Select courses from list below:
Sampling Realities of Focal Plane Arrays
Practical Applications of Thermal Imaging Systems
Engineering Approach to Imaging Systems Design
Testing and Evaluation of E-O Imaging Systems
Electro-Optical Imaging System Performance

Electro-optical Imaging System Performance

Optimization of an imaging system depends upon a myriad of radiometric, spectral, and spatial parameters. The "bare bones" sensor consists of optics, detector, display, and an observer. Range degrading parameters include 3D noise, optical blur, and pixel interpolation. Scenario parameters include detection/recognition/identification probability, target contrast, target size, line-of-sight motion, and atmospheric conditions. Generally, the customer provides the scenario and the analyst optimizes the sensor parameters to achieve maximum acquisition range. A wide variety of programs have been available in the past (e.g., SSCamIP, NVThermIP etc.). These programs have been consolidated into the Night Vision Integrated Performance Model (NVIPM). For convenience, the calculations are performed in the frequency domain (MTF analysis). This is often called image chain modeling. All equations necessary for modeling are provided. Although the math is sometimes complex, the equations are graphed for easy understanding. NVIPM inputs are high-lighted to match equation parameters. Several optimization examples are provided (case study examples). NVIPM can easily perform trade studies and provides a gradient (sensitivity) analysis.

A simplified model is also presented providing practical engineering solutions. This systematic methodology identifies those components that affect range the most. The basic performance parameter is the ratio of the detector cutoff to the optics cutoff (Fλ/d). Early systems had "large" detectors (Fλ/d < 0.5). These systems were detector limited and acquisition range was inversely proportional to detector size. With "small" detectors (Fλ/d > 1.5) the system is optics limited. Here changing the detector size has minimal effect on range. Selecting a mid-wave (MWIR) or long-wave (LWIR) infrared sensor depends upon Fλ/d and atmospheric conditions.

With noisy detectors, noisy electronics, or low atmospheric transmittance, the sensor can be sensitivity limited. If the target is small, the system can be resolution limited. Examples of both situations are provided

This course will enable to you to

  • Use the correct MTFs for image chain analysis
  • Recognize the limitations of back-of-the-envelope approximations such as resolution and sensitivity
  • Identify the subsystem (e.g., motion, optics, detector, electronics, and display) that limits performance
  • Understand the limitations of range performance predictions
  • Appreciate the importance of trade studies
  • Appreciate the value of graphs rather than a table of numbers
  • Be conversant with the myriad of technical terms
  • Become a smart buyer, analyst, and/or user of imaging systems

This course is intended for researchers, engineers, system designers, managers, and buyers who want to understand the wealth of information available from imaging system end-to-end analysis. It is helpful if the students are familiar with linear system theory (MTF analysis).
Course level: Intermediate
Course length: full day

Gerald Holst is an independent consultant for imaging system analysis and testing. He was a technical liaison to NATO, research scientist for DoD, and a member of the Lockheed-Martin senior technical staff. Dr. Holst has chaired the SPIE conference Infrared Imaging Systems: Design, Analysis, Modeling and Testing since 1989. He is author of over 30 journal articles and 6 books (published by SPIE and/or JCD Publishing). Dr. Holst is a member of OSA and is a SPIE Fellow.

Electro-Optical Imaging System Performance, fifth edition (SPIE Press and JCD Publishing, 2008), by Gerald C. Holst.

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