Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

Joshi, Sushil Kumar; Baumgartner, Stefan V.; Silva, André Barros Cardoso da; Krieger, Gerhard

doi:10.3390/rs11111270

Cited by 42 publications

(31 citation statements)

References 41 publications

(76 reference statements)

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“…The methodology proposed in [16] is used to detect ships in the range-Doppler domain. In that paper, the authors have proposed a constant false alarm rate (CFAR)-based ship detection algorithm, which is adapted for the RC airborne data.…”

Section: Detection and Clusteringmentioning

confidence: 99%

“…5). In the last stage, i.e., the update stage, the predicted state and the covariance matrix from ( 17) and ( 18) are updated based on the received noisy measurements [see (16)]. The updated target stateẐ (k + 1 | k + 1) and the associated covariance matrix P(k + 1|k + 1) then becomê 20)…”

Section: Kalman Filtermentioning

confidence: 99%

“…We use range-compressed (RC) radar data (single-channel or multichannel data) for airborne-based maritime This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ surveillance [16]. With the airborne or future HAP or HAPS radars, the signal-to-noise ratio (SNR) in many cases is sufficiently large.…”

Section: Introductionmentioning

confidence: 99%

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

Joshi

Baumgartner

Krieger

2022

IEEE Trans. Geosci. Remote Sensing

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Ship tracking facilitates a comprehensive insight into maritime traffic situations and ensures its safety and security. However, with the current operational surveillance systems, detecting several maritime threats is still a major challenge. In this article, we propose a supportive ship tracking concept using an airborne-based radar sensor. The proposed tracking algorithm is suitable for dense multitarget scenarios. Tracking is performed in the range-Doppler domain. The primary advantage of using the range-Doppler domain is that ships even with low radar cross section moving with certain line-of-sight velocity appear out of the clutter region, thus improving their detectability. In addition, a powerful track management system is also developed to handle false targets. The simulated and real experimental results from the DLR's airborne radar sensors, F-SAR and DBFSAR, are presented to prove the concept.

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Section: Detection and Clusteringmentioning

confidence: 99%

Section: Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Joshi

Baumgartner

Krieger

2022

IEEE Trans. Geosci. Remote Sensing

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“…Although cooperative targets, such as large ships, are now equipped with an automatic identification system (AIS), which reduces the collision risk of cooperative ships, there is no guarantee that small ships and non-cooperative targets are equipped with AIS systems. Therefore, improving the detection capabilities of sea-surface surveillance radars and navigational radars on sea targets, especially low observable targets, is the key to ensuring the safe navigation of ships [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

Zhang

Song

2021

Remote Sensing

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Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

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“…In ref. [6] a fast and effective approach is developed to detect ship in range-compressed radar data acquired by an airborne radar sensor during linear, circular, and arbitrary flight tracks. The approach is developed to work in a real-time-oriented way by exploiting a constant false alarm rate (CFAR) detection threshold that is computed in the range-Doppler domain together with an automatic ocean training data extraction procedure.…”

mentioning

confidence: 99%

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

2019

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Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts. Measurements collected by active and passive remote sensing instruments can be used for both marine and maritime applications. They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditional methods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gas rigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a very hot-topic in the field of global monitoring of environment and security.Among the remote sensing tools, Synthetic Aperture Radar (SAR) is one of the most used since it allows all-day and almost all-weather observations with a moderate-to-fine spatial resolution. SAR imagery gives the possibility to overcome the limits of maritime patrol allowing non-cooperative all-day target surveillance, over wide regions and under almost all-weather conditions. An increasing number of SAR satellites have become available since the early 1990s. This unprecedented development in SAR sensors requires the definition of new techniques and algorithms to detect marine targets, as well as in the assessment of existing methods. Hence, although there is a great deal of literature that concerns SAR methods for detecting targets at sea, there is still room to improve both models and methods.Within this context, methods and models are proposed in this Special Issue (SI) to exploit single-and multi-polarization SAR measurements for observation of targets at sea, with special interest for vessels.In ref.[1] the complex coherence between the two channels of the dual-polarized Sentinel-1 SAR imagery is exploited to detect ships in a timely manner using the entire image. The proposed rationale is verified against real SAR imagery that includes a large number of vessels of different sizes. In addition, Automatic Identification System (AIS) data are used for an extensive and large-scale cross-comparison with the SAR-based detections. Experimental results clearly point out the remarkable detection rate of the SAR-based method and its complementarity with respect to AIS information.In ref.[2] fishing vessels detection is addressed with reference to the problem of global overfishing. A method is here developed to deal with the discrimination between fishing and non-fishing vessels and a showcase is presented. The method, which is based on the Random Forest (RF) classifier, takes as input the vessel's length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the AIS and Sentinel-1 SAR imagery that refers to the North Sea.In ref.[3] a system to monitor illegal fishing activities is proposed. The approach, which is developed in the framework of a cooperation between the Plymouth Marine Laboratory (PML) and

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Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

Cited by 42 publications

References 41 publications

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

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

Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

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