Quasiprobable estimation of arrival instant of finite signal

Estimating the Position of an Image with Unknown Intensity Shape

“…In view of the above, one can conclude that the decision statistics in Equation 15of the QL estimation algorithm in Equation 9is the Gaussian random process, for which the mathematical expectation and the correlation function, under conditions of high a posteriori accuracy, allow the representations in Equations (22) and (31), respectively. The statistical properties of such a process are studied in details in [10,15]. Taking into account Equation (22), relations in Equation 14conditioning the consistency of the QL estimate in Equation 9, can be rewritten in a more convenient form:…”

“…Now, the minimum and maximum values of the coordinate on the axis Oy on the border of the area , 0X^h are denoted as points A and B, respectively, while the parametric descriptions of the left and noise components, respectively. Similarly to[15], let it be supposed that in theneighborhood of the point 0 m the signal function satisfies the conditions do not vanish at the boundary of the area , S m h X^h. Then, position of the maximum of the signal function , S 0 m m h by the variable λ coincides with the real value 0 m of the image position and the QL estimate in Equation (9) is consistent.Let of the noise component in Equation(13).…”