Proc. SPIE. 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications
KEYWORDS: Target detection, Signal to noise ratio, Sensors, Fourier transforms, Interference (communication), Linear filtering, Clouds, Time-frequency analysis, Submerged target detection, Digital breast tomosynthesis
The signal-to-clutter ratio couldn't be improved effectively for conventional spatial or temporal high-pass filter is
difficult to remove clutter, especially clutter edge, and a dim moving target detection method based on the timefrequency
characters difference among target, noise and clutter is presented in this paper. Theoretical analysis shows that
the waveform at the target location in time-frequency domain is a small wave packet, the magnitude of the wave packet
is consistent with the target amplitude, and the width of the packet is inversely proportional to target speed; the
waveform at clutter edge is an "uphill" or "downhill". This target detection method takes two-stage filters to detect dim
target. At first, a threshold based on false alarm ratio criteria in the time-frequency is adopted to remove noise, and then
the ratio between "main lobe" and "side lobe" in the wave packet is counted to remove clutter and detect target. The dim
target detection experiment under cloud is included and the result shows that the method is effective.
While tracking dim and small moving targets in the electro-optical (EO) tracking system, the numerous false alarms resulted from the low signal-to-noise ratio would seriously debase the performance of target recognition and tracking. The probabilistic data association filter in conjunction with a maximum likelihood approach (PDAF-ML) has been applied effectively to low observable or dim target motion analysis. Whereas, the PDAF-ML supposes that the amplitude of target is not correlative among different sampling instants, and that the greater the amplitude value is, the greater the probability of being the target of interest would be. In the EO imaging tracking system, the amplitude information and the motion of target are consistent and highly correlative in a short period. To resolve the problem that the PDAF-ML is inconsistent with the EO imaging tracking system, the two features, namely, the amplitude information and the motion as well as their consistency, are modeled as Markov stationary random signals and are fused by means of PDAF. Experiments are carried out, and the results show that, with the proposed approach, the uncertainty of trajectory association would be largely decreased, and the performance of target recognition and tracking could be significantly improved.