Index Terms— acoustic signature, STFT spectrum, Doppler model frequency, Destructive–Interference frequency
R
1. INTRODUCTION Acoustical signature of light-weight, unmanned aircrafts contains rich information about both engines and motion parameters. It has been widely used for condition monitoring and fault diagnosis. In [1], [2] the independent component analysis (ICA) is applied to identify the engine noise sources. In [3], acoustic emission (AE) measurements are presented to analyze the tribological property in engines. In [4], rotating microphones are used to measure the inlet of the Advanced Ducted Propeller (ADP). In [5], the visual dot pattern is used for fault diagnosis in internal combustion engines. Acoustic signals of aircraft which is measured by groundbased sensors can be applied to detect and identify targets [6-8]. Narrowband tones in the acoustic spectrogram reveals structural details of an aircraft such as the number of blades, cylinders, strokes and blade passage frequency [9], [10]. Using the model of the Doppler Effect [11], the parameters, such as speed and height are estimated based on observed spectrogram of the acoustic signal. It is further extended to consider the effect of multipath delay [12][13]. Built-on the earlier results, in this work, two goals are sought: First, we conducted experiment and develop signal processing algorithms to estimate the dynamic parameters of flying airplanes. In particular, a nonlinear least squares method is proposed. Second, we investigate the impact of destructive frequency interference due to multi-path propagation of acoustic signals. This paper is organized as follows. In section 2, the equations for estimating the Doppler Effect and Destructive Interference frequency effect are derived. In section 3, the
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CPA Rc
ht
y
dc x
Figure 1. Geometry of flying path and Doppler Effect model
Referring to Fig. 1, at time instant , a narrowband acoustic signal is emitted by a moving source (say, a unmanned aerial vehicle, UAV) traversing at a constant speed v. The sound propagation speed in the air is denoted by c (~343.2 m/sec). At the closest point of approach (CPA), the height of the aircraft is denoted by ht, the sensor to target distance is denoted by Rc, and the horizontal ground range at the CPA is dc. Doppler Effect model [11] Let R() be the source to sensor distance at , then
t R c
(1)
Let c be the time when the target is at the CPA, clearly 2 R Rc2 v 2 c .
(2)
Substitute eq. (1) into eq. (2) and solve for , one has
c
c 2 t c Rc2 c 2 v 2 c 2v 2 t c c2 v2
2
(3)
Now that the instantaneous frequency is given by
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ChinaSIP 2013
f f0
d dt
Frequency destructive interference occurs when the phase difference between the received acoustic signal from these two paths equals to an odd multiple of : That is, R Rd (8) 2 f n t r 2n 1 c So the nth frequency destructive interference at time t may be expressed as:
(4)
where f0 is the origin frequency. Substituting eq. (3) into eq. (4), f may be solved as v2 t c f c2 (5) f t 2 0 2 1 2 2 2 2 2 2 c v Rc c v c v t c In eq. (5), f(t) is expressed as a function of f0, |v|, Rc, andc. Given these values, one may predict the time variations of the harmonic lines as will be shown in section 5. On the other hand, based on the estimated values of f(t), and known parameter values such as c, one may also estimate the unknown parameters using nonlinear least square fit [11] or other statistical estimation methods such as maximum likelihood estimates.
fn t
(9)
Substituting eqs. (1) and (3) into eq. (9), we have 2n 1 c 2 fn t 4 c2 v2
Rc2 c 2 v 2 c 2v 2 t c v 2 t c 2
(10)
hr ht
Differentiating fn(t) against t and set it to 0, it can be shown that the minimum of fn(t) occurs at t* = c + R(c)/c and the corresponding fn(t*) is 2n 1 c Rc (11) f n t * 4 hr ht
Multi-path Frequency Destructive Interference Model
CPA v c
2n 1 c R 4 hr ht
3. EXPERIMENT SETUP
dc
Source
Rd
ht
ht hr
R Rr
Sensor hr
O hr Figure 3. The microphone array for data collection
Figure 2. Frequency Destructive Interference model
A ground-based stationary microphone array as shown in Fig. 3 was used to collect the acoustic signatures. The array is configured such that hr = 1.7 m for x and y directions, and microphones are placed at x, y and z directions with a spacing of 0.5 m. There are 6 microphones in the x direction, 6 in the y direction and 4 in the z direction. The acoustic sensors are pre-polarized free-field 1/2” microphone, with a dynamic range larger than 146dB, and covered with cylindrical sponge wind breakers. A SONY SIR-1000i recorder was used to sample the sound at a sampling rate of 48 KHz, with 16 bits resolution per sample. Four different airplanes were used in this experiment. The fundamental frequencies of the propeller sound while the planes were in stationary position are list in Table 1. The XT-912 and LB-3C are light airplanes, while the other two are unmanned aerial vehicles (UAVs). Since the airplanes
As illustrated in Fig. 2, the sensor array is placed at a height of hr, so the acoustic signal arrives at the sensor at time via a direct path and one or more ground-reflected path. If the phases of these multi-path signals cancel each other, frequency destructive interference will occur. Denote Rd() and Rr() respectively to be the lengths of the direct path and the ground-reflected path at time [12], one may write
Rd v 2 c dc2 ht hr 2
2
Rr v 2 c dc2 ht hr 2
2
Usually, ht >> hr. Hence
Rr Rd 2ht hr / R
(7)
Moreover, R dc2 ht2 v 2 c 2 , Rc dc2 ht2 .
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use different types of engine, operating at different speed, the measured fundamental frequencies are different.
frequencies of the acoustic signal attenuate faster over long distance. The wave-shaped variations of these horizontal harmonic lines are due to the Doppler Effect. An analysis will be provided in section 5.
Table 1-Fundamental Frequencies of Four Airplanes Type Fundamental frequency XT-912 64Hz LB-3C 81Hz UAV DH-II 112Hz UAV DH-III 87Hz
UAV DH-II instantaneous frequency 15 10 5 0
dB
Each type of airplanes flew over the microphone array multiple times, performing different kinds of maneuvers, including approaching, departing, turning left, and turning right. GPS (global positioning system) was placed on the airplanes and the plane’s trajectory was downloaded after it landed and used as the real trajectory.
-5 -10 -15 -20
4. RESULTS AND OBSERVATION
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Background & Engines idling noise Microphone array recorded background noise in the absence of any airplane. A typical segment of the STFT spectrogram of the background noise is depicted in Fig. 4 below. The two horizontal line structures indicate the persistent presence of harmonics locating at 24Hz and 198Hz. The harmonics are due to heavy machines operating in a construction plant about 2 km away. These background harmonics should be treated like radar clutter and removed before the data is analyzed.
0
100
200
300
400 500 600 Frequency/Hz
700
800
900
1000
Figure 5. Spectrum of an idling UAV DH-II engine
Figure 6. Spectrogram of a passing UAV DH-II
Figure 4. Spectrogram of the background noise
In Fig. 5, a time slice of the spectrogram of UAV DH-II is presented. A peak finding algorithm is applied first to estimate the harmonic frequencies in the spectrum as the location of these periodic peaks. Then these estimated harmonic frequencies are fitted into a linear function. The fundamental frequencies listed in Table 1 are estimated as the slopes of these fitted straight lines. Airplanes passing noise The spectrogram of a fly-over UAV DH-II is shown in Fig. 6. The corresponding Rc = 336 meters and hc = 280 meters. The horizontal harmonic lines are clearly visible. The higher order harmonic lines are shorter as the higher
Figure 7. Spectrogram of passing light plane LB-3C
In Fig. 7, the spectrogram when a light plane LB-3C flew past the array with Rc = 444 meters is depicted. It contains both the line spectral lines (dark color) and U-
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shaped patterns showing some broadband noise which may be due to vibration of the wings or other engine related noise sources. The U-shaped light pattern is the destructiveinterference frequencies varying with time. Also note that the dark harmonic frequency lines are not continuous, becoming thinner or broken at those U-shaped light regions. We suspect these are also due to frequency destructive interference. The spectrograms of UAV DH-III are similar to the DHII, and those of the delta wing plane XT-912 are similar to the light plane LB-3C. Due to space limit, they are not included in this paper.
80 sec of the light plane LB-3C flight. Rc = 444 meters, ht = 337 meters, and v = 24.6 m/s. hr = 1.94 meters above the ground. The predicted frequency destructive interference lines are plotted overlaying the spectrogram and shown in Fig. 9. It can be seen that these solid curves fit into the light curves in the spectrogram very well. Hence, it is validated that these white curves are due to frequency destructive interference. Deviations from the spectrogram curves may be due to inaccuracy of parameter estimation and other noise conditions.
5. DISCUSSION By searching peaks in instantaneous frequency spectrogram, the line structure can be extracted. In Fig. 8, three lines extracted from Fig. 6 are depicted. Signals fewer than 200 Hz are likely to be overwhelmed by background noise such as the wind gust and remote construction engine sound. So only the 3rd, 4th, and 5th harmonics are extracted. Using GPS-deduced values for parameters Rc, c, and v, the harmonic frequency variations due to the Doppler Effect may be predicted using eq. (5) and plotted in solid lines in Fig. 8. Doppler Frequencies Measured-Estimated 600
550
Figure 9. Frequency Destructive Interference curves and the Spectrogram of light plane LB-3C
estimated lines 5th harmonic
6. CONCLUSION Acoustic signals collected by a stationary microphone can be used for classify a passing airplane and estimate related parameters such as height, speed and fundamental frequency using the Doppler Effect model as well as the frequency destructive interference model. Field experiment results are reported and these models are validated using experimental data. Future works include integrating microphone array target detection, direction of arrival estimation and tracking into these models.
frequency/Hz
500
450
4th harmonic
400
350
300
3th harmonic
0
10
20
30
40 50 time/s
60
70
80
90
Figure 8. Doppler model frequencies & measured frequencies of UAV DH-II
7. REFERENCES [1] W. Li, F. Gu, A. D. Ball, A. Y. T. Leugn and C. E. Phipp, “A study of the noise from diesel engines using the independent component analysis”, Mechanical Systems and Signal Processing (2001) 15(6), 1165-1184
Flight parameters such as Rc, c, and v can be estimated using least squares method from the measured Doppler frequencies [11]. The three estimations are not the same, and details are listed in Table 2. In fact, the plane didn’t have an ideal constant speed and height: the acceleration may be the reason for the differences.
[2] A. Albarbar, F. Gu and A. D. Ball, “Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis”, Measurement (2010)
Table 2-measured parameters & best fitting parameters. 3th4th5thmeasured fitted fitted fitted 280.7m 263.0m 235.8m 282.5m Height 28.26m/s 26.34m/s 24.54m/s 22.1m/s Velocity
[3] R.M. Douglas, J.A. Steel, R.L. Reuben “A study of the tribological behavior of piston ring/cylinder liner interaction in diesel engines using acoustic emission”, Tribology International, vol. 39, pp. 1634-1642, 2006. [4] D. G. Hall, L. Heidelberg and K. Konno, “Acoustic Mode Measurements in the Inlet of a Model Turbofan Using a Continuously Rotating Rake: Data Collection/Analysis Techniques,” AIAA–93–0599, NASA TM–105936, January 1993.
Using parameter values measured from the on-board GPS data, the destructive-interference frequency lines can be predicted using eq. (10) over the duration from 27sec to
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[5] J.-D. Wu, Chao-Qin Chuang, “Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals”, NDT&E International, 38 (2005), pp. 605–614. [6] S. Sadasivan, M. Gurubasavaraj, and S. Ravi Sekar, "Acoustic signature of an unmanned air vehicle exploitation for aircraft localisation and parameter estimation." Defense Science Journal vol. 51, No. 3, 2002, pp. 279-284. [7] M. Cai, I. M. Sou, C. Layman, B. Bingham, and J. Allen, "Characterization of the acoustic signature of a small remotely operated vehicle for detection." Proc. OCEANS, pp. 1-7, 2010. [8] A. Arora, et. al, “A line in the sand: a wireless sensor network for target detection, classification, and tracking,” Computer Networks, vol. 46, 2004, pp. 605–634. [9] D. Chiang, W. Fishbein and D. Sheppard, “Acoustic Aircraft Detection Sensor”, Proc. Int’l conf. Security Technology , Ottawa Canada, 13-15 October 1993, pp. 127-133. [10] Richard O. Nielsen, “Acoustic Detection of Low Flying Aircraft”, IEEE Conference on Technologies for Homeland Security, 2009. HST ’09. [11] Kam W. Lo and Brian G. Ferguson, “Flight Path Estimation Using Frequency Measurements from a Wide Aperture Acoustic Array”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 2, April 2001, pp. 685-694 [12] Kam W. Lo, Stuart W. Perry, and Brian G. Ferguson (2002), “Aircraft Flight Parameter Estimation Using Acoustical Lloyd’s Mirror Effect”, IEEE Transactions on Aerospace and Electronic Systems, vol.38, No.1, Jan. 2002, pp. 137-150. [13] Kam W Lo, Brian G. Ferguson, Yujin Gao and A. Maguer, “Aircraft Flight Parameter Estimation Using Acoustic Multipath Delays” IEEE Transactions on Aerospace and Electronic Systems, vol.39, No.1, Jan 2003, pp.259-267
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