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

机译:GNSS / INS深度集成的跟踪回路模型和硬件原型验证

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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 infor-mation) 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 state's 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.
机译:GNSS / INS深度集成的信号跟踪环路还没有系统且完整的理论模型。基于硬件原型的深层耦合系统的性能尚未得到充分验证。这些限制阻碍了GNSS / INS深层耦合技术的发展和应用。本文研究了基于标量深度集成的GPS L1接收机GNSS / INS深度耦合技术。它建立了误差源(包括热噪声,振荡器相位噪声,惯性测量单位(IMU)误差,多普勒辅助信息的延迟)与深度积分的跟踪环路误差之间的传递函数。然后提出并分析了稳态跟踪模型。成功开发了硬件/软件集成的GNSS / INS标量深度耦合原型,并在系统运行和辅助信息延迟方面进行了实时优化。基于GPS / IMU硬件模拟器和户外测试,对设计的深度耦合原型的性能进行了全面评估。结果表明,通过在正常动态条件下将积分时间延长至20 ms,并将带宽压缩至3 Hz,INS帮助可以提高稳态的跟踪性能。本文提出的错误模型,设计方法和硬件原型可以进一步应用于GNSS / INS深度耦合系统的关键性能研究,例如动态条件下的灵敏度和抗干扰性。

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