共查询到20条相似文献，搜索用时 578 毫秒
1.
针对北斗B1C信号在低载噪比情况下跟踪精度低的问题，提出一种基于扩展卡尔曼滤波（EKF）的北斗B1C信号数据/导频联合跟踪方法.通过构建数据/导频双通道联合跟踪模型，增加对B1C信号利用率，并在联合跟踪模型的基础上引入扩展卡尔曼滤波器，削弱传统跟踪环路中鉴别器和环路滤波器带来的跟踪误差，进一步提高跟踪环路对低载噪比信号的跟踪性能.仿真结果验证:在低载噪比情况下，相比于传统单导频通道跟踪、单导频扩展卡尔曼跟踪和联合跟踪，该方法可以有效提高跟踪精度. 相似文献
2.
3.
Compared with the traditional GPS L1 C/A BPSK-R(1) signal, wideband global navigation satellite system (GNSS) signals suffer more severe distortion due to ionospheric dispersion. Ionospheric dispersion inevitably introduces additional errors in pseudorange and carrier phase observations that cannot be readily eliminated by traditional methods. Researchers have reported power losses, waveform ripples, correlation peak asymmetries, and carrier phase shifts caused by ionospheric dispersion. We analyze the code tracking bias induced by ionospheric dispersion and propose an efficient all-pass filter to compensate the corresponding nonlinear group delay over the signal bandwidth. The filter is constructed in a cascaded biquad form based on the estimated total electron content (TEC). The effects of TEC accuracy, filter order, and fraction parameter on the filter fitting error are explored. Taking the AltBOC(15,10) signal as an example, we compare the time domain signal waveforms, correlation peaks, code tracking biases, and carrier phase biases with and without this all-pass filter and demonstrate that the proposed delay-equalization all-pass filter is a potential solution to ionospheric dispersion compensation and mitigation of observation biases for wideband GNSS signals. 相似文献
4.
Xin-Xiang Jin 《Journal of Geodesy》1996,70(11):673-680
This paper presents a new algorithm for generating differential GPS corrections. This algorithm directly uses code and carrier observations in the measurement model of a Kalman filter, so that it is possible to use a simple stochastic observation model and to use the standard algorithm of the Kalman filter. The algorithm accounts for biases like multipath errors and instrumental delays in code observations and it shows how differential corrections are differently affected by code biases when dual or single frequency data are used. 相似文献
5.
The architecture of the ultra-tight GPS/INS/PL integration is the key to its successful performance; the main feature of this
architecture is the Doppler feedback to the GPS receiver tracking loops. This Doppler derived from INS, when integrated with
the carrier tracking loops, removes the Doppler due to vehicle dynamics from the GPS/PL signal thereby achieving a significant
reduction in the carrier tracking loop bandwidth. The bandwidth reduction provides several advantages such as: improvement
in anti-jamming performance, and increase in post correlated signal strength which in turn increases the dynamic range and
accuracy of measurements. Therefore, any degradation in the derived Doppler estimates will directly affect the tracking loop
bandwidth and hence its performance. The quadrature signals from the receiver correlator, I (in-phase) and Q (quadrature),
form the measurements, whereas the inertial sensor errors, position, velocity and attitude errors form the states of the complementary
Kalman filter. To specify a reliable measurement model of the filter for this type of integrated system, a good understanding
of GPS/PL signal characteristics is essential. It is shown in this paper that phase and frequency errors are the variables
that relate the measurements and the states in the Kalman filter. The main focus of this paper is to establish the fundamental
mathematical relationships that form the measurement model, and to show explicitly how the system error states are related
to the GPS/PL signals. The derived mathematical relationships encapsulated in a Kalman filter, are tested by simulation and
shown to be valid.
相似文献
| Ravindra Babu (Corresponding author)Email: |
| Jinling WangEmail: |
6.
Triple Differencing with Kalman Filtering: Making It Work 总被引：4，自引：0，他引：4
Since global positioning system (GPS) measurements are ranges (code) and biased ranges (carrier), it seems natural to model
them as ranges and determine the biases. This is particularly compelling since the double-difference range biases turn out
to be integers. At some level there is also an elegance, perhaps therefore a naturalness, to modeling the carrier measurements
as time differences of double differences. While something is lost something else is gained. Here we apply the proven delayed-state
Kalman filter to processing carrier phase measurements as triple differences. In practice we process these triple differences
along with double-difference code measurements. We also treat the measurement error as, mostly, Gauss-Markov states to be
determined. Many of the details are discussed and experimental results are included. These demonstrate that excellent performance
can be obtained if the Kalman filter modeling is done carefully. ? 2000 John Wiley & Sons, Inc. 相似文献
7.
JIN Xinxiang 《地球空间信息科学学报》2000,3(2):65-73,78
1 ＩｎｔｒｏｄｕｃｔｉｏｎＣｕｒｒｅｎｔｌｙ ,ｔｈｅｒｅａｌｒｅａｄｙｅｘｉｓｔｓｅｖｅｒａｌａｌｇｏｒｉｔｈｍｓｆｏｒｔｈｅｇｅｎｅｒａｔｉｏｎｏｆｄｉｆｆｅｒｅｎｔｉａｌｃｏｒｒｅｃｔｉｏｎｓ,ｆｏｒｉｎ ｓｔａｎｃｅ ,ｔｈｅａｌｇｏｒｉｔｈｍｂａｓｅｄｏｎｃａｒｒｉｅｒｆｉｌｔｅｒｅｄｃｏｄｅｏｂｓｅｒｖａｔｉｏｎｓ (ｖａｎＤｉｅｒｅｎｄｏｎｃｋ ,1 993 ;Ｌａｎｄａｕ ,1 993 )ａｎｄｔｈｅａｌｇｏｒｉｔｈｍｂａｓｅｄｏｎｃｏｄｅｏｂｓｅｒｖａｔｉｏｎｓａｎｄｓｅｑｕｅｎｔｉａｌｄｉｆｆｅｒｅ… 相似文献
8.
Jin Xinxiang 《地球空间信息科学学报》2013,16(2):65-73
The DGPS technique can provide considerably better relative positioning accuracy than the stand-alone GPS positioning, but the improvement depends on the distance between the user and the reference station (spatial correlation), the latency of differential corrections (temporal correlation), and the quality of differential corrections. Therefore, how to correctly generate differential corrections as well as their pricision is very important to the DGPS positioning technique. This paper presents a new algorithm for generating differential GPS corrections. This algorithm directly uses code and carrier observations in the measurement model of a Kalman filter, so that it is possible to use a simple stochastic model and to use the standard algorithm of the Kalman filter. The algorithm accounts for biases like multipath errors and instrumental delays in code observations and it shows how differential corrections are differently affected by code biases when dual or single frequency data is used. In addition, the algorithm can be integrated with a real time quality control procedure. As a result, the quality of differential corrections can be guaranteed with a certain probability. 相似文献
9.
Information on trajectory and attitude is essential for analyzing gravimetric data collected on kinematic platforms. Usually, a Kalman filter is used to obtain high-accuracy positional and velocity information. However, this can be affected by measurement outliers and by state disturbances that occur frequently under a fast-changing environment. To overcome these problems, a robust adaptive Kalman filtering algorithm is applied for state estimates, which introduces an equivalent weight to resist measurement outliers and an optimal adaptive factor to balance the contributions of the kinematic model information and the measurements. In addition to the conventional robust estimator, an improved Current Statistical (CS) model is proposed. The improved CS model adopts a variance adaptive learning algorithm, and it can perform self-adaptation of acceleration variance with the innovation information; thus, it can overcome the shortcoming of lower tracking accuracy and avoid setting the maximum acceleration. Following a gravimetry campaign on the Baltic Sea, it is shown in theory and in practice that the robust adaptive Kalman filter is not only simple in its calculation but also more reliable in controlling the colored observation noise and kinematic state disturbance compared with the classical Kalman filter. The improved CS model performs best, especially when analyzing the positioning errors at the turns due to the target maneuvering. Compared to the CS model, the RMS values of the positional estimates derived from the improved CS model decrease by almost 30% in the horizontal direction, and no significant improvement in the vertical direction is found. 相似文献
10.
11.
GPS结构振动监测数据滤波方法及其性能实验研究 总被引：2，自引：0，他引：2
使用GPS监测结构振动的目的在于提取振动信号特征,但GPS观测量受多种误差源的影响,因此,选用合理的数据处理方法有效地分离各误差项,对于提高GPS的监测精度具有重要意义。将Vondrak滤波、小波滤波、自适应FIR滤波和卡尔曼滤波等四种方法应用于资料序列中振动信号的分离,通过对模拟振动实验观测资料的分析表明:运用滤波法可提高GPS测量微小动态变形和变频振动信号的检测能力;4种滤波法均能有效地提高GPS监测结构振动的精度,其中,Vondrak滤波和小波滤波的性能相当,且优于自适应FIR和卡尔曼滤波。同时,在对各滤波法参数选择的优缺点进行分析的基础上,提出不同情况下选择滤波器的建议。 相似文献
12.
多路径误差为一时空环境效应,难以构建准确数学模型消除其影响,且该误差在基线两端不具有空间相关性,运用现有差分技术也无法很好消除,是高精度短基线测量中主要误差之一．为进一步削弱多路径误差,本文以监测站坐标时间序列中多路径误差为研究对象,根据多路径误差在历元间的时变特性,建立多路径误差状态空间模型,采用标准卡尔曼滤波和顾及有色噪声的卡尔曼滤波从监测站第一天双差固定解坐标残差序列中估计多路径误差改正序列,并根据多路径误差的周日重复特性,利用第一天得到的多路径误差改正序列对之后各天坐标序列进行改正．最后通过实验分析,得出顾及有色噪声的卡尔曼滤波估计方法优于标准卡尔曼滤波的结论．研究方法对提高GNSS定位精度具有重要实用价值． 相似文献
13.
针对实际环境中量测噪声易被野值污染而呈现非高斯分布,进而导致传统卡尔曼滤波(KF)算法性能降低的问题,提出了最大熵卡尔曼滤波(MCKF)算法. 该算法基于最大熵准则(MCC)和M估计的思想推导得到. 与KF相比,所提算法能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,与基于Huber函数的卡尔曼滤波(HKF)算法相比,其能够更有效地利用量测信息,因此所提算法相比于KF和HKF而言更加鲁棒. 在全球卫星导航系统(GNSS)与惯性导航系统(INS)的紧组合模式下进行车载实测实验,由于GNSS的伪距与伪距率等原始量测信息质量不佳,因此KF和HKF的性能均受到影响,而所提MCKF算法能够有效地抑制异常量测值的影响,能够更快地收敛且得到更高的估计精度. 相似文献
14.
15.
A dual-rate Kalman Filter (DRKF) has been developed to integrate the time-differenced GPS carrier phases and the GPS pseudoranges with INS measurements. The time-differenced GPS carrier phases, which have low noise and millimeter measurement precision, are integrated with INS measurements using a Kalman Filter with high update rates to improve the performance of the integrated system. Since the time-differenced GPS carrier phases are only relative measurements, when integrated with INS, the position error of the integrated system will accumulate over time. Therefore, the GPS pseudoranges are also incorporated into the integrated system using a Kalman Filter with a low update rate to control the accumulation of system errors. Experimental tests have shown that this design, compared to a conventional design using a single Kalman Filter, reduces the coasting error by two-thirds for a medium coasting time of 30?s, and the position, velocity, and attitude errors by at least one-half for a 45-min field navigation experiment. 相似文献
16.
北斗伪距观测值存在特有的多路径系统性偏差,偏差的数量级达到几个分米到米。该系统偏差可分为两类:一类是IGSO/MEO卫星随高度角变化的伪距系统性偏差;另一类是GEO卫星(高度角仅微小变化)明显的伪距系统性偏差。系统性的伪距偏差导致GEO卫星MP序列的标准偏差较大,本文针对GEO卫星伪距偏差问题提出了一种基于卡尔曼滤波的修正方法,修正后的GEO卫星MP序列的标准偏差下降了10%~16%。基于伪距相位组合的单频PPP技术的伪距权重较大,会受到北斗伪距偏差的影响,分析表明该系统性偏差将导致单频PPP定位结果高程方向产生约1m的偏差。对GEO伪距偏差采用提出的卡尔曼滤波修正方法进行修正,并应用Wanninger和Beer的高度角模型消除IGSO/MEO观测值伪距偏差,本文对修正后的单频精密单点定位精度进行了分析。4个multi-GNSS experiment(MGEX)站10d观测数据的分析结果表明:仅改正和卫星多路径误差,高程方向定位结果精度可改善65%左右;采用本文方法对GEO卫星的多路径修正后,该方向定位结果精度改善比例将进一步提高至75%左右。 相似文献
17.
A Kalman filter-based method combining the energy of both L1 C/A and L2C GPS signals in a combined tracking loop method to
enhance performance under adverse conditions is developed. Standard tracking methods and the ionospheric effect on GPS signals
are reviewed and compared to a new Kalman filter that simultaneously estimates delay, phase and total electron content by
combining L1 C/A and L2C code and phase discriminator outputs. The new filter is tested and compared to standard methods for
tracking L1 C/A and L2C using both simulated and real data. The new method is found to have improved sensitivity of 3 dB compared
to standard L1 tracking and 4.5 dB compared to standard L2C tracking while at the same time providing an accurate estimate
of the total electron content along the signal path. 相似文献
18.
19.