scholarly journals Radar Waveform Design for Extended Target Recognition under Detection Constraints

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Huadong Meng ◽  
Yimin Wei ◽  
Xuhua Gong ◽  
Yimin Liu ◽  
Xiqin Wang
Keyword(s):  
Optimization Problem ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Waveform Design ◽  
Code Design ◽  
Quadratic Optimization Problem ◽  
Important Constraint ◽  
Extended Target ◽  
Gaussian Disturbance

We address the problem of radar phase-coded waveform design for extended target recognition in the presence of colored Gaussian disturbance. Phase-coded waveforms are selected since they can fully exploit the transmit power with sufficient variability. An important constraint, target detection performance, is considered to meet the practical requirements. The waveform is designed to achieve maximum recognition performance under a control on the achievable signal-to-noise ratio (SNR) of every possible target hypothesis. We formulate the code design in terms of a nonconvex, NP-hard quadratic optimization problem in the cases of both continuous and discrete phases. Techniques based on semidefinite relaxation (SDR) and randomization are proposed to approximate the optimal solutions. Simulation results show that the recognition performance and the detection requirements are well balanced and accurate approximations are achieved.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianghua Nie ◽  
Yongsheng Xiao ◽  
Lizhen Huang ◽  
Feng Lv
Keyword(s):  
Least Squares ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Learning ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.

scholarly journals MIMO Radar Adaptive Waveform Design for Extended Target Recognition

2015 ◽  
Vol 11 (6) ◽  
pp. 154931 ◽  
Author(s):  
Lulu Wang ◽  
Kai-Kit Wong ◽  
Hongqiang Wang ◽  
Yuliang Qin
Keyword(s):  
Target Recognition ◽  
Waveform Design ◽  
Mimo Radar ◽  
Extended Target

scholarly journals Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 261 ◽  
Author(s):  
Tianduo Hao ◽  
Chen Cui ◽  
Yang Gong
Keyword(s):  
Optimization Problem ◽  
Nearest Neighbor ◽  
Design Method ◽  
Average Power ◽  
Waveform Design ◽  
Cognitive Radar ◽  
Extended Target ◽  
Convex Problem ◽  
The Matrix ◽  
Matrix Variable

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform.

Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

2019 ◽  
Vol 9 (4) ◽  
pp. 497-506 ◽  
Author(s):  
Khamis A. Al-Karawi
Keyword(s):  
Speech Processing ◽  
Speaker Recognition ◽  
System Performance ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Space ◽  
Signal To Noise ◽  
Verification Systems ◽  
Noisy Condition

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.

Classroom Noise and Children Learning Through a Second Language

2005 ◽  
Vol 36 (3) ◽  
pp. 219-229 ◽  
Author(s):  
Peggy Nelson ◽  
Kathryn Kohnert ◽  
Sabina Sabur ◽  
Daniel Shaw
Keyword(s):  
Second Language ◽  
Word Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Recognition Task ◽  
English Word ◽  
Noise Condition ◽  
English Only ◽  
Task Behavior ◽  
The Impact

Purpose: Two studies were conducted to investigate the effects of classroom noise on attention and speech perception in native Spanish-speaking second graders learning English as their second language (L2) as compared to English-only-speaking (EO) peers. Method: Study 1 measured children’s on-task behavior during instructional activities with and without soundfield amplification. Study 2 measured the effects of noise (+10 dB signal-to-noise ratio) using an experimental English word recognition task. Results: Findings from Study 1 revealed no significant condition (pre/postamplification) or group differences in observations in on-task performance. Main findings from Study 2 were that word recognition performance declined significantly for both L2 and EO groups in the noise condition; however, the impact was disproportionately greater for the L2 group. Clinical Implications: Children learning in their L2 appear to be at a distinct disadvantage when listening in rooms with typical noise and reverberation. Speech-language pathologists and audiologists should collaborate to inform teachers, help reduce classroom noise, increase signal levels, and improve access to spoken language for L2 learners.

scholarly journals Design and Optimization for UAV-Enabled Two-Way Relaying System with SWIPT

2020 ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two userequipment exchange information with the help of UAV relay and harvest energythrough power splitting (PS) scheme. Under the transmission power constraintsat UEs and UAV relay, a non-convex intractable optimization problem isformulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factors optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of two proposed schemes.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

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