Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

A state variable block diagram method is given for the realization of universal biquadratic transfer functions employing second-generation current-controlled conveyors (CCCIIs). Using minimum number of passive components and properly adjusting the bias currents of CCCIIs, the proposed circuits can realize all the tunable frequency standard filter functions: high-pass, band-pass, low-pass, notch-pass, and all-pass by choosing appropriate input branches without changing the passive elements. These presented circuits are in current-mode and voltagemode separately. The non-ideality analyses of these configurations are given. Additionally, a high-order low-pass filter derived from the proposed voltage-mode biquadratic filter is introduced. PSPICE simulation results are included to verify the theory.

A novel fully differential telescopic operational transconductance amplifier (OTA) is proposed. An additional PMOS differential pair is introduced to improve the unit-gain bandwidth of the telescopic amplifier. At the same time, the slew rate is enhanced by the auxiliary slew rate boost circuits. The proposed OTA is designed in a 0.18μm CMOS process. Simulation results show that there is a 49% improvement in the unit-gain bandwidth compared to that of a conventional OTA; moreover, the DC gain and the slew rate are also enhanced.