AUV SLAM and Experiments Using a Mechanical Scanning Forward-Looking Sonar

He, Bo; Liang, Yan; Feng, Xiaoxiao; Nian, Rui; Yan, Tianhong; Li, Minghui; Zhang, Shujing

doi:10.3390/s120709386

Cited by 51 publications

(29 citation statements)

References 23 publications

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“…1 For example, they have been widely used in commercial, scientific and military missions for searches and investigation, 2 and to map and monitor the marine environment, 3 to service ocean engineering work, to explore resources, 4 and to test, evaluate and explore the sea bottom. 5 6 The main advantages of AUVs lie in their safety and economy.…”

Section: Introductionmentioning

confidence: 99%

A New Nonlinear Actuator Fault Detection and Isolation Algorithm for Autonomous Underwater Vehicles

Shi¹,

Wang²,

Gao³

et al. 2015

j comput theor nanosci

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Autonomous Underwater Vehicles (AUVs) are widely used in commercial, military and scientific missions for various purposes. Fault Detection and Isolation (FDI) technology is urgently required because of the long duration of missions and the unknown environment. It is necessary to detect and isolate the fault to increase the reliability and availability of AUVs during the task-performing process. The Extended Multiple-Model Adaptive Extended Kalman Filter (EMMAEKF) method is widely used in FDI technology, but there are errors in the linearization process of Extended Kalman Filter (EKF). To overcome this limitation, a new nonlinear Extended Multiple-Model Adaptive Cubature Kalman Filter (EMMACKF) method is proposed in this article. The CKF has been used to generate residual signal on the six degrees-of-freedoms (DOFs) model of AUVs. Simulation results have shown that the original states and extended states can be well evaluated under the actuator fault scenario, and the faulty apparatus can be simultaneously detected and effectively isolated using the proposed methods. Compared with the EMMAEKF and Extended Multiple-Model Adaptive Unscented Kalman Filter (EMMAUKF) algorithms, both accuracy and time delay have been improved to some extent.

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Section: Introductionmentioning

confidence: 99%

A New Nonlinear Actuator Fault Detection and Isolation Algorithm for Autonomous Underwater Vehicles

Shi¹,

Wang²,

Gao³

et al. 2015

j comput theor nanosci

View full text Add to dashboard Cite

show abstract

“…The objectives of developing signal processing techniques for ultrasonic NDE is to suppress the speckle noise and enhance image contrast without blurring. This problem appears in a variety of applications with parallel environment such as radar [1,2], sonar [3], communications [4][5][6], and ultrasound imaging [7,8], and has received considerable attention in the recent decades.…”

Section: Introductionmentioning

confidence: 99%

Image de-noising via spectral distribution similarity analysis for ultrasonic non-destructive evaluation

Xiao¹,

Li²,

Gongzhang³

et al. 2014

AIP Conference Proceedings

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Ultrasonic detection and characterization of flaws in coarse-grained materials exhibiting heterogeneous and scattering microstructure is of particular importance across many industries, but remains challenging. Most spectral based denoising methods in the literature are sensitive to material properties, which necessitate a troublesome parameter optimization process and consequently impede their application into ultrasonic image processing. In order to improve flaw visibility in an image, we propose a novel and robust clutter suppression method through spectral distribution similarity analysis (SDSA). This method isometrically segments all the time-series data in a dataset acquired by the Full-Matrix-Capture technique and then censuses the spectral distribution of global segments and of local segments for every focusing point in the Total-FocusingMethod image. The coefficient computed by measuring the similarity between the two spectral distributions reveals the possibility of a legitimate flaw indication. Experiments on two highly scattering samples were conducted to validate this method. By applying SDSA, crack visibility is greatly enhanced with an average >20 dB target-to-noise ratio enhancement for a stainless steel weld sample, whilst ~30dB improvement for an austenitic steel sample. The proposed technique retains excellent performance for both samples when the selected segment length is varied, proving its robustness and highlighting its potential for application across various materials.

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“…Many techniques have been developed to achieve noise reduction in applications with parallel environment such as radar [1,2], sonar [3], communications [4][5][6], and ultrasound imaging. In particular, phased array was introduced and several practical techniques based on it have already achieved promising results, such as the widely used Focused B-scan [7], Total Focusing Method (TFM) [7], minimum variance beamforming and adaptive processing [8,9], and spatial diversity based techniques like Spatially Averaged Sub-Array Correlation Imaging (SASACI) [10].…”

Section: Introductionmentioning

confidence: 99%

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

Gongzhang¹,

Xiao³

et al. 2014

AIP Conference Proceedings

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This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signals through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data

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AUV SLAM and Experiments Using a Mechanical Scanning Forward-Looking Sonar

Cited by 51 publications

References 23 publications

A New Nonlinear Actuator Fault Detection and Isolation Algorithm for Autonomous Underwater Vehicles

A New Nonlinear Actuator Fault Detection and Isolation Algorithm for Autonomous Underwater Vehicles

Image de-noising via spectral distribution similarity analysis for ultrasonic non-destructive evaluation

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

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