Yalong Ban

Fast thermal calibration of low-grade inertial sensors and inertial measurement units.

The errors of low-cost inertial sensors, especially Micro-Electro Mechanical Systems (MEMS) ones, are highly dependent on environmental conditions such as the temperature. Thus, there is a need for the development of accurate and reliable thermal compensation models to reduce the impact of such thermal drift of the sensors. Since the conventional thermal calibration methods are typically time-consuming and costly, an efficient thermal calibration method to investigate the thermal drift of a full set of gyroscope and accelerometer errors (i.e., biases, scale factor errors and non-orthogonalities) over the entire temperature range in a few hours is proposed. The proposed method uses the idea of the Ramp method, which removes the time-consuming process of stabilizing the sensor temperature, and addresses its inherent problems with several improvements. We change the temperature linearly for a complete cycle and take a balanced strategy by making comprehensive use of the sensor measurements during both heating and cooling processes. Besides, an efficient 8-step rotate-and-static scheme is designed to further improve the calibration accuracy and efficiency. Real calibration tests showed that the proposed method is suitable for low-grade IMUs and for both lab and factory calibration due to its efficiency and sufficient accuracy.

Modeling and Development of INS-Aided PLLs in a GNSS/INS Deeply-Coupled Hardware Prototype for Dynamic Applications

A GNSS/INS deeply-coupled system can improve the satellite signals tracking performance by INS aiding tracking loops under dynamics. However, there was no literature available on the complete modeling of the INS branch in the INS-aided tracking loop, which caused the lack of a theoretical tool to guide the selections of inertial sensors, parameter optimization and quantitative analysis of INS-aided PLLs. This paper makes an effort on the INS branch in modeling and parameter optimization of phase-locked loops (PLLs) based on the scalar-based GNSS/INS deeply-coupled system. It establishes the transfer function between all known error sources and the PLL tracking error, which can be used to quantitatively evaluate the candidate inertial measurement unit (IMU) affecting the carrier phase tracking error. Based on that, a steady-state error model is proposed to design INS-aided PLLs and to analyze their tracking performance. Based on the modeling and error analysis, an integrated deeply-coupled hardware prototype is developed, with the optimization of the aiding information. Finally, the performance of the INS-aided PLLs designed based on the proposed steady-state error model is evaluated through the simulation and road tests of the hardware prototype.

Quantitative Analysis to the Impacts of IMU Quality in GPS/INS Deep Integration

In the Global Positioning System (GPS)/Inertial Navigation System (INS) deep integration system, the pure negative effect of the INS aiding is mainly the INS navigation error that is independent with the motion dynamics, which determine whether the INS aiding is worthy. This paper quantitatively assesses the negative effects of the inertial aiding information from different grades of INS by modeling the phase-locked loops (PLLs) based on the scalar-based GPS/INS deep integration system under stationary conditions. Results show that the largest maneuver-independent velocity error caused by the error sources of micro-electro-mechanical System (MEMS) inertial measurement unit (IMU) is less than 0.1 m/s, and less than 0.05 m/s for the case of tactical IMU during the typical GPS update interval (i.e., 1 s). The consequent carrier phase tracking error in the typical tracking loop is below 1.2 degrees for MEMS IMU case and 0.8 degrees for the tactical IMU case, which are much less than the receiver inherent errors. Conclusions can be reached that even the low-end MEMS IMU has the ability of aiding the receiver signal tracking. The tactical grade IMU can provide higher quality aiding information and has potential for the open loop tracking of GPS.

Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics

The phase locked loop (PLL) bandwidth suffers a dilemma on carrier phase accuracy and dynamic stress tolerance in stand-alone global navigation satellite systems (GNSS) receivers. With inertial navigation system (INS) aiding, PLLs only need to tolerate aiding information error, instead of dynamic stress. To obtain accurate carrier phase under high dynamics, INS-aided PLLs need be optimally designed to reduce the impact of aiding information error. Typical micro-electro-mechanical systems (MEMS) INS-aided PLLs are implemented and tested under high dynamics. Tests using simulation show there is a step change in the aiding information at each integer second, which deteriorates the carrier phase accuracy. An improved structure of INS-aided PLLs is proposed to eliminate the step change impact. Even when the jerk is 2000 m/s3, the tracking error of the proposed INS-aided PLL is no more than 3°. Finally, the performances of stand-alone PLLs and INS-aided PLLs are compared using field tests. When the antenna jerk is 300 m/s3, the carrier phase error from the stand-alone PLLs significantly increased, while the carrier phase error from the MEMS INS-aided PLLs almost remained the same. Therefore, the proposed INS-aided PLLs can suppress tracking errors caused by noise and dynamic stress simultaneously under high dynamics.

Low-end MEMS IMU can contribute in GPS/INS deep integration

In a deeply-coupled GPS/INS integrated system, the use of the inertial aiding information can improve the tracking loop performance and make the system more robust. To meet this requirement, the inertial aiding information should have sufficient accuracy in short-term (such as the sampling interval of GPS, e.g. 1sec). The MEMS (Micro-Electro Mechanical System) IMU (Inertial Measurement Unit) can be a promising candidate due to its small size and low cost. There should be no doubt that MEMS INS (Inertial Navigation System) can aid the GPS receiver tracking loop by eliminating the dominant part of the motion dynamic stress, considering that the INS errors induced by the receiver motion dynamics is much less than the motion dynamic itself, when the receiver manoeuvres. So the only concern the side effect caused by MEMS INS, which determine whether MEMS IMU is qualified for deep integration, is its navigation error independent with the motion dynamics (i.e. manoeuvre-independent error). This paper assesses this side effect of MEMS INS in terms of providing Doppler aiding data in to the GPS carrier tracking loop through a thorough error propagation analysis. The Laplace transform analysis is applied to the simplified INS error dynamic equations under stationary condition and find out the transfer relation between the error sources and the velocity estimation errors. Then the velocity error is converted to Doppler aiding error and substitute into the GPS tracking loop to analyze the corresponding carrier phase error. Results show that the largest velocity error caused by maneuver-independent errors is less than 0.1m/s during the typical GPS update interval (e.g. 1 sec), which meets the real road test results. The consequent carrier phase tracking error caused by the maneuver-independent error of MEMS INS is below 1.2 degree, which is much less than receiver inherent errors (e.g. the oscillator error and thermal noise). Conclusion can be reached that even the low-end MEMS IMUs have the ability of aiding the GPS receiver signal tracking although it induces some additional errors.

Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of Matrix

To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.

How the Integral Operations in INS Algorithms Overshadow the Contributions of IMU Signal Denoising Using Low-Pass Filters

This paper has made a comprehensive investigation of the contribution of inertial measurement unit (IMU) signal denoising in terms of navigation accuracy, through theoretical analysis, simulations and real tests. Analysis shows that the integral step in the inertial navigation system (INS) algorithm is essentially equivalent to a super low-pass filter (LPF), whose filtering strength is related to the integral time of the INS. Therefore the contribution of the IMU denoising filter is almost completely overshadowed by the effect of the integral step for normal navigation cases. The theoretical analysis result was further verified by the simulations with an example of inertial angle estimation and by real tests of INS and GPS/INS systems. Results showed that the IMU signal denoising cannot bring observable improvement to INS or GPS/INS systems. This conclusion is strictly valid in the condition that the equivalent cut-off frequency of the integral step (which equals the reciprocal of the INS working alone time) is lower than the cut-off frequency of the denoising filter, which is the usual case for INS applications (except for some static data processing such as the stationary alignment of INS).

Modeling and Quantitative Analysis of GNSS/INS Deep Integration Tracking Loops in High Dynamics

To meet the requirements of global navigation satellite systems (GNSS) precision applications in high dynamics, this paper describes a study on the carrier phase tracking technology of the GNSS/inertial navigation system (INS) deep integration system. The error propagation models of INS-aided carrier tracking loops are modeled in detail in high dynamics. Additionally, quantitative analysis of carrier phase tracking errors caused by INS error sources is carried out under the uniform high dynamic linear acceleration motion of 100 g. Results show that the major INS error sources, affecting the carrier phase tracking accuracy in high dynamics, include initial attitude errors, accelerometer scale factors, gyro noise and gyro g-sensitivity errors. The initial attitude errors are usually combined with the receiver acceleration to impact the tracking loop performance, which can easily cause the failure of carrier phase tracking. The main INS error factors vary with the vehicle motion direction and the relative position of the receiver and the satellites. The analysis results also indicate that the low-cost micro-electro mechanical system (MEMS) inertial measurement units (IMU) has the ability to maintain GNSS carrier phase tracking in high dynamics.

Performance Evaluation of a Real-Time Integrated MEMS IMU/GNSS Deeply Coupled System

Compared with loosely coupled system and tightly coupled system, deeply coupled system could enhance the accuracy and the robustness of the receiver and the whole system. The real-time integrated deeply coupled system based on MEMS IMU can provide technical support for navigation service. This paper proposes an integrated MEMS IMU/GNSS deeply coupled system framework whose processing core is DSP + FPGA. In this paper the IMU aided tracking loop is modeled first, and then the error of the tracking loop is analyzed, the design of system’s real-time is optimized. Tests results show that the system can operate consecutively in real-time conditions, and the IMU auxiliary information latency is less than 0.5 ms; the error of the tracking loop is greatly reduced; the dynamic tests results preliminarily verify the feasibility of the real-time integrated deeply coupled system.

Tracking Loop Model and Hardware Prototype Verification of GNSS/INS Deep Integration

There is no systematic and complete theoretical model for signal tracking loop of the GNSS/INS deep integration. And the performance of the deeply-coupled system based on hardware prototype hasn’t been fully verified. These limitations block the progress and application of the GNSS/INS deeply-coupled technology. This paper studies the GNSS/INS deeply-coupled technology based on the scalar deep integration for GPS L1 receiver. It establishes the transfer functions between the error sources (including thermal noise, oscillator phase noise, inertial measurement unit (IMU) error, the delay of Doppler aiding information) and the tracking loop error of the deep integration. And then the steady state tracking model is proposed and analyzed. A hardware/software integrated GNSS/INS scalar deep-coupled prototype is successfully developed, and real-time optimizations are made in terms of the system operation and aiding information delay. The performance of the designed deeply coupled prototype is fully evaluated based on a GPS/IMU hardware simulator and outdoor tests. The result shows that the INS aiding could improve the steady states tracking performance by extending the integration time to 20 ms and by compressing the bandwidth to 3 Hz under normal dynamic conditions. The proposed error models, designed methods, and hardware prototype developed in this paper can be further applied to the key performance study of the GNSS/INS deeply coupled system, such as the sensitivity and anti-interference under dynamic conditions.