Hongyan Xing

Bio: Hongyan Xing is an academic researcher. The author has contributed to research in topics: Clutter & Wavelet packet decomposition. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

Abstract: In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

A Novel Method for CFAR Detector with Bi-Thresholds in Sea Clutter

Abstract: It is confirmed by the field test that the power density of sea clutter received by the coherent radar varies in the Doppler domain,while the distribution of the amplitude of the Doppler frequency component can be mathematically modeled by the Rayleigh distribution or the Weibull distribution.A novel method for the constant false alarm rate(CFAR) detector with bi-thresholds is proposed.The proposed detector is implemented with a serial structure and has the ability for both threshold estimation and the CFAR.In addition,the proposed detector can tremendously outperform the traditional column window detector with nearly the same computational complexity.The simulation results show that the proposed method can achieve a gain of 9 dB in the term of the signal-to-noise ratio(SNR) compared to the column window detector with the false alarm probability of 10-5 and the detection probability of 90%.

Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm

Abstract: On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.

Artificial Intelligence in Marine Science and Engineering

Abstract: This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e [...]

Elevator Car Vibration Signal Denoising Method Based on CEEMD and Bilateral Filtering

Abstract: Elevator car vibration signals are important information to monitor and diagnose the operating status of elevators, but during the process of vibration signals acquisition, vibration signals are always inevitably disturbed by noise, which affects further research. Therefore, aiming at the vibration signal with noise, we propose a new vibration signal denoising method on the basis of complementary ensemble empirical mode decomposition (CEEMD) and bilateral filtering. Firstly, the collected original vibration signals are decomposed by the CEEMD into several inherent mode functions. Then, the false components are removed by determining the correlation coefficients of mode components, and then the noise-dominate components are denoised by bilateral filtering. Finally, the processed inherent mode functions are reconstructed to require the denoised signal. We test the method through simulation and practical application. The results indicate that the proposed method can efficaciously reduce the noise in the vibration signal of an elevator car.