11 research outputs found
νλ ¨ μλ£ μλ μΆμΆ μκ³ λ¦¬μ¦κ³Ό κΈ°κ³ νμ΅μ ν΅ν SAR μμ κΈ°λ°μ μ λ° νμ§
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Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : μμ°κ³Όνλν μ§κ΅¬νκ²½κ³ΌνλΆ, 2021. 2. κΉλμ§.Detection and surveillance of vessels are regarded as a crucial application of SAR for their contribution to the preservation of marine resources and the assurance on maritime safety. Introduction of machine learning to vessel detection significantly enhanced the performance and efficiency of the detection, but a substantial majority of studies focused on modifying the object detector algorithm. As the fundamental enhancement of the detection performance would be nearly impossible without accurate training data of vessels, this study implemented AIS information containing real-time information of vesselβs movement in order to propose a robust algorithm which acquires the training data of vessels in an automated manner.
As AIS information was irregularly and discretely obtained, the exact target interpolation time for each vessel was precisely determined, followed by the implementation of Kalman filter, which mitigates the measurement error of AIS sensor. In addition, as the velocity of each vessel renders an imprint inside the SAR image named as Doppler frequency shift, it was calibrated by restoring the elliptic satellite orbit from the satellite state vector and estimating the distance between the satellite and the target vessel. From the calibrated position of the AIS sensor inside the corresponding SAR image, training data was directly obtained via internal allocation of the AIS sensor in each vessel. For fishing boats, separate information system named as VPASS was applied for the identical procedure of training data retrieval.
Training data of vessels obtained via the automated training data procurement algorithm was evaluated by a conventional object detector, for three detection evaluating parameters: precision, recall and F1 score. All three evaluation parameters from the proposed training data acquisition significantly exceeded that from the manual acquisition. The major difference between two training datasets was demonstrated in the inshore regions and in the vicinity of strong scattering vessels in which land artifacts, ships and the ghost signals derived from them were indiscernible by visual inspection. This study additionally introduced a possibility of resolving the unclassified usage of each vessel by comparing AIS information with the accurate vessel detection results.μ μ²ν μ§κ΅¬ κ΄μΈ‘ μμ±μΈ SARλ₯Ό ν΅ν μ λ° νμ§λ ν΄μ μμμ ν보μ ν΄μ μμ 보μ₯μ λ§€μ° μ€μν μν μ νλ€. κΈ°κ³ νμ΅ κΈ°λ²μ λμ
μΌλ‘ μΈν΄ μ λ°μ λΉλ‘―ν μ¬λ¬Ό νμ§μ μ νλ λ° ν¨μ¨μ±μ΄ ν₯μλμμΌλ, μ΄μ κ΄λ ¨λ λ€μμ μ°κ΅¬λ νμ§ μκ³ λ¦¬μ¦μ κ°λμ μ§μ€λμλ€. κ·Έλ¬λ, νμ§ μ νλμ κ·Όλ³Έμ μΈ ν₯μμ μ λ°νκ² μ·¨λλ λλμ νλ ¨μλ£ μμ΄λ λΆκ°λ₯νκΈ°μ, λ³Έ μ°κ΅¬μμλ μ λ°μ μ€μκ° μμΉ, μλ μ λ³΄μΈ AIS μλ£λ₯Ό μ΄μ©νμ¬ μΈκ³΅ μ§λ₯ κΈ°λ°μ μ λ° νμ§ μκ³ λ¦¬μ¦μ μ¬μ©λ νλ ¨μλ£λ₯Ό μλμ μΌλ‘ μ·¨λνλ μκ³ λ¦¬μ¦μ μ μνμλ€.
μ΄λ₯Ό μν΄ μ΄μ°μ μΈ AIS μλ£λ₯Ό SAR μμμ μ·¨λμκ°μ λ§μΆμ΄ μ ννκ² λ³΄κ°νκ³ , AIS μΌμ μμ²΄κ° κ°μ§λ μ€μ°¨λ₯Ό μ΅μννμλ€. λν, μ΄λνλ μ°λ체μ μμ μλλ‘ μΈν΄ λ°μνλ λνλ¬ νΈμ΄ ν¨κ³Όλ₯Ό 보μ νκΈ° μν΄ SAR μμ±μ μν 벑ν°λ₯Ό μ΄μ©νμ¬ μμ±κ³Ό μ°λ체 μ¬μ΄μ 거리λ₯Ό μ λ°νκ² κ³μ°νμλ€. μ΄λ κ² κ³μ°λ AIS μΌμμ μμ λ΄μ μμΉλ‘λΆν° μ λ° λ΄ AIS μΌμμ λ°°μΉλ₯Ό κ³ λ €νμ¬ μ λ° νμ§ μκ³ λ¦¬μ¦μ νλ ¨μλ£ νμμ λ§μΆμ΄ νλ ¨μλ£λ₯Ό μ·¨λνκ³ , μ΄μ μ λν μμΉ, μλ μ λ³΄μΈ VPASS μλ£ μμ μ μ¬ν λ°©λ²μΌλ‘ κ°κ³΅νμ¬ νλ ¨μλ£λ₯Ό μ·¨λνμλ€.
AIS μλ£λ‘λΆν° μ·¨λν νλ ¨μλ£λ κΈ°μ‘΄ λ°©λ²λλ‘ μλ μ·¨λν νλ ¨μλ£μ ν¨κ» μΈκ³΅ μ§λ₯ κΈ°λ° μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ ν΅ν΄ μ νλλ₯Ό νκ°νμλ€. κ·Έ κ²°κ³Ό, μ μλ μκ³ λ¦¬μ¦μΌλ‘ μ·¨λν νλ ¨ μλ£λ μλ μ·¨λν νλ ¨ μλ£ λλΉ λ λμ νμ§ μ νλλ₯Ό 보μμΌλ©°, μ΄λ κΈ°μ‘΄μ μ¬λ¬Ό νμ§ μκ³ λ¦¬μ¦μ νκ° μ§νμΈ μ λ°λ, μ¬νμ¨κ³Ό F1 scoreλ₯Ό ν΅ν΄ μ§νλμλ€. λ³Έ μ°κ΅¬μμ μ μν νλ ¨μλ£ μλ μ·¨λ κΈ°λ²μΌλ‘ μ»μ μ λ°μ λν νλ ¨μλ£λ νΉν κΈ°μ‘΄μ μ λ° νμ§ κΈ°λ²μΌλ‘λ λΆλ³μ΄ μ΄λ €μ λ νλ§μ μΈμ ν μ λ°κ³Ό μ°λ체 μ£Όλ³μ μ νΈμ λν μ νν λΆλ³ κ²°κ³Όλ₯Ό 보μλ€. λ³Έ μ°κ΅¬μμλ μ΄μ ν¨κ», μ λ° νμ§ κ²°κ³Όμ ν΄λΉ μ§μμ λν AIS λ° VPASS μλ£λ₯Ό μ΄μ©νμ¬ μ λ°μ λ―Έμλ³μ±μ νμ ν μ μλ κ°λ₯μ± λν μ μνμλ€.Chapter 1. Introduction - 1 -
1.1 Research Background - 1 -
1.2 Research Objective - 8 -
Chapter 2. Data Acquisition - 10 -
2.1 Acquisition of SAR Image Data - 10 -
2.2 Acquisition of AIS and VPASS Information - 20 -
Chapter 3. Methodology on Training Data Procurement - 26 -
3.1 Interpolation of Discrete AIS Data - 29 -
3.1.1 Estimation of Target Interpolation Time for Vessels - 29 -
3.1.2 Application of Kalman Filter to AIS Data - 34 -
3.2 Doppler Frequency Shift Correction - 40 -
3.2.1 Theoretical Basis of Doppler Frequency Shift - 40 -
3.2.2 Mitigation of Doppler Frequency Shift - 48 -
3.3 Retrieval of Training Data of Vessels - 53 -
3.4 Algorithm on Vessel Training Data Acquisition from VPASS Information - 61 -
Chapter 4. Methodology on Object Detection Architecture - 66 -
Chapter 5. Results - 74 -
5.1 Assessment on Training Data - 74 -
5.2 Assessment on AIS-based Ship Detection - 79 -
5.3 Assessment on VPASS-based Fishing Boat Detection - 91 -
Chapter 6. Discussions - 110 -
6.1 Discussion on AIS-Based Ship Detection - 110 -
6.2 Application on Determining Unclassified Vessels - 116 -
Chapter 7. Conclusion - 125 -
κ΅λ¬Έ μμ½λ¬Έ - 128 -
Bibliography - 130 -Maste
ν΄λ¦¬λΉλ μμ½μ¬κ³λ₯Ό μ΄μ©ν μλλ Έλ³΅ν©μ²΄μ μ μ‘° λ° νκ²½λΆμΌ μμ©
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : ννμ물곡νλΆ, 2014. 2. μ₯μ μ.μ΅κ·Ό λλ
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μλ₯Ό μ μ‘°νλ λ°μ μμ΄ μμ μ μν μ νμλ€. λΆλΆμ μΈ νμκ³Όμ μ κ±°μ³ κΈμ μ λλ
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μκ° λ°ν ν λ‘κ²νμ λλ
Έλ³΅ν©μ²΄λ₯Ό μ μ‘°ν μ μμκ³ , μ΄λ κ² μ μ‘°ν λλ
Έλ³΅ν©μ²΄λ κ°μκ΄ μμμ λΉμ ν‘μνλ νΉμ±μ 보μλ€. λν, κ°μκ΄ νμμ μ°μν νλΌμ€λͺ¬-κ΄μ΄λ§€ μ±λ₯μ λνλ΄μλ€. λ³Έ μ°κ΅¬λ₯Ό ν΅ν΄ ν λ‘κ²νμ μ§μ§μ²΄μ ν¬κΈ°κ° λΉ ν‘μ μμμ μν₯μ λ―ΈμΉλ€λ κ²μ λ°νλ΄μλ€.
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Όλ¬Έμλ κ°λ¨νκ³ μΉνκ²½μ μΈ κ³΅μ μ ν΅ν΄ μμ ν¨μ ν λλ
Έλ¬Όμ§λ€μ μ μ‘°νκ³ κ·Έλ₯Ό μμ©νλ κ°λ₯μ±μ λν μ°κ΅¬λ₯Ό μνν λ΄μ©μ λ΄μλ€. ν΄λ¦¬λΉλ μμ½μ¬μ μμ ν¨μ ν λλ
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Όλ¬Έμ μμ΄ λ°ν ν λ‘κ²νμ λλ
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Έλ³΅ν©μ²΄μ μ μ‘° κ³Όμ μ λν μ΄ν΄λ₯Ό μ¦μ§ μν¬ μ μμ κ²μΌλ‘ κΈ°λλλ€.1. Introduction 1
1.1 Background 1
1.1.1 Nanomaterials 1
1.1.2 Metal nanostructures 2
1.1.2.1 Silver nanoparticles 5
1.1.2.2 Silver halide nanocomposite 5
1.1.3 Metal-polmer nanocomposite 8
1.1.3.1. Electrospun silver-polymer nanofibers 8
1.1.3.2. Solution-phase synthesized silver-polymer nanofibers 10
1.1.4. Application of silver-containing nanocomposites 12
1.1.4.1 Visible light-responsive plasmonic photocatalyst 12
1.1.4.2 Antibacterial agent 13
1.1.4.2.1. Antibacterial silver nanoparticles 14
1.1.4.2.2. Antibacterial polymeric compounds 18
1.2 Objectives and Outlines 22
1.2.1 Objectives 22
1.2.2 Outlines 22
2. Experimental Details 24
2.1 Fabrication of silver halide nanomaterials 24
2.1.1 Fabrication of AgCl nanocubes with PVA stabilizer 24
2.1.2 Partical reduction of AgCl nanoparticles 25
2.1.3 Fabrication of AgBr nanocubes with PVA stabilizer 28
2.1.4 Partical reduction of AgBr nanoparticles 28
2.2 Fabrication of silver-polymer composite nanofibers 30
2.2.1 Fabrication of silver/poly(vinyl alcohol) nanofibers by complex-mediated growth 30
2.2.2 Fabrication of silver/poly(vinyl alcohol)/poly[2-(tert-butyl aminoethyl) methacrylate-co-ethylene glycol dimethacrylate] nanofibers using radical mediated dispersion polymerization 31
2.3 Applications 33
2.3.1 Photocatalytic properties of silver halide nanoparticles 33
2.3.1.1 Materials 33
2.3.1.2. Dye-decomposing test 33
2.3.2. Antibacterial properties of silver-polymer nanofibers 34
2.3.2.1 Materials 34
2.3.2.2 Modified Kirby-Bauer (KB) antimicrobial test 35
2.3.2.3 Antibacterial kinetic test 35
2.3.2.4 Minimum inhibitory concentration (MIC) test 36
2.4 Instrumental anaysis 37
3. Results and Discussions 39
3.1. Fabrication of size-controllable Ag@AgCl plasmonic nanoparticles through aqueous system as visible-light plasmonic photocatalysts 39
3.1.1 Fabrication of AgCl nanoparticles 39
3.1.1.1 Synthetic procedure of AgCl nanocubes using poly(vinyl alcohol) as stabilizer 39
3.1.1.2 Size-control of AgCl by varying reaction temperature 44
3.1.2 Systematic investigation of partial reduction of AgCl nanoparticles 47
3.1.2.1 Characterization of Ag@AgCl nanoparticles 47
3.1.2.2 Time-dependent observation of reduction of AgCl nanoparticles 51
3.1.2.3 The effect of AgCl size in the light absorption properties of Ag@AgCl nanoparticles 59
3.1.3 Visible-light responsive photocatalytic properties of synthesized Ag@AgCl nanoparticles 65
3.2. Fabrication of Ag@AgBr photocatalytic nanoparticles in aqueous system with PVA stabilizer 73
3.2.1 Fabrication procedure and size-control of AgBr nanocubes with poly(vinyl alcohol) stabilizing system 73
3.2.2 Size-control of AgBr nanoparitcles 77
3.2.3 Synthesis and characterization of Ag@AgBr nanocomposite 80
3.2.3.1 Microscopic observation of the Ag@AgBr nanoparticles 80
3.2.3.2 Spectroscopic observation of Ag@AgBr nanoparticles 83
3.2.2.3 The effect of AgBr size in the light absorption properties of Ag@AgBr nanoparticles 87
3.2.4 Visible-light responsive photocatalytic properties of synthesized Ag@AgBr nanoparticles 90
3.3. Fabrication of silver/poly(vinyl alcohol) composite nanofiber in aqueous system 94
3.3.1 Characterization of silver nanoparticles-containing poly(vinyl alcohol) nanofibers 95
3.3.1.1. Microscopic observation of the nanofibers 95
3.3.1.2. Spectroscopic analysis of the nanofibers 98
3.3.2. Systematic investigation of the fabrication of silver/poly(vinyl alcohol) nanofibers 104
3.3.2.1. Control experiments 104
3.3.2.2. Time-dependent observation of growth of the nanofibers 109
3.3.3. Study on synthetic mechanism of the complex-mediated growth of silver/poly(vinyl alcohol) nanofibers 114
3.4. Fabrication of silver/poly(vinyl alcohol)/poly[2-tert-butylaminoethyl] methacrylate] nanofibers through aqueous system as antibacterial agents 120
3.4.1. Fabrication and characterization of silver/poly(vinyl alcohol)/poly[2-(tert-butylaminoethyl) methacrylate] nanofibers 120
3.4.1.1. Synthetic procedure of the silver/cationic polymer nanofibers using a radical mediated dispersion polymerization 120
3.4.1.2. Characterization of the synthesized silver/cationic polymer nanofibers 123
3.4.1.2.1. Microscopic observation of the nanofibers 123
3.4.1.2.2. Spectroscopic observation of the nanofibers 128
3.4.2 Antibacterial properties of the synthesized silver nanoparticles embedded cationic polymer nanofibers 136
3.4.2.1 Modified Kirby-Bauer (KB) antimicrobial test 136
3.4.2.2 Antibacterial kinetic test 139
3.4.2.3 Minimum inhibitory concentration (MIC) test 142
4. Conclusion 145
References 150
κ΅λ¬Έμ΄λ‘ 162Docto
λ₯΄λ€μμ€ μ¬μ±μ μ 체μ±κ³Ό λ₯΄λ€μμ€ λλΌλ§ John Websterμ The White Devilκ³Ό The Duchess of Malfi μ°κ΅¬οΌ
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μμ΄μλ¬Ένκ³Ό μλ¬Ένμ 곡,1997.Maste
The analysis for anesthesia cost variation of colorectal cancer patients according to the severity
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μ μμ¬μ μ μ μνκ³ μ μνλμλ€. μ΄λ₯Ό μνμ¬ λμ₯μ νμμ μ§λ£λΉ νν©μ νμ
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μ΄λ₯Ό μν΄ μλκΆ μμ¬ μκΈμ’
ν©λ³μ μΈ κ³³μ νμ μ€ κ±΄κ°λ³΄νμ¬μ¬νκ°μ λμ₯μ ν΅κ³μ ν¬ν¨λλ μ½λ 7κ°μ§ μ€ νλλΌλ 보μ νκ³ μλ νμλ₯Ό λμμΌλ‘ μ 보λ₯Ό μμ§νκ³ μ§λ£λΉ λ΄μμ μ¬μ©νμλ€.
μ΄ μ°κ΅¬μ μ£Όμ κ²°κ³Όλ₯Ό μμ½νλ©΄ λ€μκ³Ό κ°λ€. νμμ μ€μ¦λλ₯Ό λ°μνμ§ λͺ»νλ€κ³ κ³ λ €λλ μ±λ³, μ°λ Ή λ±μμλ μ§λ£λΉμ λ³μ΄κ° μ‘΄μ¬νμ§ μμμΌλ νμμ μκΈλλ μ€μ¦λλ₯Ό λνλ΄λ ASA Stage λ³μμμλ μ§λ£λΉμ λ³μ΄κ° λνλλ κ²μΌλ‘ νμΈνμλ€. μ΄λ¬ν κ²°κ³Όλ₯Ό λ°νμΌλ‘ λμΌν μ§νμ κ°μ§ νμκ΅° λ΄μμλ μλ£ μΈλ ₯μ κΈ°μ , λ§μ·¨μμ κ΄λ ¨ μ¬λ£ λ±μμ μ°¨μ΄κ° μ‘΄μ¬ν μ μμμ νμΈνμλ€. κ·Έλ¬λ νν λ§μ·¨ κ΄λ ¨ μκ°μ§λΆμ λλ μ΄λ¬ν λ§μ·¨λΆμΌμ νΉμμ±μ κ°μνμ§ λͺ»νκ³ μλ μ€μ μ΄λ€.
μ΄ μ°κ΅¬ κ²°κ³Όλ₯Ό ν λλ‘ λ³΄λ©΄ λ§μ·¨λΆμΌμ λ³΄λ€ νμ€νλ μκ°μ§λΆμ²΄κ³μ λν λ
Όμκ° νμν κ²μΌλ‘ 보μ΄λ©° μ΄λ₯Ό μν΄ μ¬λ κΉμ νμ μ°κ΅¬κ° νμν κ²μΌλ‘ μ¬λ£λλ λ°μ΄λ€.
The purpose of this study was analyze the variation of anesthesia cost of patients who have colorectal cancer according to severity and to suggest policy implications. For these purpose, the status of the medical expenses of patients with colorectal cancer was analyzed and it was confirmed whether there was a variation in the medical expenses according to the characteristics by the characteristics including the severity. We also tried to identify which factor causes the cost variation in the colorectal cancer patients.
For this purpose, we collected information on the patients who have at least one of the seven code included cancer screening statistics of the Health Insurance Review and Assessment Service(HIRAs) among the three superior general hospital in the metropolitan area.
The major results of this study are summarized as follows. although there was no variation in the cost of gender and age, which were considered to not reflect the severity of the patient. But, it was confirmed that the ASA stage variable, which represents the emergency or severity of the patient. Depending on these results, it was confirmed that there may be difference in the technology of medical personnel and the anesthesia-related materials among the patients having the same disease. However, current anesthesia payment system does not consider the specificity of this anesthetic field.
According to the results of this study, the more realistic debates about payment system of anesthesia field and subsequent studies seem to be necessary.ope
A Study on the Improvement of Sequential Recommender System through Data Augmentation
νμλ
Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : μ΅ν©κ³ΌνκΈ°μ λνμ μ§λ₯μ 보μ΅ν©νκ³Ό, 2021. 2. μλ΄μ.Recommender systems, which have been developed based on the maturity of the information society, have recently achieved high performance improvement with the development of βDeep Learningβ technology that makes use of artificial deep neural network. Owing to the improved performance, its influence as well as its importance have been increasing today. Generally, it is known that the recommender models based on neural network structure can achieve higher performance than those with traditional approaches, but it premises a large volume of data in order to estimate numerous parameters of the model. However, it is quite expensive to obtain additional data when the size of dataset being in hand is small. Especially, recommender systems have more difficulty in obtaining additional data compared to other areas in that they deal with actual user behavior logs.
In the field of Computer Vision, where Deep Learning technology is being actively applied, it has been seeking to improve the performance of the model by greatly increasing the volume of training data using various data augmentation technologies. It is also expected that recommender systems can have significant benefits using these data augmentation technologies, but the research on data augmentation has not been sufficiently progressed yet in the recommender systems, especially in the field of sequential recommendation.
Motivated by this situation, we aim to show that various data augmentation techniques can improve the performance of sequential recommender system, especially when the training dataset is small. To this end, we describe how data augmentation changes the performance with the extensive experiment based on the latest sequential recommender model of neural network architecture. Our suggested data augmentation techniques are 1) Noise Injection, 2) Redundancy Injection, 3) Masking, 4) Synonym Substitution, all of which transform original item sequences in the way of direct corruption. Experiments performed with three benchmark datasets - βMovielens-1Mβ, βGowallaβ and βAmazon Video Gamesβ - demonstrate that overall performance improvement can be found when our suggested data augmentation techniques applied. Itβs notable that the performance improvement can be large if the size of dataset is relatively small. It can be said that applying data augmentation techniques can be effective to boost the performance, especially when the sequential recommender system doesnβt have enough dataset on the early stage of a service.
The contributions of this study can be summarized as follows: 1) it has confirmed with quantitative experiments that data augmentation technology can improve sequential recommendation performance when training dataset is small, 2) it suggests the possibility of further performance improvement of other current SOTA(State-Of-The-Art) models, in that data augmentation is applied in the pre-processing step and does not change the overall model architecture, 3) it has described how the performance changes in different manner according to the extensive application of various data augmentation techniques, and it furthermore verified that data augmentation can work as an universal pre-processing technique in the design of recommender system, 4) it can be referred as a basic research result in the process of developing the research on data augmentation in recommender systems, which has not yet been fully investigated.μ 보ν μλμ μ±μμ λ°νμΌλ‘ λ°λ¬ν΄ μ¨ μΆμ² μμ€ν
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Abstract 109Maste
Exploitation of Dual-polarimetric Index of Sentinel-1 SAR Data inVessel Detection Utilizing Machine Learning
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Assessment of Backprojection-based FMCW-SAR Image Restoration by Multiple Implementation of Kalman Filter
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Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.N