Generation of coherent lognormal clutter with specified power spectrum using ZMNL transform

Xie, Junhao; Jia, Dongdong; Xi, Kun; Yao, Xiaoman; Wang, Zexun; Zhou, Wei

doi:10.1016/j.dsp.2018.02.001

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Clutter from the second pulse In clutter simulation, the whole working time memory should be developed [10] . After the clutter at different times is coherently superimposed by distance, the clutter of one-dimensional time axis is generated, and then the receiver gate is sliced into a two-dimensional matrix [11] . The specific steps are as follows:…”

Section: Large Span Heavy Algorithm Designmentioning

confidence: 99%

Simulation method for large span heavy sea clutter in pulsed Doppler radar

Chen,

Hao,

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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The clutter generated by multiple transmitting pulses overlaps in the time domain, which results in the two-dimensional aliasing of the range domain and Doppler spectrum of the clutter for the medium and high repetition frequency radar. In the simulation of different battlefield environments, a large number of range-Doppler grid units will be generated. It is necessary to establish a simulation model of aircraft based on GPU-CPU architecture to solve the track, attitude and radar parameters in real time to generate grid units, and then add antenna and sea situation information to calculate the cross-heavy sea clutter. This is of great significance for rapidly generating large span heavy sea clutter and improving the speed and reliability of sea clutter simulation.

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Section: Large Span Heavy Algorithm Designmentioning

confidence: 99%

Simulation method for large span heavy sea clutter in pulsed Doppler radar

Chen,

Hao,

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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“…As a result, the square of a linear system's transfer function is equivalent to the value obtained by dividing the power spectral density of the output process by that of the input process. ZMNL can obtain the following method principle based on the statistical characteristics of linear systems [21]:…”

Section: Preliminarymentioning

confidence: 99%

“…For the purpose of simulating the data of sea clutter, it is necessary to generate N sample data which meets the following two conditions in advance: (1) The sample data is distributed according to a specific amplitude; (2) The sample data owns certain spectral features [21]. The essential idea of ZMNL lies in generating related random Gaussian sequences first, and then conducting some nonlinear transformation to obtain relevant random specifically distributed sequences.…”

Section: Preliminarymentioning

confidence: 99%

“…There should be an angle of 90 • FIGURE 1. Schematic diagram of ZMNL [21]. Wherein, V i represents white Gaussian noise, H(w) represents a linear digital filter, S v (w) represents the initial array of linear systems, W i represents a random result input sequence, S w (w) represents the system amplitude frequency function power, and Z i represents the output sequence of the calculation result.…”

Section: A Study On Fitting Characteristics Of Semi-empirical Models and Nathanson Measured Datamentioning

confidence: 99%

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A Semi-Empirical Model Of Sea Clutter Based On Zero Memory Nonlinearity

Fan

Zhang

et al. 2019

IEEE Access

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Based on the computer simulation technology of zero memory nonlinearity (ZMNL), this paper combines with the backscattering features of sea clutter and conducts simulation for four typical backscattering coefficient empirical models of sea clutter, namely, Technology Service Corporation, Georgia Institutes of Technology, Hybrid Sea Clutter Model, and Naval Research Laboratory. According to the results, the signals of sea clutter simulated by zero memory nonlinearity are able to well satisfy the requirements of spectrum characteristics and amplitude distribution according to experiments on the fitting characteristics of simulated signals. After that, the above-mentioned sea clutter scattering coefficient fitting characteristics are applied to the finite difference time domain (FDTD) electromagnetic scattering equation of the numerical method, and the optimal recursive solution to the backward scattering area fitted by the above four semi-empirical models is proposed for the first time. Then, the method of discretizing the power spectrum equation of the differential form is combined with the FDTD discrete form. The numerical results of discrete simulation take the form of the random phase ω between 0 and 2π and then are converted into a basic iteration of one-dimensional rough length, thus realizing time domain power spectrum inversion. Finally, combined with the above conclusions, this paper proposes a new statistical model based on sea surface backscatter coefficient to invert the two-dimensional sea surface, that is, the three-parameter statistical model. At the same time, this paper further deduces the backscattering function of sea clutter based on random distribution and points out that the incident frequency and the rubbing angle are the main factors affecting the sea clutter scattering model. Based on this conclusion, this paper combines the sea clutter estimation model and the directional function model to simulate a two-dimensional random rough sea surface satisfying the physical laws of the ocean.INDEX TERMS Zero memory nonlinearity, empirical models, finite difference time domain, twodimensional sea surface.

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“…The spherically invariant random process (SIRP) method [30][31][32] and the zero-memory nonlinearity (ZMNL) method [32][33][34] are commonly used methods in sea clutter reconstruction. The ZMNL method is suitable for the reconstruction of clutter with integral shape parameter, but it often produces errors for non-integers or semi-integers.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial–Temporal Correlation

Ding

Quan

et al. 2020

Sensors

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The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial–temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial–temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

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Generation of coherent lognormal clutter with specified power spectrum using ZMNL transform

Cited by 10 publications

References 7 publications

Simulation method for large span heavy sea clutter in pulsed Doppler radar

Simulation method for large span heavy sea clutter in pulsed Doppler radar

A Semi-Empirical Model Of Sea Clutter Based On Zero Memory Nonlinearity

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial–Temporal Correlation

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