An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris
<p>Composition of charge coupled device (CCD) star image besides background: (<b>a</b>) non- geostationary earth orbit (GEO) debris; (<b>b</b>) GEO debris; (<b>c</b>) stars.</p> "> Figure 2
<p>Schematic diagram of structural element decomposition.</p> "> Figure 3
<p>CCD star image before and after background suppression: (<b>a</b>) before background suppression; (<b>b</b>) after background suppression.</p> "> Figure 4
<p>Definition of correlation region of signal-to-noise ratio (SNR).</p> "> Figure 5
<p>The procedure of calculating the translational vector of the <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>-</mo> <mi>t</mi> <mi>h</mi> </mrow> </semantics></math> sub-image.</p> "> Figure 6
<p>The search strategy schematic.</p> "> Figure 7
<p>The change of average false alarm and missed detection number per frame with <math display="inline"><semantics> <mi>η</mi> </semantics></math>.</p> "> Figure 8
<p>Result image and corresponding gray distribution of different differential algorithms: (<b>a</b>) absolute difference result image; (<b>b</b>) dilation difference result image; (<b>c</b>) enhanced dilation difference result image (<math display="inline"><semantics> <mrow> <mi>η</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>); (<b>d</b>) absolute difference gray distribution; (<b>e</b>) dilation difference gray distribution; (<b>f</b>) enhanced dilation difference gray distribution.</p> "> Figure 9
<p>Typical cases of measured data: (<b>a</b>) close target; (<b>b</b>) dim target; (<b>c</b>) targets similar to stars; (<b>d</b>) high-brightness and fast-moving disturbing object; (<b>e</b>) exposure time change; (<b>f</b>) strong moonlight interference.</p> "> Figure 10
<p>SNR of targets before image preprocessing.</p> "> Figure 11
<p>SNR of targets after image preprocessing.</p> "> Figure 12
<p>The receiver operating characteristic curves (ROC) of the proposed algorithm and the twice frame differential algorithm.</p> "> Figure 13
<p>The number of false targets detected in each frame of seven image sequences.</p> "> Figure 14
<p>Seq_III detection result.</p> ">
Abstract
:1. Introduction
2. GSO Debris Extractor
2.1. Background Suppression Based on Morphological Top-Hat Transform
2.2. Adaptive Fast Image Registration
2.3. Stellar Suppression Based on the Enhanced Dilation Difference Algorithm
2.4. GSO Debris Enhancement and Target Segmentation Based on Inter-Frame Correlation and Threshold Segmentation Technology
3. GSO Debris Tracker
3.1. Adaptive Inter-Frame Interval Estimation
3.2. GNN Multi-Target Tracking Algorithm
4. Results
4.1. Introduction of Measured Data
4.2. Processing Results of Measured Data
5. Conclusions and Prospects
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Time(s) | Seq_I (144) | Seq_II (105) | Seq_III (104) | Seq_IV (44) | Seq_V (42) | Seq_VI (36) | Seq_VII (21) |
---|---|---|---|---|---|---|---|
JointGLMB (SMC) [30] | 189.523 | 130.008 | 108.813 | 29.544 | 19.893 | 28.419 | 13.075 |
GLMB(GMS) [43] | 197.426 | 68.187 | 57.745 | 16.503 | 5.623 | 15.390 | 9.381 |
CBMeMBer (GMS) [29] | 1.181 | 0.702 | 0.864 | 0.151 | 0.116 | 0.361 | 0.093 |
CPHD(GMS) [28] | 1.005 | 0.795 | 0.745 | 0.186 | 0.167 | 0.264 | 0.101 |
PHD(GMS) [27] | 0.51 | 0.484 | 0.516 | 0.084 | 0.093 | 0.172 | 0.066 |
MHT [33] | 5.398 | 4.33 | 4.79 | 1.918 | 3.002 | 1.903 | 1.672 |
JPDA [32] | 2.493 | 2.075 | 2.262 | 0.763 | 1.345 | 0.754 | 0.612 |
GNN [31] (pp. 203–205) | 2.176 | 1.972 | 2.253 | 0.732 | 1.525 | 0.729 | 0.597 |
Number | Seq_I | Seq_II | Seq_III | Seq_IV | Seq_V | Seq_VI | Seq_VII |
---|---|---|---|---|---|---|---|
GEO debris | 6 | 7 | 6 | 4 | 4 | 5 | 3 |
Non-GEO debris | 2 | 4 | 3 | 0 | 1 | 0 | 2 |
SNR Multiples | 1313 | 1343 | 234 | 1390 | 595 | 539 | 780 |
Target | SNR_min (dB) | Omi_R (%) | Target | SNR_min (dB) | Omi_R (%) | Target | SNR_min (dB) | Omi_R (%) |
---|---|---|---|---|---|---|---|---|
Target1 | 9.85 | 0.00 | Target17 | 27.12 | 0.70 | Target33 | 17.43 | 4.35 |
Target2 | 13.4 | 0.00 | Target18 | 10.26 | 1.41 | Target34 | 19.53 | 4.35 |
Target3 | 14.14 | 0.00 | Target19 | 13.21 | 1.41 | Target35 | 22.61 | 4.35 |
Target4 | 14.23 | 0.00 | Target20 | 15.45 | 1.41 | Target36 | 25.71 | 4.35 |
Target5 | 14.78 | 0.00 | Target21 | 17.06 | 1.41 | Target37 | 10.71 | 4.90 |
Target6 | 16.32 | 0.00 | Target22 | 22.96 | 1.41 | Target38 | 16.7 | 6.52 |
Target7 | 16.99 | 0.00 | Target23 | 7.37 | 1.96 | Target39 | 13.33 | 9.38 |
Target8 | 19.28 | 0.00 | Target24 | 14.35 | 2.63 | Target40 | 17.13 | 12.50 |
Target9 | 20.5 | 0.00 | Target25 | 8.24 | 2.82 | Target41 | 5.91 | 16.67 |
Target10 | 23.06 | 0.00 | Target26 | 6.98 | 2.88 | Target42 | 10.91 | 17.65 |
Target11 | 24.22 | 0.00 | Target27 | 7.68 | 2.88 | Target43 | 15.81 | 19.05 |
Target12 | 25.31 | 0.00 | Target28 | 18.27 | 2.88 | Target44 | 6.024 | 30.39 |
Target13 | 29.89 | 0.00 | Target29 | 7.19 | 2.94 | Target45 | 24.56 | 40.66 |
Target14 | 33.08 | 0.00 | Target30 | 10.55 | 2.94 | Target46 | 17.69 | 53.33 |
Target15 | 33.5 | 0.00 | Target31 | 9.97 | 3.13 | Target47 | 12.49 | 56.67 |
Target16 | 24.46 | 0.70 | Target32 | 11.48 | 3.92 |
Time(s) | Seq_I | Seq_II | Seq_III | Seq_IV | Seq_V | Seq_VI | Seq_VII |
---|---|---|---|---|---|---|---|
Total | 189.197 | 130.945 | 122.649 | 83.118 | 85.445 | 76.034 | 31.661 |
Single Frame | 1.314 | 1.247 | 1.179 | 1.889 | 2.034 | 2.112 | 1.508 |
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Sun, Q.; Niu, Z.; Wang, W.; Li, H.; Luo, L.; Lin, X. An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris. Sensors 2019, 19, 4026. https://doi.org/10.3390/s19184026
Sun Q, Niu Z, Wang W, Li H, Luo L, Lin X. An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris. Sensors. 2019; 19(18):4026. https://doi.org/10.3390/s19184026
Chicago/Turabian StyleSun, Quan, Zhaodong Niu, Weihua Wang, Haijing Li, Lang Luo, and Xiaotian Lin. 2019. "An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris" Sensors 19, no. 18: 4026. https://doi.org/10.3390/s19184026