Szczegóły publikacji
Opis bibliograficzny
Hyperparameter free MEEF based adaptive estimator for MIMO radar / Uday Kumar Singh, Rangeet Mitra, Amit Kumar MISHRA, Vimal Bhatia, K. Venkateswaran, Rama Rao Thipparaju // W: RadarConf'25 [Dokument elektroniczny] : 2025 IEEE Radar Conference : October 4-9, 2025, Krakow, Poland : conference proceedings / eds. Marek Rupniewski, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2025. — ( Proceedings of the IEEE National Radar Conference ; ISSN 1097-5659 ). — Dod. ISBN: 979-8-3315-4432-4, 979-8-3315-4434-8. — e-ISBN: 979-8-3315-4433-1. — S. 740–745. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 745, Abstr. — A. K. Mishra - dod. afiliacja: University West, Sweden
Autorzy (6)
- Singh Uday Kumar
- Mitra Rangeet
- AGHMishra Amit Kumar
- Bhatia Vimal
- Venkateswaran K.
- Thipparaju Rama Rao
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164406 |
|---|---|
| Data dodania do BaDAP | 2025-12-06 |
| Tekst źródłowy | URL |
| DOI | 10.1109/RadarConf2559087.2025.11205019 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | Proceedings of the IEEE National Radar Conference |
Abstract
In the context of parameter estimation for nextgeneration nonlinear radar system models impaired by nonGaussian clutter, reproducing kernel Hilbert space (RKHS)-based signal processing algorithms and information-theoretic learning (ITL) have emerged as promising. Notably, the RKHS and ITLbased approaches are found to outperform conventional maximum likelihood (ML) based methods. However, these RKHS/ITLbased approaches, together with neural network/Bayesian-based approaches, are known to depend on hyperparameters for parameter/criterion estimation. In the context of next-generation radar, this paper introduces a minimum error entropy with fiducial points (MEEF)-based parameter estimation for multi-input-multiple-output (MIMO) radar to estimate target position and velocity amid non-Gaussian additive noise processes, such as clutter. Furthermore, we utilize a kernel width sampling method to make the proposed MEEF estimator hyperparameter-free. The proposed hyperparameter-free MEEF-based RKHS estimator with kernel width sampling (MEEF-KWS) is found to outperform minimum mean squared error (MMSE) and other ITL-based adaptive estimation techniques with fixed best kernel width. Computer simulations are presented assuming practical MIMO radar scenarios, which indicate that the proposed hyperparameter-free MEEF-KWS delivers improved estimation accuracy and/or lower computations compared to the existing RKHS-based MMSE and other common ITL criteria with fixed kernel width.
