Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton's laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a lowcost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.
In this study, the effect of length of the stub on the formation of the Fano resonance in structures which possess a metal-insulator-metal (MIM) waveguide coupled to rectangular cavities by the stub is investigated theoretically and numerically. The resulting Fano resonance is used to design an ultrawideband bandstop filter that can filter all wavelengths between two telecommunication windows of λ = 850 n m and λ = 1310 n m . The structure is based on two rectangular cavities coupled to the MIM waveguide by stubs that are located at an adjusted distance from each other; the interference superposition of reflected and transmitted waves from each other will make this filtering phenomenon. The center wavelength of the bandstop of the structure is highly adjustable by changing the dimensions of the structure. The theoretical and the numerical results are, respectively, based on the transmission line model and the finite-difference time-domain method. The theoretical results comply well with the numerical ones. To analyze the Fano resonance, temporal coupled mode theory is also exploited. The proposed structure has significant applications in highly integrated optical circuits.
Automatic waveform recognition has become an important task in radar systems and spread spectrum communications. Identifying the modulation of received signals helps to recognize different invader transmitters. In this paper, a noise aware model is proposed to recognize the modulation type based on time-frequency characteristics. To this end, Choi-Williams representation is used to obtain spatial 2D pattern of received signal. After that, a deep model is constructed to make signal clear from noise and extract robust and discriminative features from time-frequency pattern, based on auto-encoder and Convolutional Neural Networks (CNN). In order to reduce the effect of noise and adversarial disorders, a new database of different modulation patterns with different AWGN noises and fading Rayleigh channel is created which helps model to avoid the effects of noise on modulation recognition. Our database contains radar modulations such as Barker, LFM, Costas and Frank code which are known as frequently used modulations on wireless communication. Infact, the main novelty of this work is designing this database and proposing noise-aware model. Experimental results demonstrate that the proposed model achieves superior performance for automatic classification recognition with 99.24% of accuracy in noisy medium with minimum SNR of -5dB while the accuracy is 97.90% in SNR of -5dB and f=15 Hz of Doppler frequency. Our model outperforms 5.54% in negative and 0.4% in positive SNRs (even though with less SNR).
In this paper, we apply liquid infiltration approach for supercontinuum generation (SCG) in photonic crystal fiber (PCF) in which by selectively infiltrating three air holes of PCF, a near zero dispersion is obtained that is a key parameter for SCG. Our numerical results show that by launching a very short input optical pulse of 50 fs in normal dispersion regime with wavelength centered about 700 nm, into 50 mm PCF infiltrated by ethanol, broadband, coherent and ripple free SC as wide as 1000 nm will be achieved that covers the visible light and a part of near infrared spectra used for ultrahigh-resolution optical coherence tomography.
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