Linear frequency-modulated continuous wave active sonar signal processing

Liu, Dali; Liu, Yuntao; Cai, Haiwen; Wang, Yongfeng; Zhang, Hong

doi:10.1109/oceans-taipei.2014.6964519

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…Assuming that the SAR system transmits a frequency-modulated continuous wave (FMCW) signal, the range-compressed signal can be expressed as follows [36]:…”

Section: Bp Algorithmmentioning

confidence: 99%

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

Chu,

Ma,

et al. 2023

Sensors

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Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information using 360° observation of the target. Due to the anisotropy of target scattering characteristics in the actual scene, the sub-aperture imaging method is usually used for CSAR imaging. However, the uniformly divided overlapping sub-aperture CSAR imaging algorithm only considers phase compensation, ignoring the effect of target scattering characteristics on echo amplitude. In CSAR imaging scenarios carried by small rotor unmanned aerial vehicles (SRUAVs), the size of the observed scene cannot be ignored compared to the distance between the target and the antenna and the effect of the anisotropy of the target scattered energy on the echo amplitude should be considered. In this paper, a sub-aperture CSAR imaging method based on adaptive overlapping sub-aperture is proposed. First, the boundary points of the sub-aperture are determined by analyzing the correlation coefficient and the variation coefficient of the energy function. Next, the overlapping sub-aperture division schemes are automatically generated by screening and combining the boundary points. The sub-aperture images are then generated by a Back Projection (BP) algorithm. Finally, sub-aperture image registration and incoherent superposition are used to generate the final CSAR image. Verified by the CSAR field echo data, the proposed method can realize imaging of the original echo data without the Inertial Navigation System (INS) and Global Positioning System (GPS) observation data. Compared with the CSAR full-aperture BP imaging algorithm, the entropy of the image generated by the proposed method increased by 66.77%. Compared with the sub-aperture CSAR imaging algorithm, the entropy of the image generated by the proposed method was improved by 11.12%, retaining more details of the target, improving the target contour features, and enhancing the focusing effect.

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“…Assuming that the SAR system transmits a frequency-modulated continuous wave (FMCW) signal, the range-compressed signal can be expressed as follows [36]:…”

Section: Bp Algorithmmentioning

confidence: 99%

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

Chu,

Ma,

et al. 2023

Sensors

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show abstract

“…Each of these operators is mathematically modelled as follows in Table 2. ∆𝑋 𝑡+1 = (𝑠𝑆 𝑖 + 𝑎𝐴 𝑖 + 𝑐𝐶 𝑖 + 𝑓𝐹 𝑖 + 𝑒𝐸 𝑖 ) + 𝑤∆𝑋 𝑡 (7) All the lower-case alphabet such as: s, a, c are weights of basic operators and calculated randomly. The position vectors are calculated as follows:…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

“…classifier is tested 10 times and mean results are shown in Fig.s (7) and (8). Tables 6 and 7 show the statistical results in Iris and Sejnowski datasets respectively.…”

Section: Setting Parametersmentioning

confidence: 99%

“…Noises, reverberation, and clutter are some classes of underwater echoes [4]. Classification techniques such as statistical processing [5], signal processing [6,7], and feature extraction algorithms [8] were classical schemes. In recent years, Neural Networks (NNs) are considered for their prominent specifications [9] such as a high degree of precision, adaptability, an inherently parallel structure which is very appropriate in hardware implementation and real-time processing.…”

Section: Introductionmentioning

confidence: 99%

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Sonar Data Classification Using Neural Network Trained by Hybrid Dragonfly and Chimp Optimization Algorithms

Mousavipour

Mosavi

2022

Wireless Pers Commun

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This paper proposes a hybrid Dragonfly Algorithm (DA) for training Multi-Layer Perceptron Neural Network (MLP NN) to design the classifier for solving complicated problems and distinguishing the real target from liars' targets in sonar applications. Due to improving the cost computation and reducing the waste of time, a modified low-cost DA is designed for evaluation. To assess the accuracy of the technique, some well-known metaheuristic trainers include Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), DA, and Chimp Optimization Algorithm (ChoA) compared to show the accuracy of similar algorithms. DA and ChoA algorithms have remarkable features and a hybrid algorithm of them is proposed. The performance of the proposed classifier will be evaluated by two standard benchmark datasets. The results show that the modified hybrid DA-ChoA has 15% less time consuming and 4% better performance rather than the original dragonfly method.

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Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network

Kaveh

Khishe

Mosavi

2018

Analog Integr Circ Sig Process

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Linear frequency-modulated continuous wave active sonar signal processing

Cited by 6 publications

References 8 publications

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle

Sonar Data Classification Using Neural Network Trained by Hybrid Dragonfly and Chimp Optimization Algorithms

Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network

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