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Kalman filters have been used as a robust method for object location prediction in various tracking algorithms for nearly a decade. More recently, adaptive and extended Kalman filters have been employed, making predictions even more reliable. The presented addition to this trend is the employment of a polynomial fit to the history of object locations, using the adaptive Kalman filter framework. This allows the linear state model of the adaptive Kalman filter to predict non-linear motion, making tracking more robust. This modified filter will be used in conjunction with the Mean Shift algorithm as the measurement step. Another important consideration when using a Kalman filter in this manner will be which correlation coefficient is used. The Pearson product-moment correlation coefficient is shown to provide more robust tracking when compared to the Bhattacharyya coefficient when objects have either low resolution or are unresolved.

Using band-limited LASER speckle to measure the Modulation Transfer Function (MTF) of an image sensor offers simplified procedure and inexpensive laboratory set up compared with the traditional method of using a knife edge on the sensor imaging plane. This speckle technique has been previously demonstrated by Glen Boreman's group on devices in the visible range. We have extended the procedure to short-wave infrared (IR) sensor at 1.55 micron. Similar measurements were also made at 532 nanometer on a commercial visible (VIS) sensor. The experiments show that the LASER speckle method to be accurate when compared to knife-edge measurements for data below Nyquist. The measured MTF data support optical system design and image quality modeling for both VIS and IR sensing applications.

The development and testing of thermal signature tracking algorithms burdens the developer with a method o f testing the algorith m's fidelity. Collected video is normally used for testing tracking algorithms to evaluate performance in a variety of configurations. Acquiring suitable volumes of collected video data in multiple configurations can be prohibitive. As an alternative to collected video, the development of accurate synthetic thermal infrared vehicle models are incorporated into background infrared scenes generated using the Digital Image and Remote Sensing Image Generat ion (DIRSIG) software package. Additional software models for thermally emissive targets and motion are being implemented. The goals are to accurately incorporate thermal signatures of moving targets into realistic radiomet rically calibrated scenes. This aids in evaluating tracking algorithms using both visible and thermal infrared signatures for improved day and night detection capability. The software packages are integrated together to produce synthetic video.

We describe a compact, multi-sensor design architecture capable of providing both spectral-polarimetric imaging and adaptive matched filter target detection in real-time. The sensor suite supports airborne broad-area search missions using multiple large-format, high speed TDI scanning sensors. The technology approach leverages Micro-Electro-Mechanical System (MEMS) based spectral imaging systems and scanning TDI arrays originally developed for space based remote sensing. The MEMS spectrometer system can dynamically select and switch linear combinations of single or multiple VNIR/SWIR spectral bands with 5nm sampling resolution using a programmable MEMS mirror. The MEMS spectral filter is capable of providing high quality spectral filtering across a large format sensor with > 1MHz optical switching & update speeds. A dual instrument sensor suite architecture called the "PRISM sensor" has been developed which is based on this technology and provides simultaneous spectral-polarimetric imaging and matched filter target tracking with minimal on-board computing requirements. We describe how this technology can simultaneously perform broad-area imaging and target identification in near real-time with a simple threshold operation. Preliminary results are illustrated as additional layer of target-discriminate geospatial information that may be fused with geo-referenced imagery.