Adaptive three-dimensional spatio-temporal filtering techniques for infrared clutter suppression

Abstract

The detection of weak targets with an infrared surveillance system is often complicated not only by a severe clutter environment but also by background and platform motion effects. Conventional sequentially applied algorithms combining frame—to—frame registration, clutter rejection filtering, and adaptive thresholding detection simply overwhelm the track processor in a weak target scenario, due to the required lowering of detector block thresholds. To address this problem, we have developed a 3—D filter/track—before—detect signal processing approach in which an adaptive spatio—temporal filter is used for clutter suppression and a Viterbi track—before—detect block is used for noncoherent target integration. This paper discusses a 3—D adaptive filtering technique which combines time and spatial filtering (in both azimuth and elevation directions) to achieve simultaneous frame—to-frame registration, background clutter suppression, and target preservation/enhancement. In addition, this 3-D filtering procedure whitens the data, thus greatly facilitating the track-before—detect processing block task. Unlike other commonly employed procedures, this technique neither entails the suboptimal sequential application of filtering procedures (e.g., spatial followed by temporal filtering) nor demands very accurate subpixel-level registration or exact knowledge of the target's velocity characteristics. The only requirements are that data frames should be roughly aligned (so the offsets are contained within the filter window) and that the assumption of the moving target indicator (MTI) is valid. In this paper simulation results of the 3—D filtering procedure using real, scanned sensor array data are presented, and the procedure performance and implementation complexity are traded off versus adaptive spatial filtering, adaptive temporal, and sequentially applied time/spatial filtering techniques. Also, modification and simulation results are presented for an extension of the 3—D adaptive spatiotemporal filtering technique, which accommodates both MTI and non-MTI case scenarios.