Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
J Acoust Soc Am. 2011 Sep;130(3):1222-31. doi: 10.1121/1.3570949.
A maximum likelihood method for estimating remote surface orientation from multi-static acoustic, optical, radar, or laser images is presented. It is assumed that the images are corrupted by signal-dependent noise, known as speckle, arising from complex Gaussian field fluctuations, and that the surface properties are effectively Lambertian. Surface orientation estimates for a single sample are shown to have biases and errors that vary dramatically depending on illumination direction. This is due to the signal-dependent nature of speckle noise and the nonlinear relationship between surface orientation, illumination direction, and fluctuating radiance. The minimum number of independent samples necessary for maximum likelihood estimates to become asymptotically unbiased and to attain the lower bound on resolution of classical estimation theory are derived, as are practical design thresholds.
提出了一种从多静态声、光、雷达或激光图像中估计远程表面方向的最大似然方法。假设图像受到信号相关噪声（称为散斑）的干扰，这种噪声源于复杂高斯场的波动，并且表面特性是有效的朗伯。对于单个样本的表面方向估计，其偏差和误差会根据照明方向的变化而发生显著变化。这是由于散斑噪声的信号相关特性以及表面方向、照明方向和波动辐射率之间的非线性关系所致。推导出了最大似然估计渐近无偏和达到经典估计理论分辨率下限所需的最小独立样本数，并得出了实际设计阈值。
