Interference multipath is an important factor to affect the anti-jamming performance for the global navigation satellite system (GNSS) antenna array receiver. However, interference multipath must be considered in practical application. In this paper, the antenna array model for interference multipath is analyzed, and an equivalent model for interference multipath is proposed. According to the equivalent interference multipath model, the influence of interference multipath on anti-jamming performance is analyzed from the space only processing (SOP) and space-time adaptive processing (STAP). Interference multipath can cause loss of the degree of freedom (DoF) of SOP. Through analysis of the equivalent model and STAP mechanism, it further reveals how the STAP can solve the interference multipath. The simulation experiments prove that the equivalent model is effective, and the analysis conclusion is correct. This paper also points out that the interference bandwidth is wider and more taps in STAP are required, under the same experiment conditions.

/pdf/suppression-of-jammer-multipath-in-gnss-antenna-array-3w42mznq.pdf

Suppression of Jammer Multipath in GNSS Antenna Array Receiver

MEMS-IMUs have an extensive application in multifarious studies, as well as industrial and commercial areas. It is crucial to diminish their intrinsic errors in a suitable calibration procedure. In this paper, a novel calibration procedure was proposed for Inertial Measurement Units (IMUs) on a turntable. A general nonlinear model of the IMU output including the effects of bias, scale factor, misalignment, and lever arm was derived. Transformed Unscented Kalman Filter (TUKF) was utilized to perform the estimation of error parameters for gyroscopes and accelerometers. The calibration maneuvers were applied using a tri-axis turntable to create input signals. In addition, assuming the sensors not placing in the center of the table makes angular acceleration become another variable which affects the estimation of the error parameters. Therefore, a suitable angular acceleration estimator was designed utilizing the angular velocity output. According to experimental results, applying the proposed method caused 66% and 63% increase in the accuracy of gyroscopes and accelerometers outputs, respectively, compared with the calibrated signals based on the least square method.

Toward Calibration of Low-Precision MEMS IMU Using a Nonlinear Model and TUKF

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time-frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

https://www.mdpi.com/1424-8220/20/19/5633/pdf

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning.

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System.

Inertial navigation systems (INSs) use the temperature control system to ensure the stability of the temperature of the inertial sensors for improving the navigation accuracy of the INSs. That is, the temperature control accuracy affects the performance of the INSs. Thus, the performance of temperature control systems must be evaluated before their application. However, nearly all high-precision INSs are large and heavy and require long-term testing under many different experimental conditions. As a result, conducting an outdoor navigation experiment, which involves high–low temperature and heading rotation tests, is time consuming, laborious, and costly for researchers. To address this issue, an economical high–low temperature and heading rotation test method for high-precision platform INSs is proposed, and an evaluation system based on this method is developed to evaluate the performance of the temperature control systems for high-precision platform INSs indoors. The evaluation system uses an acrylic chamber, exhaust fans, temperature sensors, and an air conditioner to simulate the environment temperature change. The outer gimbals of the platform INSs are utilized to simulate the heading rotation. The temperature control system of a high-precision platform INS is evaluated using the proposed evaluation method. The temperature difference of the gyros is obtained in the high–low temperature test, and the temperature fluctuation of the temperature control system is observed in the rotation test. These tests verify the effectiveness of the proposed evaluation method. Then, the corresponding optimization method for the temperature control system of this high-precision platform INS is put forward on the basis of the test results of the evaluation system. Experimental results show that the maximum temperature differences of the two gyros between high- and low-temperature tests are decreased from 1.51 °C to 0.50 °C, and the maximum temperature fluctuation value of the temperature control system is decreased from 0.81 °C to 0.27 °C after the proposed evaluation and optimization processes. Therefore, the proposed methods are cost effective and useful for evaluating and optimization of the temperature control system for INSs.

/pdf/economical-high-low-temperature-and-heading-rotation-test-2l27g4wxyp.pdf

Economical High⁻Low Temperature and Heading Rotation Test Method for the Evaluation and Optimization of the Temperature Control System for High-Precision Platform Inertial Navigation Systems.

In the scenario of high dynamics and low C/N0, the discriminator output of a GNSS tracking loop is noisy and nonlinear. The traditional method uses a fixed-gain loop filter for error estimation, which is prone to lose lock and causes inaccurate navigation and positioning. This paper proposes a cascaded adaptive vector tracking method based on the KF+EKF architecture through the GNSS Software defined receiver in the signal tracking module and the navigation solution module. The linear relationships between the pseudo-range error and the code phase error, the pseudo-range rate error and the carrier frequency error are obtained as the measurement, and the navigation filter estimation is performed. The signal C/N0 ratio and innovation sequence are used to adjust the measurement noise covariance matrix and the process noise covariance matrix, respectively. Then, the estimated error value is used to correct the navigation parameters and fed back to the local code/carrier NCO. The field vehicle test results show that, in the case of sufficient satellite signals, the positioning error of the proposed method has a slight advantage compared with the traditional method. When there is signal occlusion or interference, the traditional method cannot achieve accurate positioning. However, the proposed method can maintain the same accuracy for the positioning results.

https://www.mdpi.com/2072-4292/13/8/1477/pdf

Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter

This paper presents an innovative complete observable initial alignment method for strapdown inertial navigation system. This method negates the need for multi-position rotation or a complex rotating mechanism. First, the coupling relationship between the error state variables is analytically derived. Based on the equivalence relationship between the acceleration output and the angular rate output and error state variables, an improved extended-measurement equation is then established. The feasibility of the scheme that accelerates the convergence speed is theoretically demonstrated, and the observability of the proposed method is qualitatively and quantitatively analyzed in comparisons with established methods (piece-wise constant system, singular value decomposition, and error covariance matrix analysis). The results confirm that the improved measurement model enhances the observability of state variables to different extent and can achieve complete observability of the system. The proposed method not only can provide the fast and accurate estimation of the alignment error, but also can predict the gyro bias online. More specifically, the equivalent horizontal accelerometer output certainly accelerated the convergence of the horizontal alignment error, and introducing the equivalent gyroscope output improved the convergence speed of the heading alignment error. It is a potential candidate for speeding up the convergence of the stationary initial alignment.

/pdf/a-novel-method-for-fast-stationary-initial-alignment-based-3qu01dlj1l.pdf

A Novel Method for Fast Stationary Initial Alignment Based on Extended Measurement Information

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Design and implementation of an open-source MATLAB code for GNSS/MEMS-INS deep integrated navigation

Insights into the Full Cycling of Oxygen in Vocs Oxidation on Α-Mno2: A Nap-Xps Study

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