Search

This letter presents a wideband antenna for high-precision Global Navigation Satellite System (GNSS) applications. The antenna consists of a pair of improved orthogonal dipoles that are fed by a feed network with Composite Right/Left-Handed Transmission Lines (CRLH-TLs), a polygonal patch, and a reflector. By using improved orthogonal dipole, the beamwidth of the antenna is broadened. The CRLH-TLs is adopted in feed network to obtain outputs with stable phase difference. Experiment results indicate that the antenna has a less than 10-dB return-loss bandwidth in the range of 1.1 to 1.7 GHz, a 3-dB axial-ratio bandwidth from 1.2 to 1.6 GHz, and a larger than 6.37 dBi gain in the whole operating band. The measured results show that the proposed antenna has stable performance in the whole operating band, which means that it is a suitable antenna used for GNSS applications.

Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate "high-dimensional disasters", Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment. [ABSTRACT FROM AUTHOR]

Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter (KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN. [ABSTRACT FROM AUTHOR]

Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, a three-dimensional wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified. [ABSTRACT FROM AUTHOR]

An anti-multipath antenna is an antenna that can effectively suppress multipath signals from the source of signal reception. There are many problems in the traditional choke design, such as excessive volume and low elevation gain damage. In this paper, the conventional choke ring is improved by using the complementary trapezoidal structure, and the detailed design process is given. The measured results show that the choke has good resistance to a multipath effect in the navigation frequency band (1.1–1.7 GHz) and can significantly improve the front-to-back ratio of the antenna. [ABSTRACT FROM AUTHOR]

A circular polarized square fractal patch antenna with square slot and via is proposed, which can be applied to global navigation satellite systems. Two pairs of square ring slot with different sizes are placed diagonally for antenna circularly polarized radiation. By changing the square patch into a fractal structure and adding defective ground structure, miniaturization can be achieved while the bandwidth is further expanded. The total antenna size is 0.3λo × 0.3λo × 0.03λo (λo is the free‐space wavelength at 1.575 GHz). The final measurement results show that the maximum gain is 3.26 dBic, the 3 dB axial ratio (AR) bandwidth is 1.57–1.6 GHz and the impedance bandwidth (S11 < −10 dB) is more than 60 MHz at the resonant frequency of 1.575 GHz. [ABSTRACT FROM AUTHOR]

In this letter, an improved wideband dipole antenna is proposed for BEIDOU/GPS/GLONASS/GALILEO systems. The proposed antenna includes crossed narrow magneto-electric dipoles that are excited by two -shaped strip probes, a 3-dB hybrid coupler, and reflecting plate. The experiment results reveal that the proposed antenna achieves a wide impedance bandwidth of 51.4% (1.1-1.85 GHz) for VSWR < 1.5. A 3-dB axial-ratio bandwidth of 42.9% (1.1-1.7 GHz) and a gain of 5.6 ±0.6 dBi are covered by the whole operating band. Moreover, in the operating bandwidth, very stable, symmetrical radiation patterns and high gain at the low angle are obtained. [ABSTRACT FROM AUTHOR]

Although the use of a classical Venturi tube for wet gas metering has been extensively studied in the literature, the use of an extended-throat Venturi (ETV) tube has rarely been reported since its first proposal by J. R. Fincke in 1999. The structure of an ETV is very simple, but due to the complexity of multiphase flow, its theoretical model has not been fully established yet. Therefore, in this paper theoretical models have been developed for the convergent and throat sections of an ETV, and the gradients of front and rear differential pressures are derived analytically. Several flowrate algorithms have been proposed and compared with the existing ones. Among them, the iteration algorithm is found to be the best. A reasonable explanation is provided for its performance. The relationship between the differential pressure gradient and the flowrate relative error is also studied, such that the relative error distributions varying with ETV measured flowrates can be derived. The gas flowrate error of ETV increases with the liquid content whilst the liquid flowrate error of ETV decreases with the liquid content, and the relative errors of liquid flowrate are generally 2 to 3 times larger than that of the gas flowrate. Finally, the ETV tends to be more accurate than the classical Venturi tube. The ETV can be designed more compact under the same signal intensity due to its significantly higher velocity in the throat section. [ABSTRACT FROM AUTHOR]