Since it is difficult to denoise and simplify a three dimensional point cloud data by a same parameter, a new unified algorithm based on the Extended Surface Variation based Local Outlier Factor (ESVLOF) for denoising and simplification of scattered point cloud was proposed. Through the analysis of the definition of ESVLOF, its properties were given. With the help of the surface variability computed in denoising process and the default similarity coefficient, the parameter γ which decreased with the increase of surface variation was constructed. Then the parameter γ was used as local threshold for denoising and simplifying point cloud. The simulation results show that this method can preserve the geometric characteristics of the original data. Compared with traditional 3D point-cloud preprocessing, the efficiency of this method is nearly doubled.

In order to enhance the performance of the synchronization and demodulation, a Very high frequency (VHF) Data Broadcast (VDB) signal demodulation algorithm based on the solution of differential equation was proposed. This algorithm eliminated the synchronization performance deterioration caused by the frequency offset. And frame synchronization, bit synchronization, frequency offset estimation and correction could be completed within a single set of synchronization symbols. The simulation results show that the method is effective to enhance the VDB signal demodulation performance.

For Direction-Of-Arrival (DOA) estimation of signal in additive noise, the traditional Multiple Signal Classification (MUSIC) algorithm cannot process the coherent signal with fewer snapshots. The searching scope of estimated DOA was considered as redundant dictionary. Consequently, the estimated DOA was taken as some elements in the dictionary, and could be represented sparsely by the dictionary. Then, this problem was thrown into the Second Order Cone (SOC) constraints and an efficient estimation algorithm using a single snapshot was developed. This constrained problem could be depicted as a standard SOC form and be solved by the SeDuMi, an optimization toolbox. The simulation results show that the proposed algorithm has a few advantages over the existing subspace method including one single snapshot to be needed, no requirement for the number of source signals, ability to work with coherent and non-coherent signals.

Concerning the clock skew and clock drift problem in wireless sensor networks, some different methods of synchronization time on synchronization accuracy were studied. With the principle of clock synchronization of cluster-shaped network structure, an optimized clock basis algorithm was put forward. And the Kalman filtering method was applied to adjust the nodes’ clock deviation in optimized recursive way. Compared with the general synchronization algorithm of cluster-shaped, the proposed algorithm can not only improve the synchronization accuracy, but also reduce the energy consumption of synchronous nodes. The simulation results show that the algorithm can accurately describe the synchronization precision and is more efficient for clock synchronization.