Donghu Nie

Kite-type passive acoustic detection system for underwater small targets

Potential underwater threats from some small intruders may pose a significant risk to security of some important waterside infrastructures, coastal facilities, subsea pipelines and ships by terrorists or hostile forces. Detection and tracking technologies in farther range are required to counter such threats by taking an appropriate response in time. The limitations of active sonar in high cost, negative effects on marine life, low covertness and high reverberation have stimulated researches in passive detection methods. The Kite-type Passive Acoustic Detection System (KPADS) was developed to provide alarms in the area of underwater security and protection against terrorism intruders and threats from the waterside, including unmanned underwater vehicles, small submarines, surface vessels, divers and underwater robots etc. This paper reports on the detection and tracking of stationary noise source and moving targets in Fuxianhu Lake of China. A series of technologies is applied to a passive triangular array of 18 hydrophones in a uniformly-spaced linear arrangement. Experimental results are presented demonstrating the system performance of detection range up to 8000m, tracking better not less than 4000m, bearing accuracy up to 1°, and are verified by GPS ground truth.

Underwater cylindrical shell in different thickness recognition using biomimetic dolphin clicks

For the traditional narrowband sonar signal, recognizing underwater small targets is an extremely difficult task. Therefore, a series of biomimetic dolphin clicks, a kind of broadband and transient-like sonar signal, were used as the transmitting waveform for recognition tasks, and the tasks were presented from experiments in an anechoic pool. In this experiment, three biomimetic dolphin clicks, differing in the energy distribution in the time-frequency domain, were used to recognize three copper cylindrical shells which have a diameter of 10 centimeters, 40 centimeters high and different thickness. The echoes from three targets were collected by receiving system, and were analyzed in the time domain and frequency domain. In addition using wigner-ville distribution, the echoes were projected to the time-frequency space for feature observation. Then the time-frequency features of echoes were extracted by singular value decomposition, and these features were classified by a support vector machine for recognizing target echoes. Experimental results indicated that these cylindrical shells in different thickness can be recognized by the biomimetic dolphin clicks, and different clicks obtain different echo responses and different identification results. Therefore, the echo responses have strong dependence with the time-frequency distribution of transmitting waveform. Furthermore the results show a promising way to improve underwater acoustical recognition in an intelligent waveform design manner by a dynamic closed-loop feedback sonar system.

Kalman filter restraining outliers for short baseline system

In short baseline underwater acoustic positioning system, the outliers will be included in measurement data because of noise and multi-path channel environment. In this paper, we introduce a modified Kalman filter that can perform robust based on innovation variance, this method solves the problem of filtering divergence effectively by modifying the supplement matrix. Finally the processing result of the experiment data in lake is given. The experiment result indicates that using the method can be efficiently a smooth processing to the localization result.

Resonance spectrum of submerged cylindrical shell extraction using fractional fourier filter

Acoustic wave interacts with submerged cylindrical shell to excite object resonance and then object reradiates acoustic wave into surrounding water. The backscattering field can be divided into two major parts: elastic echoes, which are produced by object resonance and the background field is composed of specular reflection echoes. Furthermore, specular echo is quite similar to the incident signal, which means that a little object information is hidden in this echo. Therefore, a fractional Fourier filter is used to extract elastic echoes from backscattering echoes in time-frequency plane. A series of free field experiments are conducted in order to verify the reliability of the proposed method of elastic echo extraction. Three stainless steel cylindrical shells with different wall thicknesses are used as the experimental objects. The experimental echoes are extracted by fractional Fourier filter, and the extracted elastic echoes are transformed to resonance spectrum for further analysis. The results show the resonance peaks in resonance spectrum, located in different frequencies corresponding to different shells. It proves to be a promising way to extract object intrinsic features in resonance spectrum and experimental results indicate that these cylindrical shells with different wall thicknesses (in millimeters) can be effectively discriminated by resonance spectrum.

Acoustic scattering from stainless steel shells with varying wall thickness using a biomimetic click: Modeling and interpretation

Using broadband click pulse, dolphins can discriminate cylindrical shells with subtle difference in wall thickness. In this study, finite element models were built to calculate the acoustic scattering from stainless steel shells with varying wall thickness. In order to further analyze the mechanism of interaction between click pulse and shells, the scattering field in fluid and the stress distribution in solid were calculated by transient solver in time domain. The simulation results show that the acoustic solid interaction leads to object resonance, and then the elastic waves radiate into surrounding fluid. Furthermore, there are significant differences among the elastic waves from different shells with varying wall thickness. It suggested that the crucial information about shell’s wall thickness is hidden in elastic echo. The results show a promising way to further understand the target discrimination in dolphin’s biosonar.

Elastic feature of cylindrical shells extraction in time-frequency domain using biomimetic dolphin click

A dolphins biosonar may effectively discriminate subtle differences among targets. In order to investigate the possible physical mechanism of target discrimination, in this study, a finite element model excited by a biomimetic click pulse was proposed. The acoustic scattering field and stress distribution of a stainless steel shell were simulated. The biomimetic click experiments were then conducted to verify the theoretical predictions in an anechoic tank. The experimental results showed a good agreement with the model simulations. Furthermore, the elastic time-frequency features of three cylindrical shells with different wall thickness were obtained using a fractional Fourier transform filter to eliminate specular reflection and cross-term interference. To compare discrimination capacity of the time-frequency features with and without the specular reflection, a time-frequency correlator was applied to calculate the correlation coefficient between different shells. The results indicated that the time-frequency features can be represented in high resolution with less cross-term interference, and these features without specular reflection showed a good capacity to discriminate the shells with different wall thickness.

Classification for underwater small targets with different materials using bio-inspired Dolphin click

Recognition of material for underwater small targets is an extremely difficult task for the traditional CW and LFM signal. In order to distinguish different material types of underwater small targets with the same size and geometry, a series of broadband, transient-like bio-inspired dolphin sonar signals (clicks) are used as the transmitting waveform. In the joint time-frequency space, Reduced Interference Distribution (RID) is used to analyze the echoes, and then the RID-SV feature is extracted by singular value decomposition (SVD). The Support Vector Machines (SVM) are used to classify echoes. In order to verify the efficiency of the bio-inspired click signal for distinguishing materials, an anechoic pool experiment was conducted. In this experiment, three bio-inspired dolphin signals are generated by two linear frequency modulation component covering different frequency band in 40-80 kHz. These three signals are used to detect and recognize three 10 cm diameter solid spherical targets with different materials (copper, aluminum, stainless). Experimental results show that these spherical targets can be classified according to the bio-inspired dolphin click echo; Results also suggest that the classification of underwater small targets made form different materials can be improved by altering energy distribution of the bio-inspired signal in the frequency space.

Research of Decision Classification of Underwater Object Echo based on Fuzzy Integral

Underwater object recognition is one of three crucial technologies of underwater acoustic equipments development. Only partial information describing objects can be acquired in single sensor that is not enough for object classification. Method of decision fusion of underwater object echo signal, based on theory of fuzzy integral and fuzzy measure, was studied to integrate primary recognition result from multi-general-sensors to raise recognition rate. Five features were extracted from data collected on the sea to prove the performance of this method, including modul maxima power feature of continual dissymmetry Gauss wavelet, crossing-zero feature, wavelet coefficient amplitude feature, Wigner feature and Walsh feature. Recognition rate is increased by approximately 9 percent

Research of a novel weak speech stream detection algorithm

Purpose of speech stream detection is to capture speech stream coming randomly in adverse acoustic environments. A novel robust method for speech stream detection is introduced based on both linear predict code all-pole model and lossless sound tube model to detect speech stream from inputs of wireless speech band communication. It makes use of autocorrelation distribution characteristics of variance sequence of linear predictive residual sequence to formulate two dimensions decision threshold vector. The decision threshold is adaptive to energy of background noise. It can make minimum decisions error. Plenty of signal stream data with various noises under various Signal-to-Noise Ratio and wireless speech band recordings on the spot were used to compare the proposed algorithm respectively with spectrum Entropy and short-time energy algorithm. The experiment results show that the new method for speech stream detection has good detection performance, and it performs well in adverse environments, and the speech stream detected sounds fluently.