university of pretoria & csir small vessel detection in coastal radar data
DESCRIPTION
UNIVERSITY OF PRETORIA & CSIR Small Vessel Detection In Coastal Radar Data. M.D. Strempel Under supervision of Dr. P. de Villiers. Summary. Detect small vessels and other low observables from dense clutter data. Summary. - PowerPoint PPT PresentationTRANSCRIPT
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UNIVERSITY OF PRETORIA & CSIR
Small Vessel Detection In Coastal Radar Data
M.D. StrempelUnder supervision of
Dr. P. de Villiers
2
Summary
• Detect small vessels and other low observables from dense clutter data
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TFC15_023 No Threshold filtering Time bins 10:13
0 5 10 15 20 253000
3200
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4400
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Time [s]
Ran
ge [m
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TFC15_023 No Threshold filtering Time bins 10:13
0 5 10 15 20 253000
3200
3400
3600
3800
4000
4200
4400
-60
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Summary
• Detect small vessels and other low observables from dense clutter data
Time [s]
Ran
ge [m
]
TFC15_023 No Threshold filtering Time bins 10:13
85 90 95 100 1053000
3200
3400
3600
3800
4000
4200
4400
-60
-50
-40
-30
-20
-10
0
10
Time [s]
Ran
ge [m
]
TFC15_023 No Threshold filtering Time bins 10:13
85 90 95 100 1053000
3200
3400
3600
3800
4000
4200
4400
-60
-50
-40
-30
-20
-10
0
10
Current Methodology
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• Approach 1: Image processing technique• Approach 2: Time-based technique• Approach 3: Clustering technique (currently being pursued)
Proposed Methodology
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• Approach 1: Image processing technique– Use common image processing algorithms to simplify datasets.– detect and then track wave crests– Can then combine crests into groups– This can improve track quality and reduce computational
complexity
3000 3500 4000 4500-10
-5
0
5
10
15
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25Range bin time series analysis
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ge [m
]
Time [s]
Proposed Methodology
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• Approach 2: Time-based technique
– Specific range bin analysis (Bin: 3010)– Do estimation in the time series domain– When sinusoidal
structure collapses (estimation covariance high), there is a chance of a target – indicated by flat spot in this graph
target
Time [s]
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ge [m
] TFC15_023 No Threshold filtering range bin 10
1 2 3 4 5 6
x 104
34353435.23435.43435.63435.8
-40-20
0
Proposed Methodology
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• Approach 3: Clustering technique
– Specific Time bins– Bins: 10s to 13s– Track peaks above
threshold– Association on peaks– Cluster on tracks
Proposed Methodology
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• Approach 3: Velocity clustering technique
track1
track2
track3 Wav
e tra
ck
Proposed Methodology
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• Approach 3: Velocity clustering technique
• Association techniques:• Associate on velocity • Associate based on Doppler
track1
track2
track3 Wav
e tra
ck
Proposed Methodology
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• Approach 3: Velocity clustering technique• Moving data illustration