Open Access

ARTICLE

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

by T. Jagadesh^1,2, B. Sheela Rani^3,*

1 Faculty of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India
2 Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
3 Sathyabama Institute of Science and Technology, Chennai, 600119, India

* Corresponding Author: B. Sheela Rani. Email:

Computer Systems Science and Engineering 2023, 44(3), 2569-2583. https://doi.org/10.32604/csse.2023.027239

Received 13 January 2022; Accepted 06 April 2022; Issue published 01 August 2022

Abstract

RSs (Radar Systems) identify and trace targets and are commonly employed in applications like air traffic control and remote sensing. They are necessary for monitoring precise target trajectories. Estimations of RSs are non-linear as the parameters TDEs (time delay Estimations) and Doppler shifts are computed on receipt of echoes where EKFs (Extended Kalman Filters) and UKFs (Unscented Kalman Filters) have not been examined for computations. RSs, certain times result in poor accuracies and SNRs (low signal to noise ratios) especially, while encountering complicated environments. This work proposes IUKFs (Iterated UKFs) to track online filter performances while using optimization techniques to enhance outcomes. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using MCEHOs (Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system non-linearity where OIUKFs (Optimized Iterative UKFs) predict a target's unknown parameters. To obtain optimal solutions theoretically, OIUKFs take less iteration, resulting in shorter execution times. The proposed OIUKFs provide numerical approximations which are derivative-free implementations. Simulation evaluation results with estimators show better performances in terms of reduced NMSEs (Normalized Mean Square Errors), RMSEs (Root Mean Squared Errors), SNRs, variances, and better accuracies than current approaches.

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Keywords

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