290 research outputs found
An Efficient ΣΔ-STAP Detector for Radar Seeker using RPCA Post-processing
Adaptive detection of moving targets in sea clutter environment is considered as one of the crucial tasks for radar seekers. Due to the severe spreading of the sea clutter spectrum, the ability of space-time adaptive processing with sum and difference beams (ΣΔ-STAP) algorithms to suppress the sea clutter is very limited. This paper, investigated the low-rank property of the range-Doppler data matrix according to the eigenvalue distribution from the eigen spectrum, and proposed an efficient ΣΔ-STAP detector based on the robust principle component analysis (RPCA) algorithm to detect moving targets, which meets the low-rank matrix recovery conditions. The proposed algorithm first adopts ΣΔ-STAP algorithm to preprocess the sea clutter, then separates the sparse matrix of target component from the range-Doppler data matrix through the RPCA algorithm, and finally, effectively detects moving targets in the range-Doppler plane. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm in the low signal-to-noise ratio scenarios.Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 344-349, DOI:http://dx.doi.org/10.14429/dsj.64.486
LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM
LiDAR odometry is one of the essential parts of LiDAR simultaneous
localization and mapping (SLAM). However, existing LiDAR odometry tends to
match a new scan simply iteratively with previous fixed-pose scans, gradually
accumulating errors. Furthermore, as an effective joint optimization mechanism,
bundle adjustment (BA) cannot be directly introduced into real-time odometry
due to the intensive computation of large-scale global landmarks. Therefore,
this letter designs a new strategy named a landmark map for bundle adjustment
odometry (LMBAO) in LiDAR SLAM to solve these problems. First, BA-based
odometry is further developed with an active landmark maintenance strategy for
a more accurate local registration and avoiding cumulative errors.
Specifically, this paper keeps entire stable landmarks on the map instead of
just their feature points in the sliding window and deletes the landmarks
according to their active grade. Next, the sliding window length is reduced,
and marginalization is performed to retain the scans outside the window but
corresponding to active landmarks on the map, greatly simplifying the
computation and improving the real-time properties. In addition, experiments on
three challenging datasets show that our algorithm achieves real-time
performance in outdoor driving and outperforms state-of-the-art LiDAR SLAM
algorithms, including Lego-LOAM and VLOM.Comment: 9 pages, 3 tables, 6 figure
Population Redistribution among Multiple Electronic States of Molecular Nitrogen Ions in Strong Laser Fields
We carry out a combined theoretical and experimental investigation on the
population distributions in the ground and excited states of tunnel ionized N2
molecules at various driver wavelengths in the near- and mid-infrared range.
Our results reveal that efficient couplings (i.e., population exchanges)
between the ground state and the excited states occur in strong laser fields.
The couplings result in the population inversion between the ground and the
excited states at the wavelengths near 800 nm, which is verified by our
experiment by observing the amplification of a seed at ~391 nm. The result
provides insight into the mechanism of free-space nitrogen ion lasers generated
in remote air with strong femtosecond laser pulses.Comment: 18 pages, 4 figure
Abnormal Global Functional Connectivity Patterns in Medication-Free Major Depressive Disorder
Mounting studies have applied resting-state functional magnetic resonance imaging (rs-fMRI) to study major depressive disorder (MDD) and have identified abnormal functional activities. However, how the global functional connectivity patterns change in MDD is still unknown. Using rs-fMRI, we investigated the alterations of global resting-state functional connectivity (RSFC) patterns in MDD using weighted global brain connectivity (wGBC) method. First, a whole brain voxel-wise wGBC map was calculated for 23 MDD patients and 34 healthy controls. Two-sample t-tests were applied to compare the wGBC and RSFC maps and the significant level was set at p < 0.05, cluster-level correction with voxel-level p < 0.001. MDD patients showed significantly decreased wGBC in left temporal pole (TP) and increased wGBC in right parahippocampus (PHC). Subsequent RSFC analyses showed decreased functional interaction between TP and right posterior superior temporal cortex and increased functional interaction between PHC and right inferior frontal gyrus in MDD patients. These results revealed the abnormal global FC patterns and its corresponding disrupted functional connectivity in MDD. Our findings present new evidence for the functional interruption in MDD
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