DOA Estimation for HFSWR Target Based on PSO-ELM

Zhang, Ling; Shi, Chenlu; Niu, Jiong; Ji, Yonggang; Wu, Qi

doi:10.1109/lgrs.2021.3062373

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…As shown in Figure 17, as the number of neurons increases, the overall trend of the test results shows that the RMSE of ELM, PSO-ELM, and EDPO-ELM presents a trend of decreasing and then increasing, while the test accuracy presents a trend of first increasing and then decreasing. When the number of hidden layer neurons is within (27,35), the RMSE of the three methods is small, the test accuracy is greater than 80%, and the number of neurons in this region is the most favorable to the test results of the network. In addition, it is evident that the EDPO-ELM network is optimal overall.…”

Section: Parameter Settingmentioning

confidence: 99%

“…When the number of hidden layer neurons is within (27,35), the RMSE of the three methods is small, the test accuracy is greater than 80%, and the number of neurons in this region is the most favorable to the test results of the network. In addition, it is evident that the EDPO-ELM network is optimal overall.…”

Section: Parameter Settingmentioning

confidence: 99%

“…However, if the network model is constructed by simply initializing the parameters randomly, there are disadvantages, such as node redundancy and overload, reducing performance. Zhang et al [27] used the particle swarm optimization (PSO) algorithm to optimize the parameters in the ELM network and improve azimuth estimation accuracy. However, this algorithm also has problems, such as poor local search ability and ease of falling into local extremes.…”

Section: Introductionmentioning

confidence: 99%

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APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

Wang,

Yu,

Zhang

et al. 2023

Remote Sensing

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Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.

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Section: Parameter Settingmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

Wang,

Yu,

Zhang

et al. 2023

Remote Sensing

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“…erefore, we adopt the strategy of training each DOAp subnetwork independently. Even so, to ensure the DOAp subnetwork estimation accuracy, the amount of training data required by conventional training methods also increases significantly with p. Given that DOA estimation of p sources and p+1 sources are two different but related tasks, we adopt parametertransfer learning [34] to train DOAp subnetworks sequentially from p � 1 to P, and training sets are generated separately according to (12). First of all, we construct a single source training set to train the DOA1 subnetwork.…”

Section: Doa Networkmentioning

confidence: 99%

“…Many DOA estimation methods based on neural networks have been developed in recent years to reduce the computational burden. References [12][13][14][15] using a convolutional neural network (CNN), references [16,17] using a support vector regression (SVR), reference [18] using a residual network, reference [19] using a fully connected neural network (FNN), reference [20] using a long short-term memory network, and references [21,22] using a radial basis function (RBF) achieve high accuracy DOA estimation. However, they can only be used in single-source scenarios, which may be extremely limited in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Chen

Joo

2022

International Journal of Antennas and Propagation

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This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

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