Advanced Digital Signal Processing for Hybrid Lidar FY 2012

Abstract

The technical objective of this project is the development and evaluation of various digital signal processing (DSP) algorithms that will enhance hybrid lidar performance. Practical algorithms must be developed taking into account the underwater propagation channel and the processing requirements for each algorithm. In general, it is desired to determine which combination of Radio Frequency (RF) modulation frequencies, modulation waveforms, and signal processing algorithms help improve hybrid lidar-radar performance in a variety of underwater environments. The approach is to focus on the optical proximity detector that is being developed with ONR funding. The goal is to replace analog hardware with digital components to benefit from the advantages offered with digital hardware and signal processing, including better sensitivity due to large dynamic range digitizers and lossless digital demodulation and filtering, reconfigurability via software to improve sensor adaptability in different environments and for multiple applications, and real-time processing for instant feedback. This year we developed a new backscatter reduction technique that leverages spatial filtering techniques developed to enhance the performance of through the wall imaging (TTWI) radar.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2012
Accession Number
ADA628172

Entities

People

  • William D. Jemison

Organizations

  • Clarkson University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Backscatter Reduction
  • Backscattering
  • Delay Lines
  • Detection
  • Detectors
  • Digital Signal Processing
  • Dynamic Range
  • Environment
  • Experimental Data
  • Frequency
  • Hybrid Lidar-Radar
  • Modulation
  • Radar
  • Radio Frequency
  • Signal Processing
  • Spatial Filtering

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Image Processing and Computer Vision.