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Speckle noise exists inherently in the synthetic aperture radar (SAR) image. Its multiplicative property leads to lots of difficulties in SAR image processing. A novel guidance-aided triple-adaptive Frost filter is proposed in this paper, which has potential for real-time processing platforms. Firstly, a scale-adaptive sliding window sizing method is adopted to determine the neighborhood ranges for every point in the image. All the subsequent processing is based on it. Then, an adaptive calculation for the tuning factor in the Frost filter is embedded into the proposed method. Lastly, the feature information apertured from the original image is used to provide guidance for edge recovery automatically, which guarantees the satisfactory ability for feature preservation. Thus, a novel improved Frost filter is proposed with triple adaptabilities. Both the positioning accuracy and response sensitivity of the scale-adaptive sliding window sizing method are verified first. The superiority of the adaptive tuning factor combined with the scale-adaptive sliding window is confirmed by two comparison experiments. At last, the results of speckle suppression experiments on the synthetic images and two natural airborne SAR images present a better performance than other methods.

Numerical investigations were conducted to determine the effectiveness of the front window visor for wind buffeting noise reduction. An unsteady flow simulation was carried out using a zonal Scale Adaptive Simulation (SAS) k-ε turbulence model. Firstly, the accuracy of the simulation method was validated based on a benchmark problem. The benchmark results, frequency, and sound pressure levels of feedback and resonance modes all matched well with the experimental data. The effect of the front window on the buffeting noise reduction was numerically investigated based on three different front side window openings. The analysis focused on the suppression effect of the front window visor. The results show that the front window visor changed the A-pillar vortex shedding trajectory and thus reduced the driver’s ear pressure fluctuation. On this basis, an optimization algorithm was employed to optimize the shape of the front window visor. The main design goal was to decrease the sound pressure level (SPL) values of the driver’s left ear. Simulation results showed that the monitoring point’s SPL of buffeting noise after the visor optimization was reduced by 12.6%, compared with that of the original visor.