In this letter, a novel time-frequency distribution, denoted as sliding fractional synchrosqueezing transform (SFSST), is proposed, which is further combined with ridge extraction to realize source localization and channel estimation. With the increase of the antenna array scale and the carrier frequency bands, the near-field zone is enlarging and thus the sources are distributed in both the far and near fields of the antenna array. Using the parabolic approximation of spherical waves, the received signal is modeled as the superposition of several linear frequency modulation (LFM) components with different frequency rates and initial frequencies. The SFSST is applied to LFM parameter estimation associated with the ridge extraction method, based on which the sources can be located and thus the channel can be estimated. The proposed method is of low complexity and robust to low SNR environments, which is verified via simulations.

In communications and radar/sonar systems, one of the most popular nonstationary stochastic signal models is the chirp cyclostationary (CCS) signal. Recently, the second-order statistics of complex CCS signals are defined based on the conjugate correlation function, which have shown to be more proper in linear canonical transform domain than in the frequency domain. However, the information provided by unconjugate correlation function is missing, which leads to an incomplete study of second-order CCS signals. In this letter, the unified conjugate and unconjugate correlation function of CCS signals is researched. In detail, the definitions of the novel second-order statistics, chirp cyclic correlation and chirp cyclic spectrum, are expounded. Then the properties of these statistics are investigated. Finally, the additional information provided by the correlation function with conjugate operation and the usefulness of the proposed statistics are introduced and explained by simulations.

The two-dimensional discrete fractional Fourier transform (2D DFrFT) has been shown to be a powerful tool for 2D signal processing. However, the existing discrete algorithms aren't the optimal for real-time applications, where the input signals are stream data arriving in a sequential manner. In this letter, a new sliding algorithm is proposed to solve this problem, termed as the 2D sliding DFrFT (2D SDFrFT). The proposed 2D SDFrFT algorithm directly computes the 2D DFrFT in current window using the results of previous window, which greatly reduces the computations. During the derivation, we find that the (m + δ, n)th DFrFT bin in previous window is needed for computing the (m, n)th DFrFT bin in current window, where the increment δ isn't always an integer. Further, a method is proposed to convert the increment δ to a certain integer by determining appropriate sampling interval. The theoretical analysis demonstrates that when compute the new 2D DFrFT in a shifted window in sliding process, our proposed algorithm has the lowest computational cost among existing 2D DFrFT algorithms.

In the wireless sensor actuator network (WSAN), in order to make the sensor nodes (S) and the actuator nodes (A) work together more efficiently and obtain more accurate assignment information of actuators, a novel data fusion model of actuators assignment were constructed. In this paper, the weights and thresholds of BP neural network (BPNN) were optimized by genetic algorithm (GA), and the GA-BPNN model was applied to prefabricated substation. In order to verify the characteristics of the model, the simulation and analysis of GA-BPNN were carried out comparing with the traditional BPNN data fusion model. The results demonstrate that the running time of the whole system can greatly be reduced, and the efficiency of operation and the correctness of the actuator nodes task assignment information can also be improved by using GA-BPNN task assignment data fusion model.

Reliability is an important research issue in wireless sensor networks(WSNs). Many literatures about how to improve network reliability in WSNs have been proposed, however the research on establishing evaluation models of reliability for WSNs is yet insufficient. In this paper, a model to evaluate the reliability of wireless sensor networks (WSNs) is proposed. This network state based model can effectively evaluate the probability of successful transmission of data packets in WSNs with the consideration of the work states of both the sensor node's different modules and the quality of the communication link. We evaluated the reliability of WSNs in single task and multi-task mode. At the same time, how different network topologies, such as flat and clustering structures, affect the reliability of WSNs is also discussed respectively. Furthermore, some typical reliable strategies, such as retransmission, multi-path transmission and multiple sink nodes, are analyzed and evaluated. The simulation attempts to evaluate the reliability of WSNs quantitatively and the results have shown the impacts of network state on reliability and the effectiveness in different reliable mechanisms.