|
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.
|
