Hughes Aircraft Company, Radar Systems Group, Los Angeles, CA 90009.
IEEE Trans Pattern Anal Mach Intell. 1986 Jan;8(1):90-9. doi: 10.1109/tpami.1986.4767755.
An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.
提出了一种基于一系列噪声图像的目标运动参数估计方法。所考虑的问题是刚体经历未知的旋转和平移运动。测量数据由两个或更多个目标对应点的噪声图像坐标序列组成。通过将对象动力学建模为时间的函数，可以使用递归和/或批处理技术从数据中提取模型参数（包括运动参数）的估计值。这允许通过使用任意数量的图像来实现所需的平滑度。目前对对象结构的某些假设。为递归估计过程呈现结果：这里考虑的情况是二维对象的一维图像序列。因此，物体在一个横向维度和深度上移动，同时保持中心投影图像模型的基本歧义（深度信息丢失）。迭代扩展卡尔曼滤波器用于递归求解。使用对象图像大小的 5-10%的噪声水平。作为对象轨迹和噪声水平的函数，推导出模型参数估计的近似克拉美-罗下限。在难以解析大量目标匹配点的情况下，这种方法可能很有用，但可以获得相对较长的图像序列（10 到 20 个或更多）。
