Turbo Parametric Spectral Estimation Method of Clutter Profile for Adaptive Radar Detection

Date

2020-06-01

Author

Eraslan, Berna
Güvensen, Gökhan Muzaffer
Tanık, Yalçın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

143
views

0
downloads

© 2020 IEEE.Identification of unwanted components of a received echo is critical to improving the radar detection performance. Previously proposed parametric parameter estimation methods, such as MUSIC, ESPRIT, and Burg were implemented to estimate moments of radar clutter. Since none of these methods could estimate the value of Doppler spread and have sufficient accuracy, the Stochastic Maximum Likelihood (SML) technique was implemented. Since its estimation accuracy was profoundly initial point dependent and computationally costly, a novel estimation method (Turbo SML) is proposed. The proposed method outperformed the strategies proposed in the literature with its high Doppler resolution, accuracy, and low computational complexity. Besides, Turbo SML performance was optimized by using Burg estimates for starting point choice. After accomplishing nearly optimal estimation, its estimates were utilized to implement an approximately Max-Normalized Signal to Interference plus Noise Ratio (SINR) filter. Superior to detection filters in literature, the proposed filter can maximize its output Signal to Interference plus Noise Ratio (SINR) with a few numbers of secondary data.

URI

https://hdl.handle.net/11511/69923

DOI

https://doi.org/10.1109/isie45063.2020.9152522

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

FISHER SELECTIVE SEARCH FOR OBJECT DETECTION BUZCU, ILKER; Alatan, Abdullah Aydın (2016-09-28) An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additiv...
Parametric spectral estimation methods of clutter profile for adaptive radar detection and classification Eraslan, Berna; Güvensen, Gökhan Muzaffer; Department of Electrical and Electronics Engineering (2019) Identiﬁcation of unwanted echoes in a received radar signal is crucial in order to improve the radar detection performance. In the scope of thesis, currently proposed parametric spectrum estimation techniques, such as MUSIC, ESPRIT and Burg, are evaluated in order to estimate moments of clutter components in received radar echo. Since none of these methods has the ability of estimating Doppler spread and adequate accuracy, Stochastic Maximum Likelihood (SML) method is implemented, working with the best perf...
ARMA model based clutter estimation and its effect on clutter supression algorithms Tanrıverdi, Güneş; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2012) Radar signal processing techniques aim to suppress clutter to enable target detection. Many clutter suppression techniques have been developed to improve the detection performance in literature. Among these methods, the most widely known is MTI plus coherent integrator, which gives sufficient radar performance in various scenarios. However, when the correlation coefficient of clutter is small or the spectral separation between the target and clutter is small, classical approaches to clutter suppression fall...
Semi-Automatic Annotation For Visual Object Tracking Köksal, Aybora; Alatan, Abdullah Aydın (2021-11-24) We propose a semi-automatic bounding box annotation method for visual object tracking by utilizing temporal information with a tracking-by-detection approach. For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently. We employ Multiple Hypothesis Tracking (MHT) to exploit temporal information and to reduce the number of false-positives which makes it possible to use l...
A tracker-aware detector threshold optimization formulation for tracking maneuvering targets in clutter Aslan, Murat Samil; Saranlı, Afşar (Elsevier BV, 2011-09-01) In this paper, we consider a tracker-aware radar detector threshold optimization formulation for tracking maneuvering targets in clutter. The formulation results in an online method with improved transient performance. In our earlier works, the problem was considered in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In the present study, we extend the ideas in the PDAF formulation to the multiple model (MM) filtering structures which use PDAFs as modules. Althou...

Citation Formats

B. Eraslan, G. M. Güvensen, and Y. Tanık, “Turbo Parametric Spectral Estimation Method of Clutter Profile for Adaptive Radar Detection,” 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/69923.