TIME REVERSAL BASED SIGNAL PROCESSING TECHNIQUES

Over last few decades, the time-reversal techniques
have increasingly found applications in various
disciplines ranging from medicine to
telecommunications. One such discipline is the
microwave remote sensing where only a limited
academic literature exists on how to utilize
time-reversal concept for improved sensing
performance. This book, therefore, introduces novel
time-reversal based signal processing techniques
employing ultrawideband electromagnetic waves for the
detection and imaging of obscured objects. Several
distinct algorithms utilizing spatial, frequency and
polarization data have been theoretically and
numerically analyzed and an inverse filter approach
is introduced for the compensation of lossy and
dispersive environment effects. The algorithms
presented here should especially be helpful to
professionals in the fields of Microwave Remote
Sensing and Signal Processing or anyone else who may
consider adapting these algorithms in different
applications such as microwave breast cancer
detection, nondestructive testing or synthetic
aperture radar.

Autorentext

MEHMET EMRE YAVUZ received his PhD degree from The Ohio StateUniversity, Columbus, OH. Currently, he is a PrincipleScientist at Halliburton Technology Research, Houston, Texas. FERNANDO L. TEIXEIRA received his PhD degree from the Universityof Illinois at Urbana-Champaign. Currently, he is an AssociateProfessor at the Ohio State University.

Klappentext

Over last few decades, the time-reversal techniqueshave increasingly found applications in variousdisciplines ranging from medicine totelecommunications. One such discipline is themicrowave remote sensing where only a limitedacademic literature exists on how to utilizetime-reversal concept for improved sensingperformance. This book, therefore, introduces noveltime-reversal based signal processing techniquesemploying ultrawideband electromagnetic waves for thedetection and imaging of obscured objects. Severaldistinct algorithms utilizing spatial, frequency andpolarization data have been theoretically andnumerically analyzed and an inverse filter approachis introduced for the compensation of lossy anddispersive environment effects. The algorithmspresented here should especially be helpful toprofessionals in the fields of Microwave RemoteSensing and Signal Processing or anyone else who mayconsider adapting these algorithms in differentapplications such as microwave breast cancerdetection, nondestructive testing or syntheticaperture radar.