Treffer: Improving efficiency in tracking with Rader Data using Kalman Filter algorithm compared.

Improving the accuracy in Target tracking with Rader Data is the major goal of this study. The study made use of the kaggle dataset as the primary source of data. Two distinct groups, Group I and Group 2, each comprising 20 samples, were utilised in this study. Group I employed the Kalman Filter, while Group 2 utilised the Particle Filter. The total sample size for the study was 40. Sample size calculations for statistical analysis, as well as the subsequent performance comparison were conducted and implementation was done using Python. The statistical analysis was carried out using clincalc.com with a statistical power (G-power) set at 85%, alpha (a) at 0.05, beta (13) at 0.2. The analysis primarily focused on comparing the performance of the Kalman Filter and Algorithm using accuracy value as the key evaluation metric. In terms of accuracy, Kalman Filter (97.8%) outperforms Particle Filter (93%), with a two-tailed, p>0.05 significance value of <.001. In summary, the accuracy of Kalman Filter outperforms Particle Filter accuracy. [ABSTRACT FROM AUTHOR]