An Enhanced Data-Driven Array Shape Estimation Method Using Passive Underwater Acoustic Data

Abstract

Beamforming-based signal enhancement technologies in passive sonar array processing often have poor performance due to array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation formulation is proposed using passive underwater acoustic data. Beamforming based on a hypothetically ideal array is firstly employed to perform the detection of narrow-band components from sources of opportunity, and the corresponding phases of these detected narrow-band components are subsequently extracted to acquire time-delay differences. Then, a weighted outlier-robust Kalman smoother is proposed to acquire enhanced estimates of the time-delay differences, since the underlying properties of slowly changing time-delay differences in the hydrophone array and diverse signal to interference and noise ratios in multiple narrow-band components are explored; and its Cramer–Rao Lower Bound is also provided. Finally, the hydrophone array shape is estimated based on the estimated time delay differences. The proposed formulation fully exploits directional radiated noise signals from distant underwater acoustic targets as sources of opportunity for real-time array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. The effectiveness of the proposed method is validated in simulations and real experimental data.

A Time-Frequency Joint Time-Delay Difference Estimation Method for Signal Enhancement in the Distorted towed Hydrophone Array

“…where S ch ( f ) represents the stationary continuous spectrum originated from the hydrodynamic noise, S mp ( f ) denotes the modulation spectrum caused by the propeller noise, and S lm ( f ) is the line-spectrum generated by the machinery noise, respectively. The machinery noise s lm (t), generated from the inevitable vibration of mechanical equipment such as diesel generator and air conditioning system, is generally denoted by the collection of multiple sinusoidal signals, i.e., [3],…”

“…Beamforming-based signal enhancement is a crucial problem in array signal processing, and plays a significant role in feature extraction and target recognition [1][2][3][4]. In a passive sonar system, an important topic is to acquire the enhanced underwater ship-radiated noise signal from the received data of the hydrophone array [5][6][7].…”

“…The method in [31] regarded the time-delay difference as the slope of the regression line of the phase difference and applied regression analysis to acquire the time-delay differences estimates. Note that first, the accuracy of time-delay difference estimation exploiting phase difference of line-spectrum component is sensitive to noise, and drastically degrades with decreases of the SNR of the line-spectrum component [3]. Furthermore, regarding the linespectrum components with the same SNR, the time-delay difference estimation accuracy is proportional to the frequency of the line-spectrum component, i.e., the estimation accuracy of the high-frequency line-spectrum component outperforms that of the low-frequency one [30].…”

“…Unfortunately, this requirement cannot be met for the underwater ship-radiated noise signal due to the development of the vibration control and noise suppression techniques [5] resulting in a much weaker radiated noise level. Moreover, the phase difference measurements are acquired in each hydrophone element without any array gain [3].…”

“…Several improved algorithms have been proposed to acquire the enhanced time-delay difference estimates. For example, an effective outlier-robust Kalman smoother (WORKS) method has been developed in [3] to acquire enhanced time-delay estimates by exploiting the properties of slowly changing time-delay difference in the hydrophone array. This method can alleviate the effects of the outliers resulting from the diverse noise levels in the multiple line-spectrum components.…”