Hongyi Xu

This paper proposes a coupled compressed samples online robust learning (C2ORL) algorithm for constructing an adaptive analog-to-information conversion (AIC) structure. The C2ORL optimizes both the dictionary and the observation matrix in each mini-batch learning process. The compressed samples are used for learning to resolve the issue that both the analog received signals and the unstable reconstructed signals affected by reconstruction error are not suitable for direct training. Furthermore, the online mechanism and the robust objective functions are adopted in order to make the learning algorithm better matched for the requirements of the analog signal compressed sensing (CS). Based on the C2ORL, we construct an adaptive AIC structure, which can considerably improve the performance in the sense of minimizing the reconstruction error. Also, the structure is more tolerant to the outliers and the noise in the receive process and has denoising ability to improve the signal-to-noise ratio (SNR) under the noise background. The mathematical expression shows that the proposed structure clearly reflects the optimization brought by the coupled learning algorithm. Finally, the experiments demonstrate that the proposed scheme considerably outperforms any existing approaches.

This paper proposed a channelized-based denoising generalized orthogonal matching pursuit algorithm (CD-gOMP) for reconstructing structural sparse signal in engineering application. The algorithm combines the CS channelization method, the pre-estimation based adaptive method with the generalized orthogonal matching pursuit (gOMP). By channelizing the observation matrix, the algorithm first eliminates most of the channels that only contain noise with a residual based detection method. Then, according to a pre-estimated sparsity level, the signal can be accurately and adaptively reconstructed by re-screening the redundant support obtained by the gOMP. The two steps of the algorithm effectively reduce the deterioration of the reconstruction caused by noise, thereby significantly improving the output signal-to-noise ratio (SNR). A mathematical derivation of the reconstruction conditions is given. Also, the computational complexity and the theoretical SNR improvement are discussed. Besides, the upper bound of the reconstruction error in the noise environment is mathematically analyzed. Finally, the experiments verified the performance analysis and detailed demonstrated the advantages of the proposed algorithm for recovering structural sparsity signals under noise interference. The results show that the proposed scheme considerably outperforms any existing adaptive and denoising greedy algorithm in the sense of the reconstruction accuracy and the output SNR.

In the development of analog signal compressive sensing (CS), the degradation of reconstruction performance under noise is the main bottleneck, because the CS framework is very sensitive to noise. This paper proposes an adaptive channelization-based orthogonal matching pursuit algorithm (C-OMP) combining the channelization and the adaptive iteration methods. The proposed C-OMP has two steps: channel screening and global iteration. Based on the proposed method, the original signal can be recovered adaptively in high probability of success with fewer observations under the noise background. Simultaneously, the noise can be reduced as much as possible to enhance the output signal-to-noise ratio (SNR) by excluding the noise channel during the channel screening and separating noise atoms during the global iteration. The relationship between the probability of successful reconstruction and the number of observations is mathematically analyzed. Furthermore, the parameter settings, computational complexity and output SNR are analytically evaluated. The simulation results confirm the analytical results and further demonstrate the effectiveness and advantages of the C-OMP in the noise environment. Overall, the proposed algorithm considerably improves the performance of the analog signal CS in the practical noisy environment.

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications.

The multi‐dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum‐behaved particle swarm optimisation and self‐adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi‐constrained conditions. Finally, the HQPSOGA is compared with the integer‐value genetic algorithm, standard particle swarm optimisation (PSO) and quantum‐behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.