Feature estimation performance using a two-dimensional parametric model of radar scattering

1997 ◽  
Author(s):  
Andria van der Merwe ◽  
Mike J. Gerry ◽  
Lee C. Potter ◽  
Inder J. Gupta
Keyword(s):  
Parametric Model ◽  
Two Dimensional ◽  
Estimation Performance ◽  
Radar Scattering ◽  
Feature Estimation

scholarly journals Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1914
Author(s):  
Jian Xie ◽  
Qiuping Wang ◽  
Yuexian Wang ◽  
Xin Yang
Keyword(s):  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Array Signal Processing ◽  
Elevation Angle ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rank Reduction ◽  
Communication Signals ◽  
Estimation Performance ◽  
Direction Cosines

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

A GTD-based parametric model for radar scattering

1995 ◽  
Vol 43 (10) ◽  
pp. 1058-1067 ◽  
Author(s):  
L.C. Potter ◽  
Da-Ming Chiang ◽  
R. Carriere ◽  
M.J. Gerry
Keyword(s):  
Parametric Model ◽  
Radar Scattering

scholarly journals Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huaxin Yu ◽  
Xiaofeng Qiu ◽  
Xiaofei Zhang ◽  
Chenghua Wang ◽  
Gang Yang
Keyword(s):  
Compressive Sensing ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Rectangular Array ◽  
Angle Estimation ◽  
Estimation Performance ◽  
Trilinear Model ◽  
2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

scholarly journals A parametric model for barred equilibrium beach profiles: Two-dimensional implementation

2016 ◽  
Vol 117 ◽  
pp. 166-175 ◽  
Author(s):  
Robert A. Holman ◽  
David M. Lalejini ◽  
Todd Holland
Keyword(s):  
Parametric Model ◽  
Two Dimensional

scholarly journals Development and implementation of the linear phase algorithm in a two-dimensional sun-sensor

2021 ◽  
Author(s):  
Albert Kar-Kei Yam
Keyword(s):  
Embedded System ◽  
Performance Testing ◽  
Peak Position ◽  
Position Estimation ◽  
Dimensional System ◽  
Two Dimensional ◽  
Linear Phase ◽  
Estimation Performance ◽  
Sun Sensor ◽  
Two Dimensional System

The sub-pixel peak position estimation performance of the Linear Phase algorithm was enhanced and extended for use with a two-dimensional sensor. The two-dimensional system is composed of orthogonal pairs of slits diffracting light onto a single pixel array. The adaptation of the Linear Phase algorithm into the two-dimensional system was performed by manipulating the sensor image into two one-dimensional images. Performance testing used simulated sensor output and images attained from a real sun-sensor. The Linear Phase algorithm was then implemented into an embedded system to simulate onboard processing. A C8051 microcontroller was used as the embedded microcontroller. Results from the embedded processing were compared against the native Matlab implementation for consistency in sub-pixel peak position estimation performance. The Linear Phase algorithm was able to perform with excellent results, comparable to current two-dimensional sun-sensor algorithms.

scholarly journals Automatic Clustering of Flow Cytometry Data with Density-Based Merging

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
Guenther Walther ◽  
Noah Zimmerman ◽  
Wayne Moore ◽  
David Parks ◽  
Stephen Meehan ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Parametric Model ◽  
Two Dimensional ◽  
Laboratory Setting ◽  
Flow Cytometry Data ◽  
Current Limit ◽  
Automatic Clustering ◽  
Automated Tools ◽  
A Current ◽  
Number Of Cells

The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

scholarly journals Development and implementation of the linear phase algorithm in a two-dimensional sun-sensor

2021 ◽  
Author(s):  
Albert Kar-Kei Yam
Keyword(s):  
Embedded System ◽  
Performance Testing ◽  
Peak Position ◽  
Position Estimation ◽  
Dimensional System ◽  
Two Dimensional ◽  
Linear Phase ◽  
Estimation Performance ◽  
Sun Sensor ◽  
Two Dimensional System

The sub-pixel peak position estimation performance of the Linear Phase algorithm was enhanced and extended for use with a two-dimensional sensor. The two-dimensional system is composed of orthogonal pairs of slits diffracting light onto a single pixel array. The adaptation of the Linear Phase algorithm into the two-dimensional system was performed by manipulating the sensor image into two one-dimensional images. Performance testing used simulated sensor output and images attained from a real sun-sensor. The Linear Phase algorithm was then implemented into an embedded system to simulate onboard processing. A C8051 microcontroller was used as the embedded microcontroller. Results from the embedded processing were compared against the native Matlab implementation for consistency in sub-pixel peak position estimation performance. The Linear Phase algorithm was able to perform with excellent results, comparable to current two-dimensional sun-sensor algorithms.

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