| Navigation and location services have become the fastest growing emerging industries after the Internet.As the main application field of LBS,car navigation has continued to grow rapidly in recent years.Unmanned driving is the research direction of the future car.It is the current research frontier of science and technology.Hotspots,these are urgently needed to provide stable and reliable positioning results for GNSS receivers in urban environments.However,due to the occlusion of the building group,the elevated frame,the shade,etc.,the tracking performance of the traditional GNSS receiver is degraded,and the multipath interference causes poor positioning accuracy,which cannot provide stable lane-level positioning results.Therefore,this paper uses GNSS deep integration technology to improve receiver performance from the two aspects of baseband signal processing and positioning.At the baseband signal processing level,the parallel code phase acquisition algorithm is first used to reduce the capture time and combine the INS prior information to improve the capture performance.Secondly,the weak signal tracking technology-NonCoh-FFT in the unassisted tracking loop is proposed,which can track the Doppler frequency variation normally in the weak signal dynamic scene.Next,the scalar deep integration technology in urban environment is studied,and the open loop tracking technology is theoretically analyzed.The STIM300 is taken as an example for simulation.A new signal strength detection method,SNR-FFT,is proposed to verify the change of signal energy and adjust the loop strategy accordingly.Research on multi-source fusion assisted deep integration technology in urban environment,the INS baseband assisted Doppler error divergence is limited by the odometer information,and the test shows that the method is feasible.Finally,the implementation of vector deep integration in urban environment is given,and the loop tracking performance and influencing factors are analyzed.Firstly,the improvement from the observation and measurement is improved.The bit synchronization and frame synchronization techniques under weak signals are given to ensure accurate transmission time.For INS deep integration receivers,when there isINS auxiliary information,INS short-term accuracy can be used to improve the observation gross error.Secondly,the pseudorange difference technique is used to improve the positioning accuracy,and the observation weight is set by the INS information in the deep integration receiver.Finally,for urban in-vehicle navigation,vehicle constraints are used to limit the error divergence of the INS in the absence of GNSS observations.Finally,GNSS deep integration software receivers is designed and implemented.receiver performance testing from both baseband and positioning results.The baseband performance test first gives the unassisted information loop performance test,and compares the sensitivity performance and dynamic performance of the NonCoh-FFT and SFFT.Secondly,the performance test of deep integration tracking loop is given.Through simulation test,when some satellites are occluded,the open loop tracking Doppler error of deep integration loop will not diverge,the pseudorange error will reach80 m in 10 min;all satellites are occluded,the tracking error is affected by the INS error and the clock drift,and the divergence is faster.When all satellites are occluded,the open-loop error of the multi-source-assisted deep integration loop is mainly affected by the clock drift,and the influence of the INS error divergence is basically negligible.The positioning performance test is compared with the EVK-M8 U from three aspects:observation measurement,positioning result and multi-sensor fusion result.The overall performance and typical scene comparison results show that the GNSS deep integration software receiver performance is better than EVK-M8 U.Moreover,the positioning error of the multi-source fusion result of the deep integration receiver reaches 1.099 m,which achieves the lane-level accuracy. |
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