Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Saffari, Abbas; Zahiri, Seyed Hamid; Khishe, Mohammad

doi:10.1016/j.dt.2022.05.007

Cited by 15 publications

(12 citation statements)

References 17 publications

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“…Step 5 Finding the Number of Source-Receiver Pairs to Cover Volume: The number of source and receiver pairs required to guarantee full coverage of a given V volume is defined as (15), which is the ratio of the entire monitoring volume to the effective monitoring volume obtained for a source-receiver pair.…”

Section: Figure 7 Frequency Dependent Ocean Ambient Noisementioning

confidence: 99%

“…• Low false alarm rate: Since multiple sensor receivers can be used, target detection and localization will be more accurate with fewer false alarms. Additionally, with the automatic target recognition feature, operatorrelated errors in the system detection performance are eliminated [15], [16]. The detection performance of MSSN is directly related to sensor deployment and coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Figs 15. and 16 show the results obtained using the three models for f = 15 kHz and f = 30 kHz, respectively.…”

mentioning

confidence: 99%

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Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

2022

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Recent changes in the design of enemy threats such as submarines and the technological achievements in sensor development have paved the way for multistatic sonar applications, which increase security and situational awareness in underwater tactical operations. Previously, coverage in multistatic sonar sensor networks (MSSN) was studied using Cassini ovals and the traditional sonar detection model in two dimensions without any discussion of the practicability and feasibility in terms of conditions related to the underwater acoustic propagation environment. In this study, a practical three-dimensional MSSN channel model is proposed. The proposed model covers a spectral variation of absorption loss, ambient noise, sound speed profile, and shadow zones. The realistic effects of sound propagation and environmental conditions are modeled and evaluated using Lybin, which is a well-known sonar performance prediction tool. Using the practical MSSN channel model, the number of source-receiver pairs required to cover a three-dimensional MSSN volume is calculated. The impacts of frequency, source-to-receiver distance, and source level are investigated. The results are compared to the Cassini oval and traditional sonar detection models via an error expression derived according to our verified practical model. The results reveal that the inclusion of ambient conditions and sound propagation characteristics in the channel model leads to huge error levels of 4700000% in the Cassini oval model and 170000% in the traditional sonar detection model, depending on frequency. Thus, the applicability of these models in realistic MSSN deployment scenarios is limited.

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Section: Figure 7 Frequency Dependent Ocean Ambient Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

2022

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“…The constant false alarm rate can be detected using the deep transfer technique [9]. Finally, compared to highly deformable animals like cats, aircraftaremostlyhardobjects,simplifying some aspects of their modeling and enabling one to concentrate on the key elements of the fine-grained recognition challenge [10]. LimingZhou,et.al,… [12]adoptedthe MSDNnetwork using fewer grids within the input photosto measure subtle aero planes.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection Analysis Using Remote Sensing Images Deploying Deep Neural Network

et al. 2023

Recent Trends in Data Science and Its Applications

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In the processing of images, aircraft recognition is crucial. The shape of the airplane can be extracted using are cognition processor. The practice of detecting as well as identifying a specific object or component in a digitized image or video is known as image recognition. This technique is employed in numerous applications, including frameworks for computerizing industrial lines, toll corner observation, and security observation. In addition to having a complicated structure, different types of air craft range in size, form, and color shading. Even within a single type, the texture and brightnesswerefrequentlyvariabledependingonthesituation.Addi tionally,numerousdisruptionsincludingclutter,disparatecontrast s,andhomogeneityanxietyfrequentlyimpairrecognition.Therefor e,thetechniqueheavilydependsonrobustnessanddisturbanceresist ance. This technology makes use of neural networks to recognize aircraft. The median filter algorithm is used to process the input satellite image. Shape, size, and texture are used to extract features. There lating to global of the filter outputs is then used to calculate the feature representation, which eases the numerical challenges. Followingthat,aneuralnetworkapproachknownasaconvolutional neuralnetworkisutilizedtodeterminethelayerbetweenclasses.Dim ensionalityreduction,segmentation,andtemplatebasedaircraftidentificationareallpartofthisrecognitionmethod.A Megapixelsegmentisspecificallysuggested to lessen the dimension of the satellite picture. The desired object is then distinguished from the background using histogram probability thresholding. Convolutional neural networks are used to classify data using templates as matchingmodels.Finally,sendanalertsystemtotheadministratorw henanaircraftisdetectedand offer a higher level of accuracy than the current algorithm.

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“…In recent years, the characteristics of active Doppler sonar have been applied to estimate the velocity by accurately estimating the Doppler frequency (Yang and Fang, 2021;Saffari et al, 2022). Further, hydrophones have been used to estimate the motion information of aerial sound sources.…”

Section: Introductionmentioning

confidence: 99%

Motion parameter estimation of AUV based on underwater acoustic Doppler frequency measured by single hydrophone

Rong

Xu²

2022

Front. Mar. Sci.

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This paper describes the use of a single hydrophone to estimate the motion parameters of an autonomous underwater vehicle (AUV) from the underwater acoustic signal excited by its propulsion motor. First, the frequency range of the hydroacoustic signal radiated by the AUV motor is determined, and a detection and recognition model is designed. In the case of uniform linear motion of the AUV, the geometric relationship between the Doppler frequency shift curve of the sound source is derived and the motion model of the sound source and sound line propagation is established. An estimation algorithm for the motion parameters of multiple AUVs based on data from a single hydrophone is derived. Then, for Doppler underwater acoustic signals disturbed by independent identically distributed noise with an arbitrary probability distribution, a cumulative phase difference power amplification instantaneous frequency estimation method is proposed. This method is based on the sum of multiple logarithmic functions. Finally, the effectiveness and accuracy of the algorithm in estimating the motion parameters of multiple AUVs are verified through simulations and experiments.

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Automatic recognition of sonar targets using feature selection in micro-Doppler signature

Cited by 15 publications

References 17 publications

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

Aircraft Detection Analysis Using Remote Sensing Images Deploying Deep Neural Network

Motion parameter estimation of AUV based on underwater acoustic Doppler frequency measured by single hydrophone

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