Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

Information gain based sensor search scheduling for low-earth orbit constellation estimation

This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation.Several search scheduling methods based on the commonly used information gain are compared via simulations first.Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model.Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.

Digital spiral object identification using random light

Photons that are entangled or correlated in orbital angular momentum have been extensively used for remote sensing,object identification and imaging.It has recently been demonstrated that intensity fluctuations give rise to the formation of correlations in the orbital angular momentum components and angular positions of random light.Here we demonstrate that the spatial signatures and phase information of an object with rotational symmetries can be identified using classical orbital angular momentum correlations in random light.The Fourier components imprinted in the digital spiral spectrum of the object,as measured through intensity correlations,unveil its spatial and phase information.Sharing similarities with conventional compressive sensing protocols that exploit sparsity to reduce the number of measurements required to reconstruct a signal,our technique allows sensing of an object with fewer measurements than other schemes that use pixel-by-pixel imaging.One remarkable advantage of our technique is that it does not require the preparation of fragile quantum states of light and operates at both low-and high-light levels.In addition,our technique is robust against environmental noise,a fundamental feature of any realistic scheme for remote sensing.

C60-based clustering scheme for sensor management in STSS

Clustering-based sensor-management schemes have been widely used for various wireless sensor networks(WSNs), as they are well suited to the distributive and collaborative nature of WSN. In this paper, a C60-based clustering algorithm is proposed for the specific planned network of space tracking and surveillance system(STSS), where all the sensors are partitioned into 12 clusters according to the C60(or football surface) architecture, and then a hierarchical sensor-management scheme is well designed.Finally, the algorithm is applied to a typical STSS constellation,and the simulation results show that the proposed method has better target-tracking performance than the nonclustering scheduling method.