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1 edition of Optimal Wavelet Denoising for High Range Resolution Radar Classification found in the catalog.

Optimal Wavelet Denoising for High Range Resolution Radar Classification

Optimal Wavelet Denoising for High Range Resolution Radar Classification

  • 348 Want to read
  • 25 Currently reading

Published by Storming Media .
Written in English

    Subjects:
  • TEC033000

  • The Physical Object
    FormatSpiral-bound
    ID Numbers
    Open LibraryOL11848969M
    ISBN 101423546083
    ISBN 109781423546085

    wavelet denoising when the corrupting noise is fractal. We then describe a new method of pulse radar target detection using a recently developed complex-valued Hermitian wavelet. Finally, we examine a new wavelet video method of processing signals for continuous-wave radar fuzes. Radar Signals for Proximity Fuzing. The actual range of a target from the radar is known as slant range. Slant range is the line of sight distance between the radar and the object illuminated. While ground range is the horizontal distance between the emitter and its target and its calculation requires knowledge of the target's elevation.

    Suppose that H and G are low-pass filters and high-pass filters, respectively.C j and D j are the approximate and detail components of the original signal at resolution 2 − j, , we can get. Suppose and be the dual operators of H and G respectively, then we can get. It can be seen from the decomposition and reconstruction of the wavelet that the wavelet transform is.   As we can see in the figure above, the Wavelet transform of an 1-dimensional signal will have two dimensions. This 2-dimensional output of the Wavelet transform is the time-scale representation of the signal in the form of a scaleogram. Above the scaleogram is plotted in a 3D plot in the bottom left figure and in a 2D color plot in the bottom right figure.

      This article proposes high-order balanced multi-band multiwavelet packet transforms for denoising remote sensing images. First, properties of several wavelet transforms and their relationships are analyzed. The article then presents theoretical principles and a fast algorithm for constructing high-order balanced multi-band multiwavelet packet transforms. Radar angular resolution is the minimum distance between two equally large targets at the same range which radar is able to distinguish and separate to each other.. Angular Resolution as Antenna Parameter. The angular resolution characteristics of radar are determined by the antenna beamwidth represented by the -3 dB angle Θ which is defined by the half-power (-3 dB) points.


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Optimal Wavelet Denoising for High Range Resolution Radar Classification Download PDF EPUB FB2

Abstract: We develop a wavelet denoising scheme to aid an automatic target recognition (ATR) system in recognizing aircraft from high range resolution radar (HRR) signatures.

A template matching classification technique is used with templates formed from synthetically generated signatures. The goal of the classification system is to achieve classification accuracy equivalent to that obtained Cited by: Here target classification using high– resolution range (HRR) profiles will be discussed and a subset of the generic classification problem will be analysed.

wavelet denoising suffers a main. Wavelet preprocessing for high range resolution radar classification Article in IEEE Transactions on Aerospace and Electronic Systems 37(4) - November with 11 Reads. The Computational Complexity of Wavelet 17 Transforms 3 RADAR METHODS AND MAIN RESULTS 20 Basic Radar System Operation 20 The Millimeter Radar Data Base 21 Wavelet Approximation to the FFT 29 Target Length Estimation 34 Wavelet Target Classification Results 39 Fine Range Resolution by Wavelets A new target recognition approach with wavelet transform and information fusion is detailed, and tested on backscatter returns of high range resolution (HRR) radar.

This allows very high-resolution (and a small radar range resolution cell) to be obtained with long pulses, thus with a higher average power.

Figure 4 shows the variation of slant range resolution with bandwidth. An m resolution will be achieved with a -3 dB bandwidth of MHz theoretically. Denoising of radar signals by using wavelets and Doppler estimation by S-Transform V.

Siva Sankara Reddy#1and ala Rao#2 Department of Electronics and Communication Engineering #1Dadi Institute of Engineering and Technology, #2GMRIT,India ABSTRACT The s-transform is a variable window of STFT and extension of wavelet.

A high-accuracy algorithm based on combination of bi-dimensional empirical mode decomposition (BEMD) and wavelet denoising is presented in this paper, in which BEMD is adapted to decompose optimal. some radar signal processing using WBD are introduced.

Wavelet base selection is the first step in wavelet decomposition. Whereas symmetric filters decrease distortion, near-symmetric Daubechies family such as Coiflet family is selected.

Selection of denoising algorithm is the second step in wavelet thresholding. Block thresholding is a. Due to the Bark-wavelet and adaptive threshold denoising, the classification algorithm is also robust to the influence of noise. White Gaussian noise is added to the original signal for the simulation of different SNRs.

For comparison, a same system but without denoising algorithm is used as the baseline system. Wavelet-based radar waveforms have been investigated for several applications, including adaptive radar [18], range sidelobe suppression [19], detection of moving targets [20], synthetic aperture.

In this paper, a multi-resolution wavelet threshold denoising method which can achieve radar weak single detection is proposed. The method and the way of the radar weak signal detection which based on the Wavelets transform are described.

The simulation to real radar signal is verified by MATLAB. The good results are obtained that the proposed method effectively improves the signal noise rate. Giorgio Antonino Licciardi, in Data Handling in Science and Technology, Discrete wavelet transform.

The other most popular transform is the wavelet DWT [29] is a widely used technique for the efficient decorrelation of data obtained by splitting the data into two half-rate subsequences, carrying information, respectively, on the approximation and detail of the original.

In recent years, the performances of radar resolution, coverage, and detection accuracy have been significantly improved through the use of ultra-wideband, synthetic aperture and digital signal processing technologies.

High-resolution radars (HRRs) utilize wideband signals and synthetic apertures to enhance the range and angular resolutions of tracking, respectively. The denoising methods based on the wavelet usually transform the image content into multiple sub-bands at different resolution and scales.

Larger frequency coefficients contain the low frequency image information (approximation level) and noise and details exist in high. () An Improved Wavelet Denoising Algorithm for Wideband Radar Targets Detection. Circuits, Systems, and Signal Processing() Coherent Structures at a Forest Edge: Properties, Coupling and Impact of Secondary Circulations.

This paper focuses on the analysis of denoising the Linear Imaging Self Scanning Sensor III (LISS III) images, Advanced Very High Resolution Radiometer (AVHRR) images from National Oceanic and Atmospheric Administration 19 (NOAA 19). The invention discloses a radar target recognition method based on a deep learning network.

The method mainly solves the problems that in the prior art, when a radar high-resolution range image is recognized, the recognition rate is low and robustness is poor. According to the technical scheme, firstly, an obtained original radar high-resolution range image is divided into a training set and a.

The range resolution of a simple pulse-modulated radar depends on the pulse duration. Two reflective objects located within the spatial extent of the pulse are only displayed as one target.

To improve the range resolution for a relatively long transmission pulse duration, the transmission pulse is.

() Localized cultural denoising of high-resolution aeromagnetic data. Geophysical Prospecting() Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network.

Wavelet packet decomposition (Wickerhauser and Coifman, ) is a generalisation of wavelet decomposition at higher frequencies. In the wavelet packet decomposition, each detail coefficient vector is also decomposed into two parts using the same approach as DWT in approximate coefficients.

This offers the analysis at the high frequencies.Prof. David Jenn Department of Electrical & Computer Engineering Dyer Road, Room Monterey, CA () [email protected], [email protected] group of full range of wideband high resolution radar signal features is extracted for analyzing the the optimal sensors nb of radar signals should be solved, the distance of two peak valleys is fl, and the linear regression radar signal length is l.

2 1 (min) (min) b b b b m m.