Moving Target Tracking in SAR Data Using Combined Exo- and Endo-Clutter Processing

Henke, Daniel; Domínguez, Elías Méndez; Small, David; Schaepman, Michael E.; Meier, Erich

doi:10.1109/tgrs.2017.2746262

Cited by 24 publications

(4 citation statements)

References 19 publications

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“…According to the CMOS noise model, it is convenient to study the influence of noise in the CMOS image sensor acquisition system on the image matching and detection results [18]. Here simply compare the normalized inverse mean absolute difference sum (NISAD), normalized inverse mean variance sum (NISSD), normalized inverse maximum absolute difference (NIMD), normalized direct correlation (NDC), and normalized cross-correlation (NCC); these similarity measures change under the influence of noise.…”

Section: E Source Of Noise and The Noise Model Of Domain Visionmentioning

confidence: 99%

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Wang

Duan

2022

Security and Communication Networks

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Moving target tracking technology is an important topic in the field of time domain sensors, which is widely used in medical diagnosis, intelligent robots, human-computer interaction, education, and entertainment. This article is based on the time domain vision sensor to explore the methods of sports target tracking and speed measurement. The article introduces the regional gray-scale correlation matching method in the vision sensor algorithm, the noise model of the time domain vision sensor, and the method of eliminating noise. At the same time, the tracking effects of SIFT algorithm, Mean Shift algorithm, and MSO algorithm on moving targets are studied, analyzing the ANF algorithm, TSML algorithm, and IHTLS based on the time domain visual sensor to compare the TUD-Crossing test set, the ETHBAHNHOF test set, and the SMOT data set; then the image matching was purified and analyzed based on the time domain visual sensor. The research results show that, in the method of exploring moving target tracking based on time domain vision sensor, when the improved SIFT algorithm uses feature adaptive fusion to describe the target, it can automatically allocate the proportion of color features and texture features, which improves the accuracy and reliability of moving target tracking. Among them, the DNP algorithm can control the final accuracy rate above 80%, and its recall rate is basically greater than 80%. In the case that the accuracy of rough matching is less than 50%, the final matching accuracy after processing by multiple purification algorithms is above 98%.

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Section: E Source Of Noise and The Noise Model Of Domain Visionmentioning

confidence: 99%

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Wang

Duan

2022

Security and Communication Networks

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“…TheresultsproducedbytheproposedFBF-MTDmodeliscomparedwiththeconventionalmodels, suchasfractionalFouriertransform(FrFT) (Yu,et al,2017),matchedfilter (Yu,et al,2017),multi-framefractionalFouriertransform(Multi-frameFrFT) (Li,et al,2016b),combinedendo/exoclutter GMTI (Henke,et al,2018),andtheBF-MTD(KumarandKumar,2020.…”

Section: Comparative Methodsmentioning

confidence: 99%

“…U. College of Engineering, Andhra Pradesh, India themovingobjects.Ingeneral,SARwasdesignedforimagingonlythestaticobjects.However,with advancedmethodsofprocessing,informationonmovingobjectsareextractedwhensimultaneously imagingtheareaofinterest.Inaddition,SARdatacanbeobtainednearlyindependentofdaylight andweatherconditionsthatprovideauniquesensortechnology (Maqbali,2020),whichcanbeused inawiderangeoftrackingapplications (Henke,et al,2018).…”

Section: Moving Target Detection Using Fuzzy Bayesian Fusion Inmentioning

confidence: 99%

Moving Target Detection Using Fuzzy Bayesian Fusion in Multichannel SAR Framework

2022

International Journal of Distributed Systems and Technologies

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In this paper, a novel FBF-MTD is proposed for the detection of moving target by integrating the fuzzy concept in Bayesian fusion model. This method uses the decision fusion method that combines the matching filter, Fourier transform and the STFT. In the first step, acceleration, velocity, and RCS are simulated and the radar that returns from the target is calculated based on transmission power, distance of target, antenna gain, and RCS. Then, the FBF-MTD method combines the results of Fourier transform, short time Fourier transform, and matched filter, to produce the final decision. The performance of the proposed FBF-MTD method is analyzed with respect to the metrics, namely detection time, missed target rate, and MSE. The proposed FBF-MTD model obtained the detection time, missed target rate, and MSE values of 3.2495 sec, 0.0524, and 3344.04, respectively that show the superiority of the FBF-MTD model in MTD.

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“…Advances in multifunction radar systems have led to airborne radar systems exhibiting superior capabilities for various modes such as the target track mode, auto‐acquisition mode, and air‐to‐ground mode [1]. Among various modes, the air‐to‐ground mode is important since it supports various ground missions, including ground moving target indication/tracking [2, 3], Doppler beam sharpening [4, 5], and air‐to‐ground ranging (AGR). In engagement environments, AGR is especially crucial for missile and bomb guidance.…”

Section: Introductionmentioning

confidence: 99%

Simulation of air‐to‐ground ranging mode of airborne monopulse radar over complex terrain

Rim

Koh

Song

2020

IET Radar, Sonar & Navigation

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Moving Target Tracking in SAR Data Using Combined Exo- and Endo-Clutter Processing

Cited by 24 publications

References 19 publications

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Sports Target Tracking and Speed Measurement Method Based on Time Domain Vision Sensor

Moving Target Detection Using Fuzzy Bayesian Fusion in Multichannel SAR Framework

Simulation of air‐to‐ground ranging mode of airborne monopulse radar over complex terrain

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