scholarly journals HIGH RESOLUTION RADAR FOCUSING USING SPECTRAL ESTIMATION METHODS IN WIDE-BAND AND NEAR-FIELD CONFIGURATIONS: APPLICATION TO MILLIMETER-WAVE NEAR-RANGE IMAGING

2017 ◽  
Vol 79 ◽  
pp. 45-64
Author(s):  
Antoine Jouade ◽  
Laurent Ferro-Famil ◽  
Stephane Meric ◽  
Olivier Lafond ◽  
Laurent Le Coq
Keyword(s):  
High Resolution ◽  
Millimeter Wave ◽  
Near Field ◽  
Spectral Estimation ◽  
Wide Band ◽  
Estimation Methods ◽  
Range Imaging ◽  
High Resolution Radar

High‐resolution velocity spectra using eigenstructure methods

Geophysics ◽  
1989 ◽  
Vol 54 (7) ◽  
pp. 832-842 ◽  
Author(s):  
Biondo L. Biondi ◽  
Clement Kostov
Keyword(s):  
High Resolution ◽  
Covariance Matrix ◽  
Spectral Estimation ◽  
Wide Band ◽  
Geometric Interpretation ◽  
Maximum Likelihood Estimates ◽  
Estimation Methods ◽  
Improved Performance ◽  
Parsimonious Model ◽  
Velocity Spectra

Stacking spectra provide maximum‐likelihood estimates for the stacking velocity, or for the ray parameter, of well separated reflections in additive white noise. However, the resolution of stacking spectra is limited by the aperture of the array and the frequency of the data. Despite these limitations, parametric spectral estimation methods achieve better resolution than does stacking. To improve resolution, the parametric methods introduce a parsimonious model for the spectrum of the data. In particular, when the data are modeled as the superposition of wavefronts, the properties of the eigenstructure of the data covariance matrix can be used to obtain high‐resolution spectra. The traditional stacking spectra can also be expressed as a function of the data covariance matrix and directly compared to the eigenstructure spectra. The superiority of the latter in separating closely interfering reflections is then apparent from a simple geometric interpretation. Eigenstructure methods were originally developed for use with narrow‐band signals, while seismic reflections are wide‐band and transient in time. Taking advantage of the full bandwidth of seismic data, we average spectra from several frequency bands. We choose each frequency band wide enough, so that we can average over time estimates of the covariance matrix. Thus, we obtain a robust estimate of the covariance matrix from short data sequences. A field‐data example shows that the high‐resolution estimators are particularly attractive for use in the estimation of local spectra in which short arrays are considered. Several realistic synthetic examples of stacking‐velocity spectra illustrate the improved performance of the new methods in comparison with conventional processing.

Weakly Inhomogeneous Media Tomography

2003 ◽  
Vol 25 (2) ◽  
pp. 122-133 ◽  
Author(s):  
Robert Ferrière ◽  
Serge Mensah ◽  
Jean-Pierre Lefebvre
Keyword(s):  
High Resolution ◽  
Breast Imaging ◽  
Near Field ◽  
Wide Band ◽  
Field Zone ◽  
Reconstruction Procedure ◽  
Total Recovery ◽  
Spherical Waves ◽  
Good Penetration ◽  
Ultrasonic Scanner

Our objective is to develop an ultrasonic scanner for breast imaging. High resolution is obtained by using wide-band spherical waves transmitted and measured in the near field zone (i.e., close to the skin) all around the organ. The tomographic approach that we adopt allows us to use low central frequency waves (3–7 MHz) that are suitable for good penetration while maintaining high resolution and contrast. The procedure is thus suitable for early detection of tumors and increases the chances of total recovery. The novelty of the present reconstruction procedure is that it associates the signals acquired in transmission to the data measured in reflection over a large aperture. This enables us to correct the phase aberration induced by weak inhomogeneities whose sizes might be several wavelengths. Numerical tests based on Finite Difference Time Domain (FDTD) simulations demonstrate the greater fidelity of the reconstruction.

scholarly journals 3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation

2021 ◽  
Vol 13 (17) ◽  
pp. 3366
Author(s):  
Shunjun Wei ◽  
Zichen Zhou ◽  
Mou Wang ◽  
Jinshan Wei ◽  
Shan Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Millimeter Wave ◽  
Imaging System ◽  
Near Field ◽  
Center Frequency ◽  
W Band ◽  
Wave Radar ◽  
Imaging Results ◽  
Different Types ◽  
Imaging Algorithms

Millimeter-wave (MMW) 3-D imaging technology is becoming a research hotspot in the field of safety inspection, intelligent driving, etc., due to its all-day, all-weather, high-resolution and non-destruction feature. Unfortunately, due to the lack of a complete 3-D MMW radar dataset, many urgent theories and algorithms (e.g., imaging, detection, classification, clustering, filtering, and others) cannot be fully verified. To solve this problem, this paper develops an MMW 3-D imaging system and releases a high-resolution 3-D MMW radar dataset for imaging and evaluation, named as 3DRIED. The dataset contains two different types of data patterns, which are the raw echo data and the imaging results, respectively, wherein 81 high-quality raw echo data are presented mainly for near-field safety inspection. These targets cover dangerous metal objects such as knives and guns. Free environments and concealed environments are considered in experiments. Visualization results are presented with corresponding 2-D and 3-D images; the pixels of the 3-D images are 512×512×6. In particular, the presented 3DRIED is generated by the W-band MMW radar with a center frequency of 79GHz, and the theoretical 3-D resolution reaches 2.8 mm × 2.8 mm × 3.75 cm. Notably, 3DRIED has 5 advantages: (1) 3-D raw data and imaging results; (2) high-resolution; (3) different targets; (4) applicability for evaluation and analysis of different post processing. Moreover, the numerical evaluation of high-resolution images with different types of 3-D imaging algorithms, such as range migration algorithm (RMA), compressed sensing algorithm (CSA) and deep neural networks, can be used as baselines. Experimental results reveal that the dataset can be utilized to verify and evaluate the aforementioned algorithms, demonstrating the benefits of the proposed dataset.

scholarly journals Coherent Integration Loss Due to Nonstationary Phase Noise in High-Resolution Millimeter-Wave Radars

2021 ◽  
Vol 13 (9) ◽  
pp. 1755
Author(s):  
Chagai Levy ◽  
Monika Pinchas ◽  
Yosef Pinhasi
Keyword(s):  
High Resolution ◽  
Phase Noise ◽  
Millimeter Wave ◽  
Target Location ◽  
Moving Targets ◽  
Noise Effects ◽  
Time Frequency ◽  
Coherent Integration ◽  
Radar Systems ◽  
High Resolution Radar

Phase noise refers to the instability of an oscillator, which is the cause of instantaneous phase and frequency deviations in the carrier wave. This unavoidable instability adversely affects the performance of range–velocity radar systems, including synthetic aperture radars (SARs) and ground-moving target indicator (GMTI) radars. Phase noise effects should be considered in high-resolution radar designs, operating in millimeter wavelengths and terahertz frequencies, due to their role in radar capability during the reliable identification of target location and velocity. In general, phase noise is a random process consisting of nonstationary terms. It has been shown that in order to optimize the coherent detection of stealthy, fast-moving targets with a low radar cross-section (RCS), it is required to evaluate the integration gain and to determine the incoherent noise effects for resolving target location and velocity. Here, we present an analytical expression for the coherent integration loss when a nonstationary phase noise is considered. A Wigner distribution was employed to derive the time–frequency expression for the coherent loss when nonstationary conditions were considered. Up to now, no analytical expressions have been developed for coherent integration loss when dealing with real nonstationary phase noise mathematical models. The proposed expression will help radar systems estimate the nonstationary integration loss and adjust the decision threshold value in order to maximize the probability of detection. The effect of nonstationary phase noise is demonstrated for studying coherent integration loss of high-resolution radar operating in the W-band. The investigation indicates that major degradation in the time-frequency coherent integration due to short-term, nonstationary phase noise instabilities arises for targets moving at low velocities and increases with range. Opposed to the conventional model, which assumes stationarity, a significant difference of up to 25 dB is revealed in the integration loss for radars operating in the millimeter wave regime. Moreover, for supersonic moving targets, the loss peaks at intermediate distances and then reduces as the target moves away.

scholarly journals Nonparametric and parametric methods of spectral analysis

2019 ◽  
Vol 283 ◽  
pp. 07002 ◽  
Author(s):  
Hangfang Zhao ◽  
Lin Gui
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
High Resolution ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Stationary Time Series ◽  
Estimation Methods ◽  
Practical Applications ◽  
Small Variance

Spectral Analysis is one of the most important methods in signal processing. In practical application, it is critical to discuss the power spectral density estimation of finite data sampled from some stationary time series. A spectral estimator is expected to have good statistical properties such as consistency, high resolution and small variance. For one spectral estimation method, there exists a trade-off between high resolution and small variance. The paper provides a comparison of several popular spectral methods from both theoretical properties and practical applications. We first address several basic nonparametric methods, whose statistical characters are analysed. Then we explain the connections and differences between temporal windowing and lag windowing. Thereafter, the confidence intervals of both windows are given and used to evaluate the estimated results. Besides, several different parametric estimation methods of autoregressive time series are compared, and whose properties and effects are also introduced. Building on our understanding of these studies, we then apply parametric and nonparametric spectral estimation methods on the data of ocean surface wave height.

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