Tracking and Track Management of Extended Targets in Range-Doppler Using Range-Compressed Airborne Radar Data

Joshi, Sushil Kumar; Baumgartner, Stefan V.; Krieger, Gerhard

doi:10.1109/tgrs.2021.3084862

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…The estimation methods discussed in the paper cannot discriminate between the front and back of ships. For resolving this ambiguity additional information is required, e.g., the tracked ship positions over time [18] or the line-of-sight velocity obtained via along-track interferometry [19] or space-time adaptive processing [20]. For the performance assessment we have manually eliminated such 180 • ambiguities.…”

Section: Proposed Eigen-based Methodsmentioning

confidence: 99%

Efficient Size and Heading Angle Estimation of Ships in SAR and ISAR Images

Joshi,

Baumgartner,

Tings

2024

IEEE Geosci. Remote Sensing Lett.

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For maritime security applications it is advantageous or even required, that additionally to the geographical positions and moving directions the dimensions of the detected ships are available for subsequent classification and recognition purposes. In this paper a fast and robust method for size and heading angle estimation of ships in synthetic aperture radar (SAR) and inverse SAR (ISAR) images is proposed. The novel method leverages the eigenvalue decomposition of the detected ship pixel positions for determining the just mentioned parameters. The effectiveness of the proposed method is assessed against several state-of-the-art methods by using real X-band SAR images acquired with the German TerraSAR-X radar satellite in stripmap mode. The achieved accuracies of the proposed method are better than the ones obtained with the considered state-ofthe-art methods and, as a further benefit, the computation time is also significantly shorter.

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Section: Proposed Eigen-based Methodsmentioning

confidence: 99%

Efficient Size and Heading Angle Estimation of Ships in SAR and ISAR Images

Joshi,

Baumgartner,

Tings

2024

IEEE Geosci. Remote Sensing Lett.

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“…To overcome these shortcomings, some target detection methods in the non-image domain have been presented in recently years and one representative example is the rangecompressed domain (RCD) as it does not require timeconsuming azimuth focusing. In [7], a ship detector is proposed based on Faster R-CNN working in the RCD; in [8], a two-step detection method is presented with the first step using complex signal kurtosis in the RCD to screen possible ship areas coarsely, and the second step applying CNN to further detect the potential ship areas; an oriented ship detection strategy is designed in [9], which calculates the constant false alarm rate detection threshold in the range-Doppler domain; a supportive ship tracking concept is introduced in [10] in the range-Doppler domain using an airborne-based radar sensor; in addition, a method for ship detection from raw SAR echo data is proposed in [11]. However, most of them are based on two-dimensional data for detection, where the model size tends to be large and dependent on CPU resources, and detection based on onedimensional data often doesn't take advantage of deep learning that can extract deep features.…”

Section: Introductionmentioning

confidence: 99%

An Incept-TextCNN Model for Ship Target Detection in SAR Range-Compressed Domain

Zeng,

Song,

Yang

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Traditionally, SAR-based ship target detection is performed in the image domain, where SAR imaging processing has to be applied first. However, SAR imaging processing is complex and time-consuming, especially in the wide-swath working mode. Actually, for open sea scenes, most echoes are sea surface signals with no ship targets, and there is no need for imaging processing in those areas. Therefore, non-image domain ship target detection is studied in this paper, and a novel Incepttext convolutional neural network (TextCNN) model is proposed for ship target detection in the SAR range-compressed domain (RCD). In the proposed method, the SAR echo data is converted into a one-dimensional range profile signal firstly by range compression and mean pooling, and then, the Incept-TextCNN model is proposed and applied, and information about existence of ship targets in relevant range cells will be its output. Finally, the effectiveness and efficiency of the proposed method is testified by simulation and real spaceborne SAR data, and the results demonstrate that the proposed model can filter out the invalid range-compressed data of the sea surface area, which can significantly reduce the amount of data for subsequent SAR imaging and ship classification.

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“…Digital Object Identifier 10.1109/TGRS.2023.3271905 detected [13], [14], [15], [16], [17], [18], [19], [20], [21] and single look complex (SLC) products [5], [6], [7], [8] or at least the range-compressed products [22], [23], [24], [25], [26]. For example, constant false alarm rate (CFAR) [18], [19], [20], [21], [22], [23] is one of the most widely used amplitude-based algorithms that is used to search for unusually brighter pixels than the surrounding sea clutter.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection From Raw SAR Echo Data

Leng

Kuang

2023

IEEE Trans. Geosci. Remote Sensing

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In the context of ship monitoring in the ocean, targets are usually sparsely distributed. Thus, synthetic aperture radar (SAR) imaging of the whole scene is usually quite redundant and costly. However, raw SAR echo data were considered to be useless before focusing. Few studies have attempted to detect ships from raw SAR echo data. It seems to be an impossible task since the resolution of raw SAR echo data is too low. This article proposes a ship detection method for raw SAR echo data in view of a nonimaging target sensing paradigm. The core idea is that we can sense the existence of ships from raw SAR echo data without imaging. The underlying rationale is that the radar always speaks the same sentence, i.e., usually an exactly identical linear frequency modulated (LFM) signal, while target and clutter answer differently. The difference spread into each part of the whole echo sequence rather than only the focused energy after match filtering. Thus, the ships can be found by pattern analysis on one-dimension sequence data rather than twodimension images. The experimental results based on simulation and typical real data validate our assumption. This study shows that SAR imaging is an unnecessary intermediate process and opens up new significant possibilities for ship detection in the vast ocean.

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Tracking and Track Management of Extended Targets in Range-Doppler Using Range-Compressed Airborne Radar Data

Cited by 11 publications

References 31 publications

Efficient Size and Heading Angle Estimation of Ships in SAR and ISAR Images

Efficient Size and Heading Angle Estimation of Ships in SAR and ISAR Images

An Incept-TextCNN Model for Ship Target Detection in SAR Range-Compressed Domain

Ship Detection From Raw SAR Echo Data

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