Performance Comparison of Deep Integration and Tight Coupling

Lashley, Matthew; Bevly, David M.

doi:10.1002/navi.43

Cited by 20 publications

(10 citation statements)

References 9 publications

(26 reference statements)

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“…Then, these pseudorange and pseudorange rate residuals of all visible satellites are provided to the central navigation filter as the measurements needed to correct the position and velocity computed from an INS. Finally, the pseudoranges and pseudorange rates predicted from the corrected position and velocity by the LOS geometry algorithm are fed back to the local signal generators to adjust local replica signals [ 18 , 19 ].…”

Section: Ultra-tightly Integrated Gnss/ins Architecturementioning

confidence: 99%

“…This led many researchers to pay attention to the ultra-tight integration since it has better tracking and navigation performance than the standard receiver and the tight integration [ 7 , 8 ]. Nowadays, some successes have been achieved in the various investigations on ultra-tight integration, such as the demonstration of the anti-interference capacity [ 9 , 10 ], the design and implementation of the dual-mode GNSS/INS ultra-tight integration [ 11 ] and the (Micro Electro Mechanical System) MEMS ultra-tight integration [ 12 ]. Nevertheless, most previous research about ultra-tight integration was still mainly on the architecture and filter design of the ultra-tightly integrated GNSS/INS navigation system [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments

Qiu

Zhan

2013

Sensors

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Ultra-tight integration was first proposed by Abbott in 2003 with the purpose of integrating a global navigation satellite system (GNSS) and an inertial navigation system (INS). This technology can improve the tracking performances of a receiver by reconfiguring the tracking loops in GNSS-challenged environments. In this paper, the models of all error sources known to date in the phase lock loops (PLLs) of a standard receiver and an ultra-tightly integrated GNSS/INS receiver are built, respectively. Based on these models, the tracking performances of the two receivers are compared to verify the improvement due to the ultra-tight integration. Meanwhile, the PLL error distributions of the two receivers are also depicted to analyze the error changes of the tracking loops. These results show that the tracking error is significantly reduced in the ultra-tightly integrated GNSS/INS receiver since the receiver's dynamics are estimated and compensated by an INS. Moreover, the mathematical relationship between the tracking performances of the ultra-tightly integrated GNSS/INS receiver and the quality of the selected inertial measurement unit (IMU) is derived from the error models and proved by the error comparisons of four ultra-tightly integrated GNSS/INS receivers aided by different grade IMUs.

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Section: Ultra-tightly Integrated Gnss/ins Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments

Qiu

Zhan

2013

Sensors

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“…The most commonly cited benefits are its increased capabilities in harsh environments, e.g., low carrier-to-noise ratio (CNR) Pany and Eissfeller 2006), intermittent signal outages (Lashley and Bevly 2007;Zhao and Akos 2011;, and high dynamics ), due to the mutual aiding of the channels with respect to each other and a higher filtering gain to be used stably (Groves and Mather 2010). To further improve robustness and accuracy in poor environments, vector tracking can be easily integrated with an inertial navigation system (INS) by simply augmenting the navigation Kalman filter with appropriate INS-related states (Lashley and Bevly 2013;Luo et al 2012;Petovello and Lachapelle 2006). In recent years, with the increasing development of intelligent transportation systems and location-based services in urban canyon areas, vector tracking has received more attention.…”

Section: Introductionmentioning

confidence: 99%

Open-source MATLAB code for GPS vector tracking on a software-defined receiver

Hsu

2019

GPS Solut

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The research regarding Global Positioning System (GPS) vector tracking (VT), based on a software-defined receiver (SDR), has been increasing in recent years. The strengths of VT include its immunity to signal interference, its capability to mitigate multipath effects in urban areas, and its excellent performance in tracking signals under high-dynamic applications. We developed open source MATLAB code for GPS VT SDR to enable researchers and scientists to investigate its pros and cons in various applications and under various environments. To achieve this goal, we developed an "equivalent conventional tracking (CT)" SDR as a baseline to compare with VT. The GPS positioning estimator of this equivalent CT is based on an extended Kalman filter (EKF), which has exactly the same state, system and carrier measurement models and noise tuning method as VT. This baseline provides users with a tool to compare the performance of VT and CT on common ground. In addition, this MATLAB code is well-organized and easy to use. Users can quickly implement and evaluate their own newly developed baseband signal processing algorithms related to VT. The implementation of this VT code is described in detail. Finally, static and kinematic experiments were conducted in an urban and open-sky area, respectively, to show the usage and performance of the developed open source GPS VT SDR.

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“…& Lachapelle, G. investigated three different Kalman filter implementation options in both scalar-tracking mode and vector-tracking mode with particular consideration for carrier phase tracking [ 16 ]. The analyses and tests in [ 17 , 18 ] showed improved tracking and reacquisition performances of vector-based deep integration at the cost of increased computational loads. Babu, R. & Wang, J. noticed that, typically, the integration filter runs at a rate of 1 to 100 Hz, while the carrier tracking loop normally runs at about 1000 Hz [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking

Kou

Zhang

2016

Sensors

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By aiding GPS receiver tracking loops with INS estimates of signal dynamics, GPS/INS ultra-tight coupling can improve the navigation performance in challenging environments. Traditionally the INS data are injected into the loops once every loop update interval, which limits the levels of dynamics accommodated. This paper presents a sample-wise aiding method, which interpolates the aiding Doppler into each digital sample of the local signal to further eliminate the dynamic errors. The relationship between the tracking error and the aiding rate is derived analytically. Moreover, the effects of sample-wise aiding using linear and spline interpolations are simulated and compared with traditional aiding under different INS data update rates. Finally, extensive tests based on a digital IF (intermediate frequency) signal simulator and a software receiver validate the theoretical equations and demonstrate that the dynamic stress error can be significantly reduced by sample-wise aiding.

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Performance Comparison of Deep Integration and Tight Coupling

Cited by 20 publications

References 9 publications

Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments

Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments

Open-source MATLAB code for GPS vector tracking on a software-defined receiver

Sample-Wise Aiding in GPS/INS Ultra-Tight Integration for High-Dynamic, High-Precision Tracking

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