Donald A. Reago

Third generation imaging sensor system concepts

Second generation forward looking infrared sensors, based on either parallel scanning, long wave (8 - 12 um) time delay and integration HgCdTe detectors or mid wave (3 - 5 um), medium format staring (640 X 480 pixels) InSb detectors, are being fielded. The science and technology community is now turning its attention toward the definition of a future third generation of FLIR sensors, based on emerging research and development efforts. Modeled third generation sensor performance demonstrates a significant improvement in performance over second generation, resulting in enhanced lethality and survivability on the future battlefield. In this paper we present the current thinking on what third generation sensors systems will be and the resulting requirements for third generation focal plane array detectors. Three classes of sensors have been identified. The high performance sensor will contain a megapixel or larger array with at least two colors. Higher operating temperatures will also be the goal here so that power and weight can be reduced. A high performance uncooled sensor is also envisioned that will perform somewhere between first and second generation cooled detectors, but at significantly lower cost, weight, and power. The final third generation sensor is a very low cost micro sensor. This sensor can open up a whole new IR market because of its small size, weight, and cost. Future unattended throwaway sensors, micro UAVs, and helmet mounted IR cameras will be the result of this new class.

Thermal Imager Topics

This chapter provides details on calculating signal and noise in thermal imagers. A detectivity model is used to calculate thermal imager contrast threshold function (CTF sys ). CTF sys is used in the targeting task performance (TTP) metric to calculate imager resolution. Thermal imagers sense heat energy with wavelengths between 3 and 12 μm. The 3- to 5-μm band is called midwave infrared (MWIR), and the 8- to 12-μm band is called longwave infrared (LWIR). Figure 9.1 shows typical atmospheric transmission for a 1-km horizontal path. There are three transmission windows from 3 to 4.2 μm, 4.4 to 5 μm, and 8 to 13 μm. Everything near room temperature radiates in the infrared. The emissivity of natural objects is generally above 70%. Most manmade objects are also highly emissive. Thermal sensors derive their images from small variations in temperature and emissivity within the scene. Typically, the thermal scene is very low contrast. Figure 9.2 shows the spectral radiant exitance from blackbodies at 300 K and 303 K. The difference between the two curves is also shown. The difference is small; however, a 3-K contrast represents good thermal imaging conditions.

Quantifying the Effect of Aliasing on Visual Task Performance

Aliasing degrades imagery and affects visual task performance. This chapter describes a model that predicts the effect of sampling on target identification. Aliasing is treated as noise. The combined effect of aliasing and detector noise degrades the system contrast threshold function (CTF sys ). The degraded (elevated) CTF sys lowers the TTP resolution. The effect of aliasing on PID is predicted by the TTP metric. Details on the thermal and reflective models are described in Chapters 9 and 10, respectively. Those chapters provide details on calculating CTFsys for different types of imagers. This chapter explains modeling concepts. Aliasing acts like noise because of the imaging task. We are interested in quantifying expected or average performance over many object identification attempts. At all ranges, the imager is presented with a diverse target set. The objects are placed randomly in the field of view. At each range, the task is repeated many times. The size and placement of spatial features varies from target to target. There are many targets in many different scenes. Aliasing acts like noise because of the combination of multiple targets, target diversity, random target placement, and the random sample phase of various target details. The aliased signal is different from detector noise in two ways. First, aliasing disappears as the target contrast disappears. The amplitude of aliasing depends on target contrast. Second, the image corruption due to aliasing gets worse with increased range. This is because sampling is constant in angle space, and targets become poorly sampled as range increases. Total noise is the quadrature sum of detector noise and aliasing. In order to sum the two noises, they must be properly scaled. Section 8.1 explains how signal and detector noise are spatially normalized in the thermal and reflective models. Section 8.2 describes the different temporal treatments of noise in detectivity versus photon-counting models. As far as the eye is concerned, noise is noise. However, the disparate treatment of noise in the two types of models results in different calibration constants.