The multiplicative noise model is often used to describe the speckle noise in the SAR image. This speckling process
defines the conditional probability of the SAR image intensity I from RCS ó. The MMSE algorithm of the SAR image
reconstruction is basis on the multiplicative noise model. However, the MMSE algorithm does not define how
calculating the look number of the SAR image. This paper advises an algorithm calculating the look number of the SAR
image. This algorithm is basis on the local statistical information. Therefore, the MMSE algorithm can adapt all kinds of
the SAR images. Furthermore, when the iteration MMSE is used, the MMSE algorithm can adapt the change of SAR
image statistical information.
Proc. SPIE. 7382, International Symposium on Photoelectronic Detection and Imaging 2009: Laser Sensing and Imaging
KEYWORDS: Target detection, Signal to noise ratio, Digital signal processing, Detection and tracking algorithms, LIDAR, Laser applications, Computer programming, Signal processing, Signal detection, Filtering (signal processing)
To solve the laser weak echo detection under the clutter background, a dynamic programming (DP) technique
has been developed for the detection of weak targets. The primary advantages of DP are its sensitivity to weak
targets along with its robustness to laser echo glitter. DP technology turn searching track of the target into the
subsection optimization of the target. First, DP algorithm finds all track subsection of target. Second, these track
subsections are synthesized into the potential track of target. Third, the track of the same target is combined. Then
the detection outcome is found. Theory analysis and the simulated results indicate a sensitivity improvement of
detection performance over conventional detection algorithm.
In this paper, we present a new method for the weak signal detection in the laser radar, which is based on that the
laser echo is characteristic with chaos. The method is naturally rooted in nonlinear dynamical systems and relies on
neural networks for its implementation. At first, this paper used the observed data to analyze the chaotic characteristics
of the laser radar backscatter by calculating the chaotic character parameters, including correlation dimension, Lyapunov
exponent and local predictability. Then the predictor model based on BP neural network is proposed. Experiment results
show that the BP neural network predictor is a better math model. It is valuable for signal detection in chaotic series.
Because the airdrome is a target on the ground, this kind of target has the complex characteristic. So we must
think about all kind of factor to design the algorithm which can meet the practice, when the algorithm of
proceeding infrared image is designed. This paper advises the track-before-detect (TBD) algorithm to process
infrared image by the practice and modern image procession technology. Our results indicate that this algorithm
can distinguish between the real target and the false target under small SNR. SNR is about 1.8.
If the luminance of the cloud is high, we can identify the cloud by the luminance threshold. If the variance of the cloud is high, we can identify the cloud by the frequency information. If the difference in brightness between the light and dark cloud of a picture is big, they are difficultly identified. However, this type of the cloud has an obvious edge. This paper presents a new algorithm on base of texture to identify the cloud.