A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection

Zhao, Zhishan; Zhao, Anbang; Jiang, Hui; Hou, Baochun; Sotudeh, R.; Niu, Fang

doi:10.3390/s17071565

Cited by 15 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Matched filters are generally used to identify a known signal or template in an unknown signal. The matched filter can improve noise rejection and extract features out of a noisy signal Matched filters have been extensively used for the detection of blood vessels, ECG peaks, seismic events, moving targets, cracks, SONAR, and microfluidic cells [44][45][46][47][48][49][50][51]. It is similar to the convolution of an unknown signal with a conjugated time-reversed version of the template.…”

Section: Wheeze Detection Using Matched Filtermentioning

confidence: 99%

Design Analysis and Human Tests of Foil-Based Wheezing Monitoring System for Asthma Detection

Khan

Qaiser

Shaikh

et al. 2020

IEEE Trans. Electron Devices

View full text Add to dashboard Cite

We present a flexible acoustic sensor that has been designed to detect wheezing (a common symptom of asthma) while attached to the chest of a human. We adopted a parallel plate capacitive structure using air as the dielectric material. The pressure (acoustic) waves from wheezing vibrate the top diaphragm of the structure, thereby, changing the output capacitance. The sensor is designed such that it resonates in the frequency range of wheezing (100-1000Hz) which presents twofold benefits. The resonance results in large deflection of the diaphragm that eradicates the need for using signal amplifiers (used in microphones). Secondly, the design itself acts as a low pass filter to reduce the effect of background noise which mostly lies in >1000Hz frequency range. The resulting analog interface is minimal and thus, consumes less power and occupies less space. The sensor is made up of low-cost sustainable materials (aluminum foil) which greatly reduces the cost and complexity of manufacturing processes. A robust wheezing detection (Matched filter) algorithm is used to identify different types of wheezing sounds among the noisy signals originating from the chest that lie in the same frequency range as wheezing. The sensor is connected to a smartphone via Bluetooth, enabling signal processing and further integration into digital medical electronic systems based on the Internet of Things (IoT). Bending, cyclic pressure, heat, and sweat tests are performed on the sensor to evaluate its performance in simulated real-life harsh conditions.

show abstract

Section: Wheeze Detection Using Matched Filtermentioning

confidence: 99%

Design Analysis and Human Tests of Foil-Based Wheezing Monitoring System for Asthma Detection

Khan

Qaiser

Shaikh

et al. 2020

IEEE Trans. Electron Devices

View full text Add to dashboard Cite

show abstract

“…Zhishan Zhao et al proposed an improved matched filter in Additive white Gaussian noise (AWGN) combined with the adaptive line enhancer by analyzing the output spectrum and the spectral spectrum of the matched filter, which is the frequency-domain adaptive matched filter. Note that this method is proposed for active sonar [12]. In this paper [13], Mel frequency Cestrum Coefficients feature extraction using sound pressure and particle velocity signals are researched.…”

Section: Appl Sci 2018 8 61mentioning

confidence: 99%

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

Alaie

Farsi

2018

Applied Sciences

View full text Add to dashboard Cite

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold) that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method.

show abstract

“…Matched filtering (MF) is the optimal linear filter for maximizing the SNR of the output signal in a white noise environment. It is commonly used in the echo signal processing of radar and sonar [24,25]. By combining MF technology with a vector hydrophone array, the SNR requirements can be effectively reduced, providing valuable technical support for depth estimation of weak targets in the deep sea.…”

Section: Introductionmentioning

confidence: 99%

A Deep-Sea Broadband Sound Source Depth Estimation Method Based on the Interference Structure of the Compensated Beam Output

Liang,

Chen,

Meng

et al. 2023

JMSE

View full text Add to dashboard Cite

This paper proposes an underwater broadband target depth estimation method based on the multipath arrival structure in medium and short-range deep-sea environments. The proposed approach involves separating the multipath rays arriving at the vertical line array using the matched filtering technique. The combined beamforming is then applied to the vector hydrophone vertical array (VHVA) to obtain more accurate elevation angle estimates. The components of the interference sound field at different distances are judged, and the signal received by the array is compensated using suitable estimated angles. Finally, the multipath time delay difference is extracted from the pulse peaks of the compensated signal, and the target depth is estimated by establishing the relationship among the multipath time delay difference, the elevation angle and depth. Simulations and experiments demonstrate the effectiveness of this method in reducing the depth estimation bias and avoiding the misjudgment of surface or submerged targets.

show abstract

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection

Cited by 15 publications

References 12 publications

Design Analysis and Human Tests of Foil-Based Wheezing Monitoring System for Asthma Detection

Design Analysis and Human Tests of Foil-Based Wheezing Monitoring System for Asthma Detection

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

A Deep-Sea Broadband Sound Source Depth Estimation Method Based on the Interference Structure of the Compensated Beam Output

Contact Info

Product

Resources

About