As a sound transmitting device based on the nonlinear acoustic theory, parametric acoustic array (PAA) is able to generate high directivity and low frequency broadband signals with a small aperture transducer. Due to its predominant technical advantages, PAA has been widely used in a variety of application scenarios of underwater acoustic engineering, such as sub-bottom profile measurement, underwater acoustic communication, and detection of buried targets. In this review paper, we examine some of the important advances in the PAA since it was first proposed by Westervelt in 1963. These advances include theoretical modelling for the PAA, signal processing methods, design considerations and implementation issues, and applications of the PAA in underwater acoustic engineering. Moreover, we highlight some technical challenges which impede further development of the PAA, and correspondingly give a glimpse on its possible extension in the future. This article provides a comprehensive overview of some important works of the PAA and serves as a quick tutorial reference to readers who are interested to further explore and extend this technology, and bring this technology to other application areas.

https://www.mdpi.com/1424-8220/20/7/2148/pdf

Parametric Acoustic Array and Its Application in Underwater Acoustic Engineering

A modulation method of parametric array for underwater acoustic communication

본 논문에서는 은폐 물체 탐지를 위해 인체에 무해한 음향신호 중 파라메트릭 배열을 이용한 음향 탐지 시스템을 제안한다. 제안된 탐지 시스템은 파라메트릭 배열 현상으로부터 생성되는 고 지향성 Chirp 신호를 송신신호로 사용하고, Dechirp 처리과정을 통해 수신신호의 신호 대 잡음비를 개선하여 거리 분해능을 향상시키는 기법을 사용한다. 제안된 파라메트릭 배열 시스템의 송신센서는 8 × 2 배열로 구축하였으며, 센서 배열의 빔 폭은 수평방향 약 7°와 수직방향 약 26°를 형성한다. 제안된 시스템을 검증하기 위해 물체 탐지 및 가시화를 위한 2축 구동 제어 선형 스테이지를 구축하였으며, 이를 이용하여 은폐된 물체에 대한 A-scan, B-scan 및 C-scan 실험을 진행하였다. 실험 결과, 천에 은폐된 동판과 파이프를 탐지하고 형상을 확인하였으며, 가시화된 형상에서 동판은 0.015 ㎡, 파이프는 0.046㎡ 의 오차를 보여주었다.

파라메트릭 배열을 이용한 은폐 물체 탐지 시스템

Underwater communication is extremely challenging for small underwater robots which typically have stringent power and size constraints. In our previous work, we developed an artificial electrocommunication system which could be an alternative for the communication of small underwater robots. This paper further presents a new electrocommunication system that utilizes Binary Frequency Shift Keying (2FSK) modulation and deep-learning-based demodulation for underwater robots. We first derive an underwater electrocommunication model that covers both the near-field area and a large transition area outside of the near-field area. 2FSK modulation is adopted to improve the anti-interference ability of the electric signal. A deep learning algorithm is used to demodulate the electric signal by the receiver. Simulations and experiments show that with the same testing condition, the new communication system outperforms the previous system in both the communication distance and the data transmitting rate. In specific, the newly developed communication system achieves stable communication within the distance of 10 m at a data transfer rate of 5 Kbps with a power consumption of less than 0.1 W. The substantial increase in communication distance further improves the possibility of electrocommunication in underwater robotics.

An Electrocommunication System Using FSK Modulation and Deep Learning Based Demodulation for Underwater Robots

Digital acoustic signal processing can be applied to sonar and acoustic communications. Especially, transmitting acoustic signal to the desired direction has many applications in military and industry fields. In this paper, we present a steerable digital communication system using parametric array transducers. To evaluate the proposed system, we build digital communication system by using transducer array, power amplifier and Labview software. The Labview software is composed of two parts. The first part is designed to generate beam to the desired direction by changing parameters such as number of sensors, complex weight of each sensor, type of transmit data and etc. The second part is to generate modulated signal of ASK, FSK and PSK, and to demodulate the received signals. With laboratory experiments, we verify the performance of the proposed communication system. Experimental results show that the system can be used to mitigate multipath effect in shallow water and can achieve high data-rate transmission

Digital communication system using beamsteering for difference frequency in a parametric array

In this paper, we present novel analysis results of sonar communication using parametric array. We consider transducer diameter, primary frequency, difference frequency and acoustic power as analysis parameters of communication performance. We calculate theoretical BER(Bit Error Rate) and channel capacity of MIMO(Multi Input and Multi Output) communication system. By considering practical parameters, we obtain optimum difference frequency generation condition. The obtained primary frequency is 560 kHz, difference frequency 45kHz and acoustic power is 28.05Watt. For BER of , the range gain of parametric array communication is 3.35km compared to primary frequency communication. For channel capacity of 10bps/Hz, the SISO and MIMO communication range of parametric array communication are 3.8km and 3.98km respectively, while primary frequency communication range is 0.83km.

Analysis of Highly Directional Sonar Communication System

The convolution code(CC) of code rate 1/2 as a forward error correction (FEC) in Quadrature Phase Shift Keying (QPSK) is applied to decrease bit error rate (BER) by background noise and multipath in shallow water acoustic channel. Ratio of transmitting signal bandwidth to channel coherence bandwidth is defined as frequency selectivity index. BER and bit energy-to-noise ratio gain of transmitted signal according to frequency selectivity index are evaluated. In the results of indoor water tank experiment, BER is well matched theoretical results at frequency selectivity index less than about 1.0. And bit energy-to-noise ratio gain is also matched theoretical value of 5 dB. BER is effectively decreased at frequency selective multipath channel with frequency selectivity index higher than 1.0. But bit energy-to-noise ratio greater than a certain size in terms of CC weaving is effective in reducing bit errors. In the results, the defined frequency selectivity index in this study could be applied to evaluate a performance of CC in multipath channel. Also it could effectively reduced BER in a low speed underwater acoustic communication system without an equalizer.

/pdf/performance-of-convolution-coding-underwater-acoustic-45t0sezbjn.pdf

Performance of Convolution Coding Underwater Acoustic Communication System on Frequency Selectivity Index

High data-rate underwater communications is demanded. This demand imposes stringent requirements on underwater communication equipment of phase-locked-loop (PLL). Phase noise in PLL is unwanted and unavoidable. In this paper, we investigate the PLL phase noise effect on high order QAM for underwater communication systems. The phase noise model using power spectral density is adopted for performance evaluation. The phase noise components considered in PLL are reference oscillator, voltage controlled oscillator (VCO), filter and divider. The filters in PLL noise are assumed to be second order active and passive low pass filters. Through simulation, we analyze the phase noise characteristics of the four components and then investigate the performance improvement factor of each component. Consequently, we derive specifications of VCO, phase detector, divider to meet performance requirement of high data-rate communication using QAM under phase noise influence.

/pdf/analysis-of-pll-phase-noise-effect-for-high-data-rate-44v4mfgkda.pdf

Analysis of PLL Phase Noise Effect for High Data-rate Underwater Communications

A novel moving object classification system using UWB radar and classifier based on decision tree structure are proposed. By using the proposed radar system, we construct UWB radar signal database by considering two movements and four moving directions of human and dog. The proposed classifier is based on nonlinear support vector machine (SVM) using RBF kernel and use linear predictive code (LPC) coefficients as feature vector. By evaluating performance of the proposed decision tree structures, we obtain the best classification results when the first level SVM classifies type of movement and then the second level SVM classifies moving object. The correct classification probability ranges from 93% up to 97%. The proposed system and classifier can be used for efficient human and dog classification and can be applied to other moving objects classification as well.

Moving object classifier based on UWB radar signal

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