[논문]Bearing Estimation of Narrow Band Acoustic Signals Using Cardioid Beamforming Algorithm in Shallow Water

Chang, Duk-Hong; Park, Hong-Bae; Na, Young-Nam; Ryu, Jon-Ha

Bearing Estimation of Narrow Band Acoustic Signals Using Cardioid Beamforming Algorithm in Shallow Water 원문보기

The journal of the Acoustical Society of Korea, v.21 no.2E, 2002년, pp.71 - 80

Chang, Duk-Hong (Agency for Defense Development) , Park, Hong-Bae (School of Electrical Engineering & Computer Science, Kyungpook National University) , Na, Young-Nam (Agency for Defense Development) , Ryu, Jon-Ha (Agency for Defense Development)

Abstract ▼ AI-Helper

This paper suggests the Cardioid beamforming algorithm of the doublet sensors employing DIFAR (directional frequency analysis and recording) sensor signals in the frequency domain. The algorithm enables target bearing estimation using the signals from directional sensors. The algorithm verifies its applicability by successfully estimating bearings of a target projecting ten narrow-band signals in shallow water. The estimated bearings agree very well with those from GPS (global positioning system) data. Assuming the bearings from GPS data to be real values, the estimation errors are analyzed statistically. The histogram of estimation errors in each frequency have Gaussian shape, the mean and standard deviation dropping in the ranges -1.1°∼ 6.7°and 13.3∼43.6°, respectively. Estimation errors are caused by SNR (signal to noise ratio) degradation due to propagation loss between the source and receiver, daily fluctuating geo-magnetic fields, and non-stationary background noises. If multiple DIFAR systems are employed, in addition to bearing, range information could be estimated and finally localization or tracking of a target is possible.

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가설 설정

The variation with frequency is thought to be caused by some factors. First, the propagation loss, from the source to rece ver, is different with frequency. Particularly in shallow waters, the propagating waves inevitably interacts with the bottom, resulting high dependency on bottom characteristics (attenuation and reflection coefficients) and thus on frequency.

제안 방법

The resolution can be improved by interpolating between spatial bearings. On the contraiy, cross correlation method computes the time delay of incident wave on the separated two sensors and then estimates the bearings from the delay information.
This study suggests a reliable algorithm of the Cardioid beainfbrming which envies the bearing estimation adopting dipole sensors on the same axis. The Cardioid beamfbrmer has the maximum response of 6 dB on the MRA and the min mum at null point.
This study suggests a reliable algorithm of the Cardioid beainforming which envies the bearing estimation adopting dipole sensors on the same axis.

대상 데이터

The modified DIFAR system is deployed on the sea bottom of 40 m depth in the East sea of Korea. The signals, inherently CW (continuous wave) and omni-direction시 10 tonals, are generated by a sound source (model HX-29).

이론/모형

DIFAR system obtains the bearing information by decomposing the received sign시s into quadrature components. The system transmits the acoustic signals from omnidirectional sensors as well as the bearing information from directional sensors using the technique offrequency division multiplexing and transmission[5].

성능/효과

Most of estimation errors, more than 93%, exist in the range. Among the ten frequencies, the signal of 165 Hz gives the best results, showing the smallest standard deviation (13.25°) and thus the largest occunence within the -3 dB response range (99.7%). Meanwhile, the signals of K05 Hz gives the worst results, the largest standard deviation and smallest occurrence within the range.
After the rough estimation at one frequency, detailed searching for the incident an이e is performed by 1° interval in the bearing range -45°~+45°. As the suggested algorithm uses the MRA of the Cardioid beamforming in estimating incident angle, it promises 6 dB increase in the SNR for the narrow band signal. Figure 4 shows the block diagram of bearing estimation process.

참고문헌 (9)

G. C. Carter, 'Time Delay Estimatlon for Passive Sonar Signal Processing,' IEEE Trans. Acoust., Speech, Signal Processing, 29(3), 463-470, 1981

상세보기
C. H. Knapp and G. C. Carter, 'The Generalized Correla-tion Method for Estimation of Time Delay,' IEEE Trans. Acoust., Speech, Signal Processing, 24(4) 320-327 1976

상세보기
C. Barnes, 'Sonobuoy Developments,' NAFO 61-65
C. L. Leblanc, Handbook of Hydrophone Element Design Technology, Naval Underwater Systems Center, 6/15-6/35, 1978
J.-H. Nho, D.-H. Kim, Y.-S. Kim, J.-Y. Han, DIFAR System (Prelimnary Study), Agency for Defense Development, 1981
T. P. Krauss, L. Shure, J. N. Little, Signal processing oolbox for Use with MATLAB, The Math Works Inc., 1/61-1/70, 1992
R. O. Nielsen, Sonar Signal Processing, Interstate Elec-tronics Corporation Anaheim, California, 143-149, 1991
Y. Na, T. Shim, J. Choi, D. Chang, J. Han, 'Propagation Loss Measurement of Underwater Sound Wave using Narrow Band Acoustic Signal,' J. Acoust. Soc. Kor., 13 (E), 5-15, 1994
R. J. Urick, Ambient Noise in the Sea, Peninsula Publishing, 5/15-5/21, 1984

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