Research on the Denoising of the Beidou Carrier Signal based on EEMD Algorithm with Adaptive Reconstruction Optimizing of IMF Numbers

The Beidou carrier signal is coupled into a certain noise during propagation and reception, and these noise will directly affect the processing procedure associated with it. To deal with the problem of the influence due to the manually setting the IMF (Intrinsic Mode Function) component number for the reconstruction signal, a new measuring index that used for finding the optimal IMF components to reconstruct the signal has been designed in this paper. The index has taken the shape of the signal, signal noise ratio and correlation index into consideration. Upon on the basis, an adaptive index optimization Ensemble Empirical Mode Decomposition (AIO-EEMD) algorithm has been proposed in this paper. To verify the validity of the algorithm, four different algorithms are used to denoised the collected Beidou signal. The experiment results show that the noise reduction using the AIO-EEMD method can not only automatically obtain the optimal IMF components number, but also has a significant advantage over the other three methods.

Adaptive Reconstruction Algorithm Based on Compressed Sensing Broadband Receiver

Existing greedy reconstruction algorithms require signal sparsity, and the remaining sparsity adaptive algorithms can be reconstructed but cannot achieve accurate sparsity estimation. To address this problem, a blind sparsity reconstruction algorithm is proposed in this paper, which is applied to compressed sensing radar receiver system. The proposed algorithm can realize the estimation of signal sparsity and channel position estimation, which mainly consists of two parts. The first part is to use fast search based on dichotomy search, which is based on the high probability reconstruction of greedy algorithm, and uses dichotomy search to cover the number of sparsity. The second part is the signal matching and tracking algorithm, which is mainly used to judge the signal position and reconstruct the signal. Combine the two parts together to realize the blind estimation of the sparsity and the accurate estimation of the number of signals when the number of signals is unknown. The experimental analyses are carried out to evaluate the performance of the reconstruction probability, the accuracy of sparsity estimation, the running time of the algorithm, and the signal-to-noise ratio.

Data compression in ViSAR sensor networks using non-linear adaptive weighting

Nowadays, industrial video synthetic aperture radars (ViSARs) are widely used for aerial remote sensing and surveillance systems in smart cities. A main challenge of a group of networked ViSAR sensors in an IoT-based environment is low bandwidth of wireless links for communicating big video data. In this research, we propose a non-linear statistical estimator for adaptive reconstruction of compressed ViSAR data. Our proposed reconstruction filter is based on an adaptively generated non-linear weight mask of spatial observations. It can strongly outperform several conventional and well-known reconstruction filters for three different video samples.