Theoretical Form and Numerical Calculation of Detection Threshold for DP-TBD in Processing Plot-List Data

Compared with the constant false alarm ratio (CFAR) detector, the dynamic programming algorithm for track-before-detect (DP-TBD) can utilize target motion information in large-scale interframe time to achieve better performance advantages in detecting targets with low signal-to-clutter ratio. To reduce computational complexity and minimize clutter interference, the DP-TBD algorithm directly processes the plot-list data, which is obtained by performing threshold preprocessing (or the CFAR detector) on the raw radar data. However, it is difficult to provide theoretical guidance on the detection threshold of the algorithm. Therefore, for the DP-TBD algorithm in processing the plot-list data, we theoretically derive and propose a numerical method to determine the detection threshold. First, we theoretically derive the probability density function of the clutter merit function, thereby providing a formula for calculating the probability of false alarm (PFA) of DP-TBD. Second, to mitigate the model error caused by the independent and identical distribution assumption, we propose a modified model with a coefficient to be determined from real data. Third, based on the theoretical formula, we propose a numerical calculation method implemented by fast Fourier transform to calculate the theoretical detection threshold. The experimental results demonstrate that the proposed method can effectively and accurately describe the PFA of DP-TBD for different simulation conditions in a uniform clutter environment.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Aerospace and Electronic Systems
Published
2024-09-20
DOI
https://doi.org/10.1109/taes.2024.3463638
Citations
2
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.53

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Theoretical Form and Numerical Calculation of Detection Threshold for DP-TBD in Processing Plot-List Data

Heng Huang, Yunrong Zhu, Qiming Zhang, Yibo Zhang et al.
2 citations
IEEE Transactions on Aerospace and Electronic Systems
Traffic Prediction and Management Techniques
0.53
article

Theoretical Form and Numerical Calculation of Detection Threshold for DP-TBD in Processing Plot-List Data

Heng Huang, Yunrong Zhu, Qiming Zhang, Yibo Zhang, Yang Li
article en
2 citations

Abstract

Compared with the constant false alarm ratio (CFAR) detector, the dynamic programming algorithm for track-before-detect (DP-TBD) can utilize target motion information in large-scale interframe time to achieve better performance advantages in detecting targets with low signal-to-clutter ratio. To reduce computational complexity and minimize clutter interference, the DP-TBD algorithm directly processes the plot-list data, which is obtained by performing threshold preprocessing (or the CFAR detector) on the raw radar data. However, it is difficult to provide theoretical guidance on the detection threshold of the algorithm. Therefore, for the DP-TBD algorithm in processing the plot-list data, we theoretically derive and propose a numerical method to determine the detection threshold. First, we theoretically derive the probability density function of the clutter merit function, thereby providing a formula for calculating the probability of false alarm (PFA) of DP-TBD. Second, to mitigate the model error caused by the independent and identical distribution assumption, we propose a modified model with a coefficient to be determined from real data. Third, based on the theoretical formula, we propose a numerical calculation method implemented by fast Fourier transform to calculate the theoretical detection threshold. The experimental results demonstrate that the proposed method can effectively and accurately describe the PFA of DP-TBD for different simulation conditions in a uniform clutter environment.

IEEE Transactions on Aerospace and Electronic SystemsVol. 61(2)
Harbin Institute of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 34%
Traffic Prediction and Management Techniques
0.53
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.