
Sparse Representations for Radar with MATLAB Examples by Peter Knee
Although the field of sparse representations is relatively new, research activities in academic and industrial research labs are already producing encouraging results. The sparse signal or parameter model motivated several researchers and practitioners to explore high complexity/wide bandwidth applications such as Digital TV, MRI processing, and certain defense applications. The potential signal processing advancements in this area may influence radar technologies. This book presents the basic mathematical concepts along with a number of useful MATLAB® examples to emphasize the practical implementations both inside and outside the radar field. Table of Contents: Radar Systems: A Signal Processing Perspective / Introduction to Sparse Representations / Dimensionality Reduction / Radar Signal Processing Fundamentals / Sparse Representations in Radar-
Adaptive High-Resolution Sensor Waveform Design for Tracking
-
Cognitive Fusion for Target Tracking
-
Secure Sensor Cloud
-
Advances in Modern Blind Signal Separation Algorithms
-
Despeckle Filtering for Ultrasound Imaging and Video, Volume II
-
OFDM Systems for Wireless Communications
-
Bandwidth Extension of Speech Using Perceptual Criteria
-
Latency and Distortion of Electromagnetic Trackers for Augmented Reality Systems
-
Control Grid Motion Estimation for Efficient Application of Optical Flow
-
Theory and Applications of Gaussian Quadrature Methods
-
Engineer Your Software!
-
Despeckle Filtering for Ultrasound Imaging and Video, Volume I
-
A Survey of Blur Detection and Sharpness Assessment Methods
-
Advances in Waveform-Agile Sensing for Tracking
-
Virtual Design of an Audio Lifelogging System
-
Analysis of the MPEG-1 Layer III (MP3) Algorithm using MATLAB
-
Algorithms and Software for Predictive and Perceptual Modeling of Speech
-
MATLAB Software for the Code Excited Linear Prediction Algorithm
-
Sensor Analysis for the Internet of Things
Peter A. Knee received a B.S. (with honors) in electrical engineering from the University of New Mexico, Albuquerque, New Mexico, in 2006, and an M.S. degree in electrical engineering from Arizona State University in 2010. While at Arizona State University, his research included the analysis of high-dimensional Synthetic Aperture Radar (SAR) imagery for use with Automatic Target Recognition (ATR) systems as well as dictionary learning and data classification using sparse representations. He is currently an employee at Sandia National Laboratories in Albuquerque, New Mexico, focusing on SAR image analysis and software defined radios
| SKU | Unavailable |
| ISBN 13 | 9783031003912 |
| ISBN 10 | 3031003918 |
| Title | Sparse Representations for Radar with MATLAB Examples |
| Author | Peter Knee |
| Series | Synthesis Lectures On Algorithms And Software In Engineering |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2012-11-06 |
| Number of pages | 71 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |


















