Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Abstract

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

An Image Planar Positioning Method Base on Fusion of Dual-View Airborne SAR Data

“…Synthetic aperture radar [1,2] is a high-resolution imaging system. It can not only image the target area in all day, all weather, and high resolution [3] but can also achieve high-precision positioning of the target in the scene [4], which plays an important role in the effective monitoring of key targets and areas [5][6][7][8]. Airborne SAR [9], an important branch of the SAR system, has high military and industrial value due to the flexibility of the carrier.…”

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

“…At present, target detection methods under sea clutter suppression are mainly divided into four categories in [ 12 , 13 , 14 ]: time-domain cancellation method, subspace decomposition method, neural network clutter suppression method and time-frequency analysis. Time-domain cancellation method mainly includes moving target indication (MTI) [ 15 ], motion target detection (MTD) [ 16 , 17 , 18 , 19 , 20 ], adaptive moving target indication (AMTI) [ 21 , 22 ], space-time adaptive processing (STAP) [ 23 , 24 , 25 , 26 ] and root loop cancellation method [ 27 , 28 ]. The MTI method uses the difference of Doppler frequency between the target signal and the sea clutter signal to design the filter so that the band stop notch of the filter is aligned with the center frequency of sea clutter as much as possible to realize the suppression of the sea clutter.…”