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RSS FeedsRemote Sensing, Vol. 12, Pages 1150: Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target (Remote Sensing)

 
 

3 april 2020 21:02:48

 
Remote Sensing, Vol. 12, Pages 1150: Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target (Remote Sensing)
 


Passive detection of a moving aerial target is critical for intelligent surveillance. Its implementation can use signals transmitted from satellites. Nowadays, various types of satellites co-exist which can be used for passive detection. As a result, a satellite signal receiver may receive signals from multiple heterogeneous satellites, causing difficult in echo signal detection. In this paper, a passive moving aerial target detection method leveraging signals from multiple heterogeneous satellites is proposed. In the proposed method, a plurality of direct wave signals is separated in a reference channel first. Then, an adaptive filter with normalized least-mean-square (NLMS) is adopted to suppress direct-path interference (DPI) and multi-path interference (MPI) in a surveillance channel. Next, the maximum values of the cross ambiguity function (CAF) and the fourth order cyclic cumulants cross ambiguity function (FOCCCAF) correspond into each separated direct wave signal and echo signal will be utilized as the detection statistic of each distributed sensor. Finally, final detection probabilities are calculated by decision fusion based on results from distributed sensors. To evaluate the performance of the proposed method, extensive simulation studies are conducted. The corresponding simulation results show that the proposed fusion detection method can significantly improve the reliability of moving aerial target detection using multiple heterogeneous satellites. Moveover, we also show that the proposed detection method is able to significantly improve the detection performance by using multiple collaborative heterogeneous satellites.


 
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Remote Sensing, Vol. 12, Pages 1151: Spatiotemporal Variations in Energy Consumption and Their Influencing Factors in China Based on the Integration of the DMSP-OLS and NPP-VIIRS Nighttime Light Datasets (Remote Sensing)
Remote Sensing, Vol. 12, Pages 1145: Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning (Remote Sensing)
 
 
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