Effectiveness of Knowledge-Based STAP in Ground Targets Detection with Real Dataset

Gelli, S.; Bacci, Alessio; Giusti, Elisa; Berizzi, F.

doi:10.1049/cp.2017.0389

Cited by 5 publications

(9 citation statements)

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“…The objective is to remove undesired range bins of the training data that contain moving target signals and strong scatterers, so that cleaner training data can be obtained. The principle of the MTSR module is similar to the GIP test presented in [8], including a comparable increase of the processing time. Fig.…”

Section: E Moving Target Signal Rejectionmentioning

confidence: 99%

“…20 shows the GMTI results, where the moving cars are depicted as colored triangles pointing to their heading angles and the highway A7 is shown in white. The colors are related to the absolute velocities of the cars on ground |v abs |, estimated according to (8). The information box in Fig.…”

Section: F Real Traffic Scenariomentioning

confidence: 99%

“…More recently, a KA algorithm showed that the generalized inner product (GIP) test can be applied in the space-Doppler domain in order to remove nonhomogeneous training data from the CCM estimation [8]. This approach requires the terrain database for obtaining the statistical properties of the clutter for each region of interest.…”

mentioning

confidence: 99%

“…However, especially when real-time processing is desired, the use of KA algorithms (e.g., those presented in [6]- [8]) would further increase the complexity of STAP.…”

mentioning

confidence: 99%

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Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Silva

Baumgartner

Krieger

2019

IEEE Trans. Geosci. Remote Sensing

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Space-time adaptive processing (STAP) of multichannel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are: 1) the appropriate and automatic selection of the training data and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate threshold, and target cancelation by self-whitening. In this paper, two conventional and two novel methods for training data selection are evaluated and compared using real four-channel X-band radar data acquired with DLR's airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map.

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Section: E Moving Target Signal Rejectionmentioning

confidence: 99%

Section: F Real Traffic Scenariomentioning

confidence: 99%

mentioning

confidence: 99%

“…However, especially when real-time processing is desired, the use of KA algorithms (e.g., those presented in [6]- [8]) would further increase the complexity of STAP.…”

mentioning

confidence: 99%

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Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Silva

Baumgartner

Krieger

2019

IEEE Trans. Geosci. Remote Sensing

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“…State-of-the-art GMTI algorithms are available in the literature using a priori knowledge information (Melvin and Showman, 2006;Bang et al, 2015;Gelli et al, 2017), whereas they often require high hardware complexity and high computational effort. Thus, especially when real-time processing is desired, the use of such methodologies would further increase the complexity of STAP algorithms, which are already very demanding.…”

Section: Introductionmentioning

confidence: 99%

Fast post-Doppler STAP with road map for traffic monitoring: overview and first results

2019

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Abstract. Synthetic aperture radar (SAR) is an efficient solution for road traffic monitoring due to its high spatial resolution and independence from daylight and weather conditions. In this sense, a number of ground moving target indication (GMTI) algorithms have been developed, whereas their robustness is often achieved with high costs, increased hardware complexity and high computational burden. This paper presents a fast GMTI processor that blends the powerful post-Doppler space-time adaptive processing (PD STAP) with an a priori known road map and digital elevation model (DEM). The algorithm presents great potential for real-time processing, decreased hardware complexity and low costs compared to state-of-the-art systems. It is tested using real 4-channel X-band radar data acquired with the DLR's airborne F-SAR.

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