scholarly journals Maneuvering Target Detection Based on Subspace Subaperture Joint Coherent Integration

2021 ◽  
Vol 13 (10) ◽  
pp. 1948
Author(s):  
Langxu Zhao ◽  
Haihong Tao ◽  
Weijia Chen ◽  
Dawei Song
Keyword(s):  
Signal To Noise Ratio ◽  
Doppler Frequency ◽  
Computation Complexity ◽  
Target Signal ◽  
Coherent Integration ◽  
Range Cell ◽  
Axis Rotation ◽  
Good Trade ◽  
And Performance ◽  
Range Cell Migration

Range cell migration and Doppler frequency migration induced by the target maneuverability are two difficulties of target signal enhancement and radar detection performance. In order to resolve them, a novel subaperture joint coherent integration (SJCI) algorithm is proposed in this article, which consists of three stages. Firstly, it divides the target signal into several subapertures, in which the Doppler frequency dispersions can be neglected. Afterward, coherent integration within each subaperture is implemented via scaled Fourier transform. Finally, correcting the Doppler frequency shifts and phase differences via axis rotation and phase compensation technology, the joint coherent integration among the subapertures can be achieved effectively. Based on the SJCI algorithm, an upgrade algorithm named subspace SJCI (SSJCI) is presented. Through acceleration space division and subspace translation, the SSJCI algorithm extends the subaperture time and optimizes the computation complexity significantly. Theoretical analyses and performance comparisons demonstrate that the SSJCI algorithm can accomplish a good trade-off among signal-to-noise ratio gain, detection capability, resolution, and computation complexity. In addition, the results of the numerical experiments further verify the effectiveness of the proposed algorithm.

scholarly journals A Fast Bistatic ISAR Imaging Approach for Rapidly Spinning Targets via Exploiting SAR Technique

2020 ◽  
Vol 12 (13) ◽  
pp. 2077
Author(s):  
Zhijun Yang ◽  
Dong Li ◽  
Xiaoheng Tan ◽  
Hongqing Liu ◽  
Guisheng Liao
Keyword(s):  
Synthetic Aperture Radar ◽  
Signal To Noise Ratio ◽  
Doppler Frequency ◽  
Synthetic Aperture ◽  
Coherent Integration ◽  
Range Cell ◽  
Isar Imaging ◽  
Imaging Approach ◽  
Spinning Targets ◽  
Aperture Radar

Because of the large range of cell migration (RCM) and nonstationary Doppler frequency modulation (DFM) produced by non-cooperative targets with rapid spinning motions, it is difficult to efficiently generate a well-focused bistatic inverse synthetic aperture radar (ISAR) by use of the conventional imaging algorithms. Utilizing the property of the inherent azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, in this work, an efficient bistatic ISAR imaging approach based on circular shift operation in the range-Doppler (RD) domain is proposed. First, echoes of rapidly spinning targets are transformed into the RD domain, whose exact analytical is derived on the basis of the principle of stationary phase (POSP). Second, the RCM is corrected by using an efficient circular shift operation in the RD domain. By doing so, the energies of a scatterer that span multiple range cells are concentrated into the same range cell, and the time-varying DFM can also be compensated along the rotating radius direction. Compared with existing methods, the proposed method has advantages in its computational complexity, avoiding the interpolation and multi-dimensional search operation, and in its satisfactory imaging performance under low signal to noise ratio (SNR) conditions thanks to the two-dimensional coherent integration gain utilized. Finally, several numerical simulations are conducted to show the validity of the proposed algorithm.

scholarly journals Spatial-Variant SAR Range Cell Migration Correction Using Subaperture Strategy

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2444
Author(s):  
Liping Hu ◽  
Guanyong Wang ◽  
Lin Hou
Keyword(s):  
Cell Migration ◽  
Processing System ◽  
Doppler Frequency ◽  
Image Domain ◽  
Real Time Processing ◽  
Range Cell ◽  
Range Cell Migration ◽  
Main Component ◽  
Two Stages ◽  
The Relationship

The coupling between range and azimuth dimensions is the main obstacle for highly squinted synthetic aperture radar (SAR) data focusing. Range walk correction (RWC) processing is effective to remove the linear coupling term, but the residual high order range cell migration (RCM) parts are spatial-variant in both range and azimuth dimensions. In this paper, we propose a precise spatial-variant range cell migration correction (RCMC) method with subaperture processing. The method contains two stages. Firstly, the main component of range-variant RCM is corrected in the coarse RCMC stage. Secondly, data are derived into azimuth subapertures (SAs), an SA-image-domain RCMC is developed by interp correction, where the SA image is obtained using a modified spectrum analysis (SPECAN) algorithm by establishing the relationship between Doppler frequency and residual spatial-variant RCM. In the proposed algorithm, precise compensation of space-variant RCM is implemented by SA processing, which is designed for a better practicality in real-time processing system. Simulated and real measured data experiments are designed to validate the effectiveness of the proposed approach for highly squinted SAR imaging.

A Novel Anti-Stealth Technique Based on Stratospheric Balloon-Borne Radar in Heterogeneous Environments

Frequenz ◽  
2015 ◽  
Vol 69 (5-6) ◽  
Author(s):  
Mohamed Barbary ◽  
Peng Zong
Keyword(s):  
Detection Technique ◽  
Heterogeneous Environments ◽  
Aspect Angle ◽  
Range Ambiguity ◽  
Coherent Integration ◽  
Range Cell ◽  
Adaptive Processes ◽  
Long Time ◽  
Stratospheric Balloon ◽  
Range Cell Migration

AbstractRadar cross section (RCS) of a stealth target model like F-117A can be improved by multichannel stratospheric balloon-borne bistatic radar at higher aspect angle. The potential problem is that the stealth target may produce range walk in clutter heterogeneous environments, thus it is difficult to determine the range ambiguity under quadratic range cell migration (QRCM). In this paper, a novel detection technique known as hybrid modified fractional-radon Fourier transform (MFrRFT) and knowledge-aided space–time adaptive processes (KA-STAP) is proposed to impact this kind of problem simultaneously. KA-STAP is applied to suppress the non-homogeneous clutter in the received data, and MFrRFT is used to eliminate the QRCM along with the second-order keystone transform (SOKT), so as to estimate the range ambiguity and compensate the stealth target’s range walk. The hybrid MFrRFT/KA-STAP scheme is simple and applicable to the small RCS of fast stealth target with a long-time coherent integration. Finally, to achieve high accuracy of locating stealth target, a non-parametric detection technique based on Legendre orthogonal polynomials is applied to reconstruct the probability density function (pdf) of real RCS data predicted by physical optics (PO) approximation method.

Ego-Noise Suppression for Robots Based on Semi-Blind Infinite Non-Negative Matrix Factorization

2017 ◽  
Vol 29 (1) ◽  
pp. 114-124
Author(s):  
Kazuhiro Nakadai ◽  
◽  
Taiki Tezuka ◽  
Takami Yoshida ◽  
Keyword(s):  
Speech Recognition ◽  
Matrix Factorization ◽  
Noise Suppression ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Humanoid Robots ◽  
Motion Information ◽  
Target Signal ◽  
And Performance ◽  
Non Negative Matrix Factorization

[abstFig src='/00290001/11.jpg' width='300' text='Ego-noise suppression achieves speech recognition even during motion' ] This paper addresses ego-motion noise suppression for a robot. Many ego-motion noise suppression methods use motion information such as position, velocity, and the acceleration of each joint to infer ego-motion noise. However, such inferences are not reliable, since motion information and ego-motion noise are not always correlated. We propose a new framework for ego-motion noise suppression based on single channel processing using only acoustic signals captured with a microphone. In the proposed framework, ego-motion noise features and their numbers are automatically estimated in advance from an ego-motion noise input using Infinite Non-negative Matrix Factorization (INMF), which is a non-parametric Bayesian model that does not use explicit motion information. After that, the proposed Semi-Blind INMF (SB-INMF) is applied to an input signal that consists of both the target and ego-motion noise signals. Ego-motion noise features, which are obtained with INMF, are used as inputs to the SB-INMF, and are treated as the fixed features for extracting the target signal. Finally, the target signal is extracted with SB-INMF using these newly-estimated features. The proposed framework was applied to ego-motion noise suppression on two types of humanoid robots. Experimental results showed that ego-motion noise was effectively and efficiently suppressed in terms of both signal-to-noise ratio and performance of automatic speech recognition compared to a conventional template-based ego-motion noise suppression method using motion information. Thus, the proposed method worked properly on a robot without a motion information interface.**This work is an extension of our publication “Taiki Tezuka, Takami Yoshida, Kazuhiro Nakadai: Ego-motion noise suppression for robots based on Semi-Blind Infinite Non-negative Matrix Factorization, ICRA 2014, pp.6293-6298, 2014.”

scholarly journals Interperiod coherent integration of the received signal with a variable repetition period of the probing signal

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (7) ◽  
pp. 40-48
Author(s):  
S. R. Heister ◽  
P. G. Semashko
Keyword(s):  
Signal To Noise Ratio ◽  
Doppler Frequency ◽  
Practical Implementation ◽  
Repetition Period ◽  
Signal To Noise ◽  
Integration Algorithm ◽  
Coherent Integration ◽  
Probing Signal ◽  
Frequency Survey ◽  
Quantitative Indicators

Interperiod coherent integration of the received signal provides an increase in the signal-to-noise ratio and is simply implemented with a fixed repetition period of the probing signals. In practice, pulsed radars use a variable repetition period to protect against blind speeds. The algorithms of the interperiod coherent integration with a variable repetition period have been developed and their features have been revealed, which are advisable to take into account in the practical implementation in the radars. These features determine the complexity of the interperiod coherent integration algorithm, the radial velocity (Doppler frequency) survey interval and the spectrum features. An algorithm is developed with simultaneous interperiod coherent integration of the received signal and a single-delay clutter cancelation in the spectral domain in the case of variable repetition period of the probing signals. The quantitative indicators obtained by modeling are presented and a comparative analysis is carried out.

Enhancements of Non-contact Measurements of Electrical Waveforms on the Proximity of a Signal Surface Using Groups of Pulses

2002 ◽  
Author(s):  
Fenglei Du ◽  
Greg Bridges ◽  
D.J. Thomson ◽  
Rama R. Goruganthu ◽  
Shawn McBride ◽  
...  
Keyword(s):  
Integrated Circuits ◽  
Integrated Circuit ◽  
Signal To Noise Ratio ◽  
Electrostatic Force ◽  
Single Pulse ◽  
Electric Signal ◽  
Measurement Instruments ◽  
Cycle Times ◽  
Force Microscopy ◽  
And Performance

Abstract With the ever-increasing density and performance of integrated circuits, non-invasive, accurate, and high spatial and temporal resolution electric signal measurement instruments hold the key to performing successful diagnostics and failure analysis. Sampled electrostatic force microscopy (EFM) has the potential for such applications. It provides a noninvasive approach to measuring high frequency internal integrated circuit signals. Previous EFMs operate using a repetitive single-pulse sampling approach and are inherently subject to the signal-to-noise ratio (SNR) problems when test pattern duty cycle times become large. In this paper we present an innovative technique that uses groups of pulses to improve the SNR of sampled EFM systems. The approach can easily provide more than an order-ofmagnitude improvement to the SNR. The details of the approach are presented.

High-Performance Image Filters via Sparse Approximations

2020 ◽  
Vol 3 (2) ◽  
pp. 1-19
Author(s):  
Kersten Schuster ◽  
Philip Trettner ◽  
Leif Kobbelt
Keyword(s):  
High Performance ◽  
Hardware Acceleration ◽  
Optimization Method ◽  
Translation Invariant ◽  
Approximation Quality ◽  
Trade Offs ◽  
Sparse Approximations ◽  
Image Filters ◽  
Good Trade ◽  
And Performance

We present a numerical optimization method to find highly efficient (sparse) approximations for convolutional image filters. Using a modified parallel tempering approach, we solve a constrained optimization that maximizes approximation quality while strictly staying within a user-prescribed performance budget. The results are multi-pass filters where each pass computes a weighted sum of bilinearly interpolated sparse image samples, exploiting hardware acceleration on the GPU. We systematically decompose the target filter into a series of sparse convolutions, trying to find good trade-offs between approximation quality and performance. Since our sparse filters are linear and translation-invariant, they do not exhibit the aliasing and temporal coherence issues that often appear in filters working on image pyramids. We show several applications, ranging from simple Gaussian or box blurs to the emulation of sophisticated Bokeh effects with user-provided masks. Our filters achieve high performance as well as high quality, often providing significant speed-up at acceptable quality even for separable filters. The optimized filters can be baked into shaders and used as a drop-in replacement for filtering tasks in image processing or rendering pipelines.

Enhancement of Radar Imagery by Maximum Entropy Processing

1977 ◽  
Vol 21 (3) ◽  
pp. 241-243 ◽  
Author(s):  
Clanton E. Mancill
Keyword(s):  
Maximum Entropy ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doppler Frequency ◽  
Frequency Measurement ◽  
Entropy Method ◽  
Frequency Resolution ◽  
Sampled Data ◽  
Maximum Entropy Spectrum ◽  
Using Data

The maximum entropy spectrum (MES), a sampled data power spectrum estimator, is applied to the enhancement of imagery obtained by synthetic array radar (SAR) imaging systems. MES offers better frequency resolution than conventional Fourier transform methods for certain signal classes. Since azimuth ground resolution in SAR systems is obtained by doppler frequency measurement of the radar return, the method is capable of enhancing the resolution of SAR maps. The principal signal requirement is adequate signal-to-noise ratio. The maximum entropy method has been tested using data obtained by the Hughes FLAMR radar system. The super-resolution capabilities of the method are demonstrated using FLAMR images of corner reflector arrays.

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