An Improved Wavelet Denoising Algorithm for Wideband Radar Targets Detection

Meng, Xiandong; He, Zhiyuan; Feng, Ganzhong; Xiao, Bo

doi:10.1007/s00034-013-9549-8

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…Based on the discrete wavelet transform (DWT), various wavelet denoising methods like wavelet shrinkage [1]–[16], wavelet coefficient modeling [17]–[20], and wavelet transform modulus maxima (WTMM) [21]–[23] denoising methods have been developed and shown to be more effective than filtering methods [24], [25]. …”

Section: Introductionmentioning

confidence: 99%

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

2016

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A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of- the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method 1) provides a method to select the number of decomposition levels to denoise, 2) uses a new formula to calculate noise thresholds that does not require noise estimation, 3) uses separate noise thresholds for positive and negative wavelet coefficients, 4) applies denoising to the Approximation component, and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance (cw-ESR) spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. Also, its computation time is more than 6 times faster.

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Section: Introductionmentioning

confidence: 99%

A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds

2016

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“…Other approaches spatially or temporally decompose radar signals by wavelet transforms [16, 17]. This way, we can find patterns distributed on space and time domain to improve targets detection efficiency.…”

Section: Related Workmentioning

confidence: 99%

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

Protopapadakis

Voulodimos

Doulamis

et al. 2017

Computational Intelligence and Neuroscience

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Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology's performance.

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“…The wavelet transform is a signal processing technique that represents a transient or non-stationary signal in terms of time and scale distribution, which is an excellent tool for on-line data compression, analysis and reducing, etc. [7] , [8] . In this section, we review some basic concepts and definitions of undecimated wavelet transform that are important in the content of this paper.…”

Section: Principlesmentioning

confidence: 99%