A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

Luo, Yuping; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing

doi:10.3390/s120709666

Cited by 10 publications

(3 citation statements)

References 9 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parkinson et al (1996) explained the structure of a vector delay lock loop and its advantages over the scalar delay lock loop. This idea has been used in combining information from both GPS and inertial navigation system (INS) systems (Groves and Mather, 2010; Luo et al, 2012; Hu et al, 2015; Li et al, 2016). In these methods, position and velocity information from INS can therefore be used to aid the tracking loops for removing dynamic stress from the GPS signal.…”

Section: Introductionmentioning

confidence: 99%

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

et al. 2020

View full text Add to dashboard Cite

Global satellite navigation systems (GNSS) are nowadays used in many applications. GNSS receivers experience limitations in receiving weak signals in a degraded environment. Hence, tracking weak GNSS signals is a topic of interest to researchers in this field. Different methods have been proposed to address this issue, each of which has advantages and disadvantages. In this paper, a method based on the vector tracking method is proposed for weak signal tracking. This method has been developed based on a strong Kalman filter instead of the extended Kalman filter used in conventional vector tracking methods. In order to adjust important parameters of this filter, the fuzzy method is used. The results of tests performed with both simulated data and real data demonstrate that the proposed method performs better than previous ones in weak signal tracking.

show abstract

Section: Introductionmentioning

confidence: 99%

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Using KF-based pre-filter to estimate tracking error has recently received considerable attention in the design of GNSS scalar tracking loop, vector tracking loop, and GNSS/INS (i.e., Inertial Navigation System) deep integration navigation system that is of federated structure [ 12 , 13 , 14 , 15 , 16 ]. Compared with the traditional discriminator, the pre-filter would provide more accurate tracking error estimation because it fully utilizes the smoothing effect of the system model and the statistical characteristics of observation noise.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers

Luo

Ding

Zhao

et al. 2017

Sensors

View full text Add to dashboard Cite

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz.

show abstract

“…Overall, the ultra-tight GPS/INS integration algorithms can be divided into two categories: centralized approach, 9,10 and federated approach. 11,12 In the former approach, the system exploits the inphase and quadra-phase integration results of early, prompt, and late components directly as measurements of the integrated filter, which is then used to estimate the inertial states. Considering six measurements from each tracking channel, 6 Â N measurements from N channels will be processed in the integrated filter, resulting in a large computational load for the centralized system.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments

Xie

Liu

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

The Doppler frequency changes rapidly due to high dynamics of vehicle, which leads to the loose lock and even the abnormal performance of global positioning system (GPS) receiver. To solve this problem, a federated ultra-tight integration algorithm based on pre-filters is proposed to optimal estimate both receiver tracking control commands and inertial navigation system (INS) navigation solutions. Firstly, the INS error model and GPS receiver tracking loop structure are built to present the fundamental architecture of the proposed ultra-tightly coupled system. Meanwhile, in order to reduce the load of the integrated filter, the pre-filters are incorporated to the ultra-tightly coupled system, and the state variables are fed into the integrated Kalman filter. Secondly, the intrinsic relevance between the phase and frequency biases of replica signals and INS states is analyzed to accomplish the deep fusion of INS and tracking loop. Finally, semi-physical simulations are performed by using a GPS signal simulator to generate signals of two high dynamic trajectories. The experimental results indicate that the proposed ultra-tight integration algorithm can achieve a good performance on reliable positioning and robust tracking in high dynamic environments, compared with the conventional approaches such as tightly coupled integration strategy and third-order phase-locked loops.

show abstract

A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

Cited by 10 publications

References 9 publications

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

Increasing the Resistance of GPS Receivers by Using a Fuzzy Smart Estimator in Weak Signal Conditions

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers

Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments

Contact Info

Product

Resources

About