Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR

Niu, Wenlong; Zheng, Wei; Yang, Zhongguo; Wu, Yong; Vágvölgyi, Balázs; Liu, Bo

doi:10.1109/lgrs.2017.2781229

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…Recently, Liu et al used FFT and KL to calculate the similarity between the TP and waveform to detect the target signal [37]. Niu et al proposed detection methods based on statistical distribution distance involving high-frame-rate detection [38][39][40]. These methods are effective for TPs with a high SNR, but for TPs with a low SNR, the target signal cannot be separated from the background signal, and the time when the target appears in the TP cannot be identified.…”

Section: Tp-based Detection Methodsmentioning

confidence: 99%

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

et al. 2023

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Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.

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Section: Tp-based Detection Methodsmentioning

confidence: 99%

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

et al. 2023

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“…NAF is a variant of the median-modified Wiener filter. Besides, Niu et al [41] propose a higher order statistical method based on a traditional bispectrum-based transient signal detector to detect moving targets in high-frame-rate image sequences.…”

Section: B Multi-frame Detection Methodsmentioning

confidence: 99%

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao

Lin

2019

IEEE Access

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Infrared small target detection is challenging due to the various background and low signalto-clutter ratios. Considering the information deficiency faced by single spatial or temporal information, we construct a low false alarm spatial and temporal filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a temporal variance filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate. INDEX TERMS Infrared image sequence, small target detection, local contrast, temporal profile.

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“…Scholars pay attention to the analysis in time-frequency domain [5]. Niu et al [6] applies higher order statistics (HOS) on the detection of moving point target, which has good effect in very low signal-tonoise ratio (SNR), but the calculation of HOS is complicated. In literature [7], wavelet transform (WT) is used to analyze fault diagnosis in rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

Gao

et al. 2019

IEEE Access

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The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signalto-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at −4 dB, 99.82% at 6 dB. INDEX TERMS Radar signal recognition, non-negative matrix factorization network, transfer learning, feature fusion, improved artificial bee colony algorithm.

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Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR

Cited by 19 publications

References 26 publications

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

A Radar Signal Recognition System Based on Non-Negative Matrix Factorization Network and Improved Artificial Bee Colony Algorithm

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