Bayesian Recursive Estimation with Sampled IR Data.

Abstract

A Bayesian nonlinear filter is presented for sampled infrared (IR) sensor data processing. The filter estimates the position of the noise-corrupted target signal located within the sensor's field of view. The filter is optimal in the sense of minimizing the mean-square estimation errors. Monte Carlo simulation results are presented. The results show that the filter works well even under very low signal-to-noise ration (SNR) conditions.

Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1974
Accession Number
ADA002690

Entities

People

  • Kenneth K. Wong

Organizations

  • The Aerospace Corporation

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Data Processing
  • Data Science
  • Image Processing
  • Information Processing
  • Information Science
  • Monte Carlo Method
  • Radar Target Position Simulators
  • Simulations
  • Simulators

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Astronomy/Astrophysics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms