Acquisition and loop control of ultra-tight INS/BeiDou integration system

Zeng, Qinghua; Meng, Qian; Liu, Jianye; Feng, Shaojun; Wang, Huanhao

doi:10.1016/j.ijleo.2016.06.009

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Therefore, the design of this kind of single regulating loop has a certain “inverse” effect on the tracking process of the satellite signal under special circumstances. In addition, the GPS tracking loop of this kind of structure is an open loop [ 35 , 36 ]. If the integrated navigation filter, for some reason, such as failure, cannot work normally, the entire system will be paralyzed, and the reliability of the system will reduce.…”

Section: Sins/gps Ultra-tightly Coupling Structure Based On a Doubmentioning

confidence: 99%

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Zhao

Yan

et al. 2018

Sensors

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In this paper, we propose a robust adaptive cubature Kalman filter (CKF) to deal with the problem of an inaccurately known system model and noise statistics. In order to overcome the kinematic model error, we introduce an adaptive factor to adjust the covariance matrix of state prediction, and process the influence introduced by dynamic disturbance error. Aiming at overcoming the abnormality error, we propose the robust estimation theory to adjust the CKF algorithm online. The proposed adaptive CKF can detect the degree of gross error and subsequently process it, so the influence produced by the abnormality error can be solved. The paper also studies a typical application system for the proposed method, which is the ultra-tightly coupled navigation system of a hypersonic vehicle. Highly dynamical scene experimental results show that the proposed method can effectively process errors aroused by the abnormality data and inaccurate model, and has better tracking performance than UKF and CKF tracking methods. Simultaneously, the proposed method is superior to the tracing method based on a single-modulating loop in the tracking performance. Thus, the stable and high-precision tracking for GPS satellite signals are preferably achieved and the applicability of the system is promoted under the circumstance of high dynamics and weak signals. The effectiveness of the proposed method is verified by a highly dynamical scene experiment.

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Section: Sins/gps Ultra-tightly Coupling Structure Based On a Doubmentioning

confidence: 99%

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Zhao

Yan

et al. 2018

Sensors

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“…Tahir et al used auxiliary information to narrow down the search cell range and use Bayesian techniques to test weak signals, greatly increasing the acquisition probability and reducing the acquisition time . Wang et al and Zeng et al used the positioning information of the Strapdown Inertial Navigation System (SINS) and the Global Positioning System (GPS) ephemeris to estimate the Doppler frequency, which narrowed the Doppler frequency search range, improved the acquisition accuracy, and shortened the locked signal reacquisition time …”

Section: Introductionmentioning

confidence: 99%

“…18 Wang et al and Zeng et al used the positioning information of the Strapdown Inertial Navigation System (SINS) and the Global Positioning System (GPS) ephemeris to estimate the Doppler frequency, which narrowed the Doppler frequency search range, improved the acquisition accuracy, and shortened the locked signal reacquisition time. 19,20 We use the inertial information of SINS to narrow down the Doppler frequency search range, and then apply CS twice to implement coarse and fine acquisition on the code phase, which not only reduces the amount of calculation and the number of correlators but also solves low SNR of the output signal after deterministic compression measurement. The following sections of this article are as follows.…”

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confidence: 99%

A SINS‐aided two‐step fast acquisition method for GNSS signal based on compressive sensing

Cheng

2019

Concurrency and Computation

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Summary The Global navigation satellite system (GNSS) multi‐mode compatible and multi‐frequency information not only improves information redundancy but also eliminates errors such as ionospheric delays to improve positioning performance. However, multi‐mode and multi‐frequency observation information increases the hardware overhead and computational complexity, especially the signal acquisition. In this contribution, according to the strong complementarity between GNSS and Strapdown inertial navigation system (SINS) and the sparsity of the GNSS signal spreading code in the autocorrelation domain, a SINS‐aided fast acquisition method for GNSS signals based on Compressive sensing (CS) is proposed. First, the inertia information of SINS narrows the Doppler frequency search range; then, the CS is applied to measure compressively the GNSS signal in two steps, ie, the first step is to narrow down the code phase search range, and the second step is to determine the code phase of the half chip accuracy. Finally, the proposed method and the other two typical acquisition methods are compared by the detection probability and the Mean acquisition time (MAT). Theoretical analysis and experimental results show that the proposed method can not only reduce the number of correlators and computational complexity but also improve the detection probability and reduce the MAT.

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“…Liu G. studied the relationship between MEMS (Micro Electro Mechanical Systems) IMU precision and tracking loop stability in 2013, and the bias stability of the IMUs was proposed from the angle of usability [ 2 ]. Zeng Q. studied the loop control scheme between loop measurements and INS navigation data in 2016, and two control schemes were derived which were proved to be correct [ 3 ]. In 2014, Kirkko-Jaakkola studied the jamming mitigation performance of a deeply coupled GNSS-INS system with a low-cost MEMS IMU and gave actual experimental results instead of simulations [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Parameter Self-Calibration Method for GNSS/INS Deeply Coupled Navigation Systems in Highly Dynamic Environments

Zang

Lai

Liu

et al. 2018

Sensors

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The GNSS/INS (Global Navigation Satellite System/Inertial Navigation System) navigation system has been widely discussed in recent years. Because of the unique INS-aided loop structure, the deeply coupled system performs very well in highly dynamic environments. In practice, vehicle maneuvering has a big influence on the performance of IMUs (Inertial Measurement Unit), and determining whether the selected IMUs and receiver parameters satisfy the loop dynamic requirement is still a critical problem for deeply coupled systems. Aiming at this, a new parameter self-calibration method based on the norm principle is proposed which explains the relationship between IMU precision and the velocity error of the system; the method will also provide a detailed solution to calculate the loop steady-state tracking error, so it will eventually make a judgment about the stability of the tracking loop under present system parameter settings. Lastly, a full digital simulation platform is set up, and the results of simulations show good agreement with the proposed method.

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Acquisition and loop control of ultra-tight INS/BeiDou integration system

Cited by 11 publications

References 8 publications

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System

A SINS‐aided two‐step fast acquisition method for GNSS signal based on compressive sensing

A Parameter Self-Calibration Method for GNSS/INS Deeply Coupled Navigation Systems in Highly Dynamic Environments

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