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2022 ◽  
Vol 14 (2) ◽  
pp. 365
Author(s):  
Yan Wang ◽  
Rui Min ◽  
Zegang Ding ◽  
Tao Zeng ◽  
Linghao Li

Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations.


2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Fabrizio Mazziotti ◽  
Demetrio Logoteta ◽  
Giuseppe Iannaccone

Author(s):  
Moein , Ahmadi ◽  
Kamal Mohamed-Pour

In this paper, we consider the signal model and parameter estimation for multiple-input multiple-output (MIMO) radar with colocated antennas on stationary platforms. Considering internal clutter motion, a closed form of the covariance matrix of the clutter signal is derived. Based on the proposed closed form and low rank property of the clutter covariance matrix and by using the singular value decomposition, we have proposed a subspace model for the clutter signal. Following the proposed signal model, we have provided maximum likelihood (ML) estimation for its unknown parameters. Finally, the application of the proposed ML estimation in space time adaptive processing (STAP) is investigated in simulation results. Our ML estimation needs no secondary training data and it can be used in scenarios with nonhomogeneous clutter in range.


2022 ◽  
Vol 14 (1) ◽  
pp. 221
Author(s):  
Weike Feng ◽  
Jean-Michel Friedt ◽  
Pengcheng Wan

A fixed-receiver mobile-transmitter passive bistatic synthetic aperture radar (MF-PB-SAR) system, which uses the Sentinel-1 SAR satellite as its non-cooperative emitting source, has been developed by using embedded software-defined radio (SDR) hardware for high-resolution imaging of the targets in a local area in this study. Firstly, Sentinel-1 and the designed system are introduced. Then, signal model, signal pre-processing methods, and effective target imaging methods are presented. At last, various experiment results of target imaging obtained at different locations are shown to validate the developed system and the proposed methods. It was found that targets in a range of several kilometers can be well imaged.


2022 ◽  
pp. 105366
Author(s):  
Lin Cheng ◽  
Hongliang Lu ◽  
Minjie Xia ◽  
Wei Cheng ◽  
Yuming Zhang ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jie Gao

In order to overcome the problems of low error capture accuracy and long response time of traditional spoken French error correction algorithms, this study designed a French spoken error correction algorithm based on machine learning. Based on the construction of the French spoken pronunciation signal model, the algorithm analyzes the spectral features of French spoken pronunciation and then selects and classifies the features and captures the abnormal pronunciation signals. Based on this, the machine learning network architecture and the training process of the machine learning network are designed, and the operation structure of the algorithm, the algorithm program, the algorithm development environment, and the identification of oral errors are designed to complete the correction of oral French errors. Experimental results show that the proposed algorithm has high error capture accuracy and short response time, which prove its high efficiency and timeliness.


2021 ◽  
Author(s):  
Subhra Sankar Dhar

<p>The parameters in the well-known chirp signal model controls the frequency fluctuations of the signals, and consequently, the estimation of the parameters has received considerable attention in the literature of statistical signal processing. In the same spirit with a broader view, this article investigates the quantile estimator of parameters involved in the chirp signal model, which enables us to provide basic features of the entire distribution of the signals. In the course of this study, we establish the limiting behaviour of the associated stochastic process, which we call quantile process. As the applications of this result, we obtain the limiting distributions of various quantile based measures of descriptive statistics, which give us summarized features of the fluctuations of the signals in various senses. Finally, along with extensive simulation study, the practicability of the proposed methodology is shown on a few benchmark real datasets closely related with various chirp signal models.<br></p>


2021 ◽  
Vol 13 (24) ◽  
pp. 5013
Author(s):  
Florian Bischeltsrieder ◽  
Markus Peichl ◽  
Wolfgang Utschick

In harmonic radar applications, images produced using algorithms of conventional radar applications experience some defocusing effects of the electronic targets’ impulse responses. This is typically explained by the dispersive transfer functions of the targets. In addition, it was experimentally observed that objects with a linear transfer behavior do not contribute to the received signal of a harmonic radar measurement. However, some signal contributions based on a multipath propagation can overlay the desired signal, which leads to an undesired and unusual interference caused by the nonlinear character of the electronic targets. Here, motivated by the analysis of measured harmonic radar data, the effects of both phenomena are investigated by theoretical derivations and simulation studies. By analyzing measurement data, we show that the dispersion effects are caused by the target and not by the measurement system or the measurement geometry. To this end, a signal model is developed, with which it is possible to describe both effects, dispersion and multipath propagation. In addition, the discrepancy between classic radar imaging and harmonic radar is analyzed.


2021 ◽  
Author(s):  
Subhra Sankar Dhar

<p>The parameters in the well-known chirp signal model controls the frequency fluctuations of the signals, and consequently, the estimation of the parameters has received considerable attention in the literature of statistical signal processing. In the same spirit with a broader view, this article investigates the quantile estimator of parameters involved in the chirp signal model, which enables us to provide basic features of the entire distribution of the signals. In the course of this study, we establish the limiting behaviour of the associated stochastic process, which we call quantile process. As the applications of this result, we obtain the limiting distributions of various quantile based measures of descriptive statistics, which give us summarized features of the fluctuations of the signals in various senses. Finally, along with extensive simulation study, the practicability of the proposed methodology is shown on a few benchmark real datasets closely related with various chirp signal models.<br></p>


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