S.m.reza Soroushmehr, Shadrokh Samavi, Shahram Shirani,
Volume 1, Issue 2 (4-2005)
Abstract
In this paper a new method for determining the search area for motion estimation
algorithm based on block matching is suggested. In the proposed method the search area is
adaptively found for each block of a frame. This search area is similar to that of the full
search (FS) algorithm but smaller for most blocks of a frame. Therefore, the proposed
algorithm is analogous to FS in terms of regularity but has much less computational
complexity. To find the search area, the temporal and spatial correlations among the
motion vectors of blocks are used. Based on this, the matched block is chosen from a
rectangular area that the prediction vectors set out. Simulation results indicate that the
speed of the proposed algorithm is at least 7 times better than the FS algorithm.
Nerjes Rahemi, Kurosh Zarrinnegar, Mohammad Reza Mosavi,
Volume 21, Issue 3 (8-2025)
Abstract
In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy.
