Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect
This paper considers the detection of fluctuating targets in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP–TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter rece...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/18/7/2241 |
| _version_ | 1818014475258691584 |
|---|---|
| author | Jie Gao Jinsong Du Wei Wang |
| author_facet | Jie Gao Jinsong Du Wei Wang |
| author_sort | Jie Gao |
| collection | DOAJ |
| description | This paper considers the detection of fluctuating targets in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP–TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angles. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to a DP–TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 targets are derived first. However, the closed-form of the merit function is difficult to obtain. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating targets in heavy-tailed clutter. |
| first_indexed | 2024-04-14T06:45:44Z |
| format | Article |
| id | doaj.art-7ac022a150584cea8f2a8e8ef213f4f3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:45:44Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ac022a150584cea8f2a8e8ef213f4f32022-12-22T02:07:11ZengMDPI AGSensors1424-82202018-07-01187224110.3390/s18072241s18072241Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-DetectJie Gao0Jinsong Du1Wei Wang2Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaThis paper considers the detection of fluctuating targets in heavy-tailed clutter through the use of dynamic programming based on track-before-detect (DP–TBD) in radar systems. The clutter is modeled in terms of K-distribution, which can be widely used to describe non-Gaussian clutter received from high-resolution radars and radars working at small grazing angles. Swerling type 1 is considered to describe the target fluctuation between scans. Conventional TBD techniques suffer from significant performance loss in heavy-tailed environments due to the more frequent occurrences of target-like outliers. In this paper, we resort to a DP–TBD algorithm based on prior information, which can enhance the detection performance by using the environment and target fluctuating information during the integration process of TBD. Under non-Gaussian background, the expressions of the likelihood ratio merit function for Swerling type 1 targets are derived first. However, the closed-form of the merit function is difficult to obtain. In order to reduce the complexity of evaluating the merit function and the computational load, an efficient approximation method as well as a two-stage detection approach is proposed and used in the integration process. Finally, several numerical simulations of the new strategy and the comparisons are presented to verify that the proposed algorithm can improve the detection performance, especially for fluctuating targets in heavy-tailed clutter.http://www.mdpi.com/1424-8220/18/7/2241target detectionradar systemsK-distributed clutterheavy-tailedSwerling targettrack-before-detect (TBD) |
| spellingShingle | Jie Gao Jinsong Du Wei Wang Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect Sensors target detection radar systems K-distributed clutter heavy-tailed Swerling target track-before-detect (TBD) |
| title | Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect |
| title_full | Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect |
| title_fullStr | Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect |
| title_full_unstemmed | Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect |
| title_short | Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect |
| title_sort | radar detection of fluctuating targets under heavy tailed clutter using track before detect |
| topic | target detection radar systems K-distributed clutter heavy-tailed Swerling target track-before-detect (TBD) |
| url | http://www.mdpi.com/1424-8220/18/7/2241 |
| work_keys_str_mv | AT jiegao radardetectionoffluctuatingtargetsunderheavytailedclutterusingtrackbeforedetect AT jinsongdu radardetectionoffluctuatingtargetsunderheavytailedclutterusingtrackbeforedetect AT weiwang radardetectionoffluctuatingtargetsunderheavytailedclutterusingtrackbeforedetect |