英语翻译Systematic errors are one of the most dominant factorsin

英语翻译
Systematic errors are one of the most dominant factors
inducing the failure of carrier phase-based high precision
baseline solutions and their stability.There are many
complicated factors causing systematic errors which are
impossible to eliminate completely,including ionospheric
and tropospheric errors,orbital errors,multipath and other
unmodelled biases.Many methods have been employed to
study the mitigation of the systematic errors for GPS
baseline processing.
Ionospheric and tropospheric error modelling has been
intensively
studies.
Satirapod
&
Prapod
(2005)
investigated different tropospheric models and their effect
on GPS baseline accuracy.Ionospheric error correction
improvements of differential GPS for long baselines are
presented in Mardian et al.(2003) and Hichem et al.
(2006).The orbit bias is a baseline length dependent bias
which can be minimized by Kalman filter modelling and
carrier phase-difference modelling (Colombo et al,1995;
Han & Rizos,1996).Multipath is another significant
systematic error source.Finite impulse response (FIR)
filters are tested with the limitation of dividing mixed
multipath errors with the same frequency band (Han &
Rizos,1997; Satirapod & Rizos,2005).Adaptive filter
extraction and elimination of multipath is influenced by
the difficulty in selecting the appropriate step-size
parameter and the filter length,as investigated by
Satirapod & Rizos (2005) and Ge et al.(2002).The
effects of other unmodelled biases can also be mitigated
to some extent with appropriate stochastic modelling.
Wavelet noise reduction modelling is one of the most
effective techniques for complex signal analysis.Recently
it has been introduced into the field of GPS data
processing for signal de-noising,outlier detection,bias
separation and data compression (Satirapod & Rizos,
2005; Collin & Warnant,1995; Ogaja et al.,2001),as
well as models introducing wavelets for multipath
analysis and mitigation for baseline solutions (Han &
Rizos,2000; Satirapod & Rizos,2005; Ge et al.,2002).

我自己整理的翻译哈：
Systematic errors are one of the most dominant factors
inducing the failure of carrier phase-based high precision
baseline solutions and their stability.
系统误差是诱导载波相位的高精度基线解以及稳定性失败的最主要因素.

There are many complicated factors causing systematic errors which are
impossible to eliminate completely, including ionospheric
and tropospheric errors, orbital errors, multipath and other
unmodelled biases.
有许多复杂的因素造成的不可能完全消除的系统误差,包括电离层和对流层误差,轨道误差,多径和其他未建模的偏见等

Many methods have been employed to study the mitigation of the systematic errors for GPS
baseline processing.
许多方法已被用来研究系统误差对GPS基线处理的缓解.

Ionospheric and tropospheric error modelling has been intensively studies.
电离层和对流层误差模型已被广泛的研究

Satirapod & Prapod(2005)
investigated different tropospheric models and their effect
on GPS baseline accuracy.
在GPS基线精度上研究了不同的对流层模型及其影响.

Ionospheric error correction improvements of differential GPS for long baselines are
presented in Mardian et al. (2003) and Hichem et al.(2006).
长基线GPS差分方法的电离层延迟误差修正改进是被马迪安等人（2003）和惠工等人（2006）提出的.

The orbit bias is a baseline length dependent bias
which can be minimized by Kalman filter modelling and
carrier phase-difference modelling (Colombo et al, 1995;
Han & Rizos, 1996).
轨道的偏差是可以通过卡尔曼滤波模型最小化和载波相位差分模拟的基线长度依赖性偏差（科伦坡等人,1995；汉族与利素斯,1996）.

Multipath is another significant systematic error source.
多径是另一个重要的系统误差源.

Finite impulse response (FIR) filters are tested with the limitation of dividing mixed multipath errors with the same frequency band (Han &
Rizos, 1997; Satirapod & Rizos, 2005).
有限脉冲响应（FIR）滤波是伴随着同频带分割混合多径误差的限制的测试（汉族与利素斯,1997；赛媞雷柏 和利素斯,2005）.

Adaptive filter extraction and elimination of multipath is influenced by
the difficulty in selecting the appropriate step-size
parameter and the filter length, as investigated by
Satirapod & Rizos (2005) and Ge et al. (2002).
自适应滤波器提取和多径的消除受到选择合适的步长参数和滤波器长度难度的影响
被赛媞雷柏和利素斯（2005）以及葛等人（2002）的研究调查.

The effects of other unmodelled biases can also be mitigated
to some extent with appropriate stochastic modelling.
其他未建模偏差的影响也可以减轻一些伴有合适的随机建模的范围.

Wavelet noise reduction modelling is one of the most
effective techniques for complex signal analysis.
小波降噪模型是一个复杂的信号分析的最有效的方法.

Recently it has been introduced into the field of GPS data
processing for signal de-noising, outlier detection, bias
separation and data compression (Satirapod & Rizos,
2005; Collin & Warnant, 1995; Ogaja et al., 2001), as
well as models introducing wavelets for multipath
analysis and mitigation for baseline solutions (Han &
Rizos, 2000; Satirapod & Rizos, 2005; Ge et al., 2002
最近它被引入到GPS数据处理领域给信号去噪,离群点检测,偏差分离和数据压缩（赛媞雷柏和利素斯,2005；科林-瓦尔南,1995；ogaja等人,2001）以及模型引入小波进行多路径分析和
基线解的缓解（汉族与利素斯,2000；赛媞雷d和利素斯,2005；葛等人,2002.

如有疑问可以提出哈