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Adaptive Pulse Compression for Sidelobes Reduction in Stretch Processing Based MIMO Radars

IEEE ACCESS 2022年 10卷 93231-93244页

作者： Malik, Hamza Burki, Jehanzeb Mumtaz, Muhammad Zeeshan Natl Univ Sci & Technol NUST Coll Aeronaut Engn Islamabad 44000 Pakistan

Multiple-Input Multiple-Output (MIMO) radars provide various advantages as compared to conventional radars. Among these advantages, improved angular diversity feature is being explored for future fully autonomous vehicles. Improved angular diversity requires use of orthogonal waveforms at transmit as well as receive sides. This orthogonality between waveforms is critical as the cross-correlation between signals can inhibit the detection of weaker targets due to sidelobes of stronger targets. This paper investigates the Reiterative Minimum Mean Squared Error (RMMSE) mismatch filter design for range sidelobes reduction for a Slow-Time Phase-Coded (ST-PC) Frequency Modulated Continuous Wave (FMCW) MIMO radar. Initially, the performance degradation of RMMSE filter is analyzed for improperly decoded received pulses. It is then shown mathematically that proper decoding of received pulses requires phase compensation related to any phase distortions caused due to doppler and spatial locations of targets. To cater for these phase distortions, it is proposed to re-adjust the traditional order of operations in radar signal processing to doppler, angle and range. Additionally, it is also proposed to incorporate sidelobes decoherence for further suppression of sidelobes. This is achieved by modification of the structured covariance matrix of baseline single-input RMMSE mismatch filter. The modified structured covariance matrix is proposed to include the range estimates corresponding to each transmitter. These proposed modifications provide additional sidelobes suppression while it also provides additional fidelity for target peaks. The proposed approach is demonstrated through simulations as well as field experiments. Superior performance in terms of range sidelobes suppression is observed when compared with baseline RMMSE and traditional Hanning windowed range response.

关键词： MIMO radar Receivers Matched filters filtering algorithms Radar Transmitters Signal to noise ratio MIMO radar reiterative minimum mean squared error (RMMSE) adaptive pulse compression (APC) FMCW

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MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

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IEEE ACCESS 2022年 10卷 73033-73045页

作者： Fuh, Chiou-Shann Tung, Tzu-Chia Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei 10617 Taiwan

The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect. Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach. A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images. The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image. The histogram equalization and image multiplication are applied to achieve better visual quality. In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites. Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations. From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration. Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the second-fastest method. The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms. In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.

关键词： Atmospheric modeling filtering algorithms Adaptive filters Channel estimation Image restoration Image color analysis Estimation Atmospheric light haze removal patchless scene transmission single image

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Widely Linear Maximum Complex Correntropy Criterion Affine Projection Algorithm and Its Performance Analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 3540-3550页

作者： Qiu, Chen Qian, Guobing Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Recently, affine projection algorithm has been extensively studied in the Gaussian noise environment. However, the performance of affine projection algorithm will deteriorate rapidly in the presence of impulsive noise and other non-Gaussian noise. To address this issue, this paper proposes a novel affine projection algorithm based on the complex Gaussian kernel function, called widely linear maximum complex correntropy criterion affine projection algorithm (WL-MCCC-APA). With the maximum complex correntropy criterion (MCCC), the robustness of affine projection algorithm is improved significantly in the non-Gaussian noise environment, especially in the presence of impulsive noise. Meanwhile, a variable step size WL-MCCC-APA (VSS-WL-MCCC-APA) method based on fixed-point theory is proposed to further balance the convergence rate and steady-state misalignment of the algorithm. Finally, the step size boundary analysis and steady-state analysis are presented for theoretical analysis. Simulation results verify the correctness of obtained theoretical results and the superiority of the proposed WL-MCCC-APA and VSS-WL -MCCC-APA.

关键词： Signal processing algorithms Projection algorithms Optimized production technology Kernel filtering algorithms Cost function Convergence Widely linear affine projection maximum complex correntropy criterion variable step size

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An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第9期26卷 4656-4667页

作者： Wu, Jia Xiao, Pei Huang, Haojie Gou, Fangfang Zhou, Zhixun Dai, Zhehao Monash Univ Res Ctr Artificial Intelligence Melbourne Vic 3800 Australia Cent South Univ Sch Comp Sci & Engn Chang Sha 410083 Peoples R China Cent South Univ Sch Comp Sci & Engn Chang Sha 410083 Peoples R China Cent South Univ Xiangya Hosp 2 Dept Spine Surg Changsha 410011 Peoples R China

Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation.

关键词： Magnetic resonance imaging Image segmentation Noise reduction Tumors filtering algorithms Transformers Artificial intelligence Artificial intelligence conditional random field ETUNet magnetic resonance imaging noise reduction non-local means osteosarcoma segmentation

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The Performance of Wearable Speech Enhancement System Under Noisy Environment: An Experimental Study

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IEEE ACCESS 2022年 10卷 5647-5659页

作者： Cherukuru, Pavani Mustafa, Mumtaz Begum Subramaniam, Hema Univ Malaya Fac Comp Sci & Informat Technol Dept Software Engn Kuala Lumpur 50603 Malaysia

Wearable speech enhancement can improve the recognition accuracy of the speech signals in stationary noise environments at 0dB to 60dB signal to noise ratio. Beamforming, adaptive noise reduction, and voice activity detection algorithms are used in wearable speech enhancement systems to enhance speech signals. In recent works, a word rate recognition accuracy of 63% for a 0db signal-to-noise ratio is not satisfactory for a robust speech recognition system. This paper discusses the experimental study using fixed beamforming, adaptive noise reduction, and voice activity detection algorithms with the inclusion of -10dB to 20dB signal to noise ratio for different types of noises to test the wearable speech enhancement system's performance in noisy environments. It also compares deep learning-based noise reduction methods as a benchmark for speech enhancement and word recognition for different noise levels. We have obtained an average word rate recognition accuracy of 5.74% at -10dB and 93.79% at 20dB for non-stationary noisy environments. The outcome of the experiments shows that the selected methods perform significantly better in the environment with high noise dB for both stationary and non-stationary noise. We found that there is no significant statistical difference between the stationary and non-stationary noise word recognition and SNRs level. However, the deep learning-based method performs significantly better than the fixed beamforming, adaptive noise reduction, and voice activity detection algorithms in all noisy levels.

关键词： Speech enhancement Noise measurement Array signal processing Signal to noise ratio Speech recognition filtering algorithms Noise reduction Wearable speech enhancement beamforming adaptive noise reduction voice activity detection deep learning

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Multifeature Fusion Tracking Algorithm Based on Self-Associative Memory Learning Mechanism

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IEEE ACCESS 2022年 10卷 100605-100614页

作者： Ren, Hongge Qiao, Jingjing Shi, Tao Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Hebei Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

A multi-feature fusion tracking algorithm updated with a self-associative memory learning mechanism is proposed to address the problems of short-time disappearance, re-emergence of the target and instability of single features in the kernelized correlation filtering algorithm. When extracting features, directional gradient histogram features, color features, and scale invariant features are fused instead of single features to collect more features of the target and increase the feature robustness. In the detection stage, the bimodal detection is proposed to judge whether the target model needs updating. Bimodal detection is used to judge the maximum target response in the search domain and predict the location of the target in the next frame. The self-associative memory learning mechanism was added into the updating template, and the original algorithm framework was improved to cope with the change of target model. The new algorithm update is biogenic, can recover fragment information, deal with complex and changeable tracking situation. Simulation experiments were conducted on the OTB50, OTB100, and UAV123 video datasets for the classical and new algorithms. The simulation verified that the proposed tracking algorithm has a high success and accuracy rate, which has research value. The tracking success rate improved by 23.6% and the accuracy rate improved by 18.8%.

关键词： Target tracking Feature extraction filtering algorithms Correlation Learning systems Classification algorithms Information filters Memory management Biological system modeling Prediction algorithms Histograms Learning systems Self-associative memory mechanism target tracking correlation filter template update multi-feature fusion

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Universal MATLAB-based square-root solutions in the family of continuous-discrete Gaussian filters for state estimation in nonlinear stochastic dynamic systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第15期32卷 8227-8251页

作者： Kulikov, Gennady Yurievich Kulikova, Maria Vyacheslavovna Univ Lisbon Inst Super Tecn CEMAT Av Rovisco Pais P-1049001 Lisbon Portugal

This article concerns with the issue of square-rooting in continuous-discrete Gaussian filters intended for state estimation in nonlinear stochastic dynamic systems of continuous-discrete sort. These cover all methods devised within the quadrature, cubature, and unscented Kalman filtering approaches as well as those that can be constructed in the future. Based on the universal moment differential equations developed by Sarkka and Sarmavuori in 2013, we advance further that study and design two square-root solutions grounded on MATLAB ODE solvers in the mentioned Gaussian filtering framework. The main problem addressed is a potential negativity of some weights utilized in calculations of the predicted and filtering means and covariances, which precludes from orthogonal square-rooting schemes to be applied. In practice, such square-root implementations are often requested because of their exceptional numerical robustness to round-off and other disturbances. These also preserve the symmetry and positivity of the covariances computed, automatically. Here, we employ the recently-devised J$$ J $$-orthogonal square-rooting technique for designing our universal MATLAB-based square-root solutions in the realm of quadrature, cubature, and unscented Kalman filters, which are easily adjusted to any particular method by using its weights and deterministically selected samples exploited in calculations of the sampled means and covariances. Such a J$$ J $$-orthogonal square-rooting approach is grounded on hyperbolic QR$$ QR $$ factorizations. It leads to two novel algorithms covering any continuous-discrete Gaussian filter of the quadrature, cubature, or unscented Kalman-like kind. Practical performances of our square-root solutions are validated, assessed, and compared within two simulated ill-conditioned scenarios in aeronautical and chemical engineering.

关键词： continuously stirred tank reactor estimation filtering algorithms nonlinear filters numerical stability radar tracking state estimation stochastic systems

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Underwater Image Enhancement and Object Recognition Using CNN Algorithm 5

Underwater Image Enhancement and Object Recognition Using CN...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Vijayalakshmi, P. Seetharaman, M. Praveen, E. Department of Electronic and Communication Engineering Chennai India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376685

The research aims to improve underwater image quality due to the scattering and absorption of light, resulting in poor image quality and reduced visibility. In this study, we propose a novel approach for underwater image enhancement and object recognition using Convolutional Neural Network, or CNN algorithms with the integration of a median filter, implemented in MATLAB. The median filter serves as a preprocessing step to reduce noise and enhance image clarity, while the CNN algorithm is employed for object recognition in the enhanced images. Experimental results demonstrate significant improvements in image quality and object recognition accuracy compared to existing methods. The proposed framework offers a promising solution for underwater applications such as marine biology, underwater surveillance, and underwater robotics, where clear imaging and accurate object detection are essential. © 2024 IEEE.

关键词： Image quality Photography Accuracy Image color analysis Lighting Object detection filtering algorithms Object recognition Convolutional neural networks Image enhancement

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Novel Adaptive Fuzzy Feedback Neural Network Controller for Narrowband Active Noise Control System

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IEEE ACCESS 2022年 10卷 41740-41747页

作者： Huynh Minh-Canh Chang, Cheng-Yuan Chung Yuan Christian Univ Dept Elect Engn Taoyuan 32023 Taiwan Eastern Int Univ Dept Elect Engn Thu Dau Mot City 590000 Vietnam

With the development of science and technology in recent years, many operating machines have become sources of noise affecting quality of life. Hence, the topic of noise diminution using an active noise control (ANC) system has attracted many researchers. This paper develops a new adaptive fuzzy feedback neural network controller (AFFNNC) to improve the performance for narrowband active noise control (NANC) systems. The proposed controller combines fuzzy inference and adaptive feedback neural network controllers that are based on the filtered-s least mean square (FSLMS) algorithm. The AFFNNC comprises five network layers, in which the output layer of the controller uses an adaptive algorithm to tune directly the parameters of filters without prior training. The computational complexity, convergence and stability of the AFFNNC are analyzed. Evaluations are performed on both linear and nonlinear NANC systems with a recorded noise signal that was obtained from a transformer. Numerical simulations confirm the efficiency of the proposed controller compared with other ANC controllers.

关键词： Adaptive systems Convergence Neural networks Finite impulse response filters Linguistics Fuzzy logic filtering algorithms Narrowband noise active noise control noise cancellation adaptive fuzzy feedback neural controller

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An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第6期58卷 5256-5275页

作者： Kropfreiter, Thomas Meyer, Florian Hlawatsch, Franz TU Wien Inst Telecommun A-1040 Vienna Austria Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92037 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92037 USA

In this article, we propose an efficient random finite set (RFS)-based algorithm for multiobject tracking, in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.

关键词： Radio frequency filtering algorithms Probability density function Computational modeling Approximation algorithms Trajectory Sea measurements filtering multiobject tracking multitarget tracking object detection point processes random finite sets (RFSs) sequential estimation

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