Track-before-detect for sea clutter rejection: tests with real data

This work discusses methods and experimental results on passive radar detection of moving ships using navigation satellites as transmitters of opportunity. The reported study highlights as the adoption of proper strategies combining target motion compensation and track-before-detect methods to achieve long time integration can be fruitfully exploited in GNSS-based passive radar for the detection of maritime targets. The proposed detection strategy reduces the sensitivity of long-time integration methods to the adopted motion models and can save the computational complexity, making it appealing for real-time implementations. Experimental results obtained in three different scenarios (port operations, navigation in open area, and river shipping) comprising maritime targets belonging to different classes show as this combined approach can be employed with success in several operative scenarios of practical interest for this technology.

Section: Introductionmentioning

confidence: 99%

Experimental Demonstration of Ship Target Detection in GNSS-Based Passive Radar Combining Target Motion Compensation and Track-before-Detect Strategies

Santi

Pastina

Bucciarelli³

2020

“…This paper is mainly concerned with K-distribution, which is widely used in high-resolution radar detection systems. K-distribution [ 16 , 17 ] was derived from a paper by Eric Jakeman and Peter Pusey (1978) who used it to model microwave sea echo. It has been found to be a suitable model for heavy-tailed background in radar systems [ 18 ], since it provides an excellent agreement between theoretical and experimental data.…”

Section: Introductionmentioning

confidence: 99%

Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect

Gao

Wang

2018

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.

“…Nevertheless, when the detection performance is unsatisfactory, the tracking accuracy will decline. In this case, the track-before-detect (TBD) strategy [ 12 , 13 , 14 , 15 , 16 ] is proposed. It gets rid of the detection in any single frame, but accumulates the likelihood ratio of continuous multiple frames.…”

Section: Introductionmentioning

confidence: 99%

δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

Liu

Sun

Peng

et al. 2018

The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment.