A Bionic Fish Cilia Median-Low Frequency Three-Dimensional Piezoresistive MEMS Vector Hydrophone

Hang, Guo-jun; Li, Zhen; Wu, Shu-juan; Chen, Xue; Yang, Shi‐e; Zhang, Wendong

doi:10.1007/bf03353777

Cited by 26 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MEMS vector hydrophone used in this experiment is the ciliated bionic vector hydrophone developed by North University of China. The cilia in the center of the cross-beam structure of the hydrophone can perceive the water wave changes caused by the weak water sound, and convert the water sound signal into an electrical signal [ 41 ]. The experiment was carried out in a reservoir in Taiyuan, Shanxi.…”

Section: Mems Vector Hydrophone Signal Processingmentioning

confidence: 99%

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Yan

Chen

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

show abstract

Section: Mems Vector Hydrophone Signal Processingmentioning

confidence: 99%

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Yan

Chen

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Sensitivity measurements were performed by normalizing the signal response to a reference hydrophone with MEMS transducer. The sensitivity of the hydrophone is expressed [44,55] as = (4)…”

Section: Underwater Characterizationmentioning

confidence: 99%

Sensitivity and Directivity Analysis of Piezoelectric Ultrasonic Cantilever-Based MEMS Hydrophone for Underwater Applications

Abdul

Mastronardi

Qualtieri

et al. 2020

JMSE

View full text Add to dashboard Cite

In this paper, we report on the characterization of the sensitivity and the directionality of a novel ultrasonic hydrophone fabricated by micro-electro-mechanical systems (MEMS) process, using aluminum nitride (AlN) thin film as piezoelectric functional layer and exploiting a stress-driven design. Hydrophone structure and fabrication consist of four piezoelectric cantilevers in cross configuration, whose first resonant frequency mode in water is designed between 20 kHz and 200 kHz. The MEMS fabricated structures exploit 1 µm and 2 µm thick piezoelectric AlN thin film embedded between two molybdenum electrodes grown by DC magnetron sputtering on silicon (Si) wafer. The 200 nm thick molybdenum electrodes thin layers add a stress-gradient through cantilever thickness, leading to an out-of-plane cantilever bending. A water resistant parylene conformal coating of 1 µm was deposited on each cantilever for waterproof operation. AlN upward bent cantilevers show maximum sensitivity up to −163 dB. The cross configuration of four stress-driven piezoelectric cantilevers, combined with an opportune algorithm for processing all data sensors, permits a finer directionality response of this hydrophone.

show abstract

“…The hydrophone used in the experiments is a MEMS vector hydrophone produced by North University of China [40]. The basic structure of the hydrophone is shown in Figure 10.…”

Section: Application In the Experiments Of Mems Hydrophonementioning

confidence: 99%

MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Yan

Wang

et al. 2019

Sensors

View full text Add to dashboard Cite

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

show abstract

A Bionic Fish Cilia Median-Low Frequency Three-Dimensional Piezoresistive MEMS Vector Hydrophone

Cited by 26 publications

References 4 publications

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensitivity and Directivity Analysis of Piezoelectric Ultrasonic Cantilever-Based MEMS Hydrophone for Underwater Applications

MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Contact Info

Product

Resources

About