No abstract

With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

Radar target characteristics need to be accurately extracted to enhance the role of high-frequency (HF) radar target recognition technology in modern radar, sea and air monitoring, and other applications. The pole characteristics of radar targets have become a mainstream research focus because of their inherent advantages for target recognition. However, existing pole extraction methods for complex targets generally have problems in early- and late-time responses aliasing and target information loss. To avoid this problem, this study proposes a new method to extract radar target poles based on the special particle swarm optimization algorithm (SPSO) and an autoregressive moving average (ARMA) model. This method, which does not involve the distinction between early-and late-time responses, is used to estimate an approximation of the entire scattering echo of the target. Then the parameters of the model are precisely optimized with the help of a particle swarm optimization algorithm combined with opposition-based learning and inertia weight decreasing. Strategy. Owing to the characteristics of the azimuth consistency of the target poles, a sliding window is used to calibrate the positions of multi-azimuth poles in the complex plane. The method was demonstrated to be feasible with good performance when it was applied to extract the pole features of ships at different azimuths in the high-frequency band.

Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network (BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method.

Maritime safety issues have aroused great attention, and it has become a difficult problem to use the sky-wave over-the-horizon radar system to locate foreign targets or perform emergency rescue quickly and timely. In this paper, a distributed multi-point sky-wave over-the-horizon radar system is used to locate marine targets. A positioning algorithm based on the Doppler frequency is proposed, namely, the two-step weighted least squares (2WLS) method. This algorithm first converts the WGS-48 geodetic coordinates of the transceiver station to spatial rectangular coordinates; then, introduces intermediate variables to convert the nonlinear optimization problem into a linear problem. In the 2WLS method, four mobile transmitters and four mobile receivers are set up, and the Doppler frequency is calculated by transmitting and receiving signals at regular intervals; it is proven that the 2WLS algorithm has always maintained a better positioning accuracy than the WLS algorithm as the error continues to increase with a certain ionospheric height measurement error and the Doppler frequency measurement error. This paper provides an effective method for the sky-wave over-the-horizon radar to locate maritime targets.