【正文】 nd discussed.Filter algorithm The clockestimation algorithm is based on a Kalmanfilter,which can be used as a conventional Kalmanfilter as well as a forward/backwardfilter with smoother. The filterprocesses ionospherefree linear data binations of code and carrier phase measurements on the L1 and filter state includes the satellite clock error and the clock drift for the plete constellation of 32 satellites.The state vector additionally prises the receiver clock offset, a differential tropospheric zenith delay as well as the float carrier phase ambiguities of all satellites in view of each station. The station positions are extracted from recent IGS Sinexfiles (IGS 2008) and held fixed in the filter. The current GPS constellation has 32 active satellites and typical tracking network size for the filter is about 20 stations. Assuming that each station tracks on average 10 GPS satellites leads to a total number of about 300 elements in the state vector. Some of the state vector elements require further explanation: the estimated receiver clock offsets for the tracking stations do not represent the offset of the real receiver clocks, since the observation data has been preprocessed before being used in the filter. The pseudo range observations are used together with the a priori orbits and known station position to pute a coarse estimation of the receiver’s clock error. All observations and the measurement epoch are then corrected by the estimated clockoffset. This preprocessing reduces large clock jumps in the order of milliseconds to less then a microsecond and is beneficial for two reasons: first, the process noise for the receiver clocks can be reduced by several orders of magnitude,as ground station clock jumps do not have to be pensated for. It has been found that this procedure improved the filter stability during measurement , elimination during preprocessing eases the filter implementation in later filter steps, as no further measures are necessary for a consistent handling of the ground station clocks. In addition, individual process noise settings for each ground station are avoided, which would need to be maintained in case of changes in the station setup. The differential tropospheric zenith delay shall also be explained in further detail here. The model of the ionospherefree code and carrier phase observables already includes corrections for the tropospheric delay using a model of the standard atmosphere, which will be introduced later in this section. The true tropospheric delay will differ from the values provided by the empirical model,since the actual local weather conditions deviate from the model parameters. To pensate these deviations, a differential zenith path delay is estimated for each station,which is then mapped into a differential tropospheric slant delay, using an elevation dependent mapping function. Thecarrier phase ambiguities in the filter state are estimated as float values and are not fixed. In order to be able to perform the Kalmanfilter time update, the state vector must be predicted towards the next update epoch using a system model. For this algorithm,the GPS satellite clocks are predicted linearly in time. The clock drift and all other state parameters are assumed to be constant. Of course, the satellite clock drift is not strictly constant but it undergoes slow variations are due to the characteristics of the individual satellite clocks and are driven by hardly predictable effects like thermal variations onboard the GPS satellites. Furthermore, the ground station clock offset and the differential tropospheric delay are subject to order to pensate the deviations of the system model from the truth, process noise is introduced on these elements of the state vector. Without process noise, the covariance of the state vector would decrease over time and as a result, the weight of the measurements during the filter update decreases, which leads to divergence of the filter.Figure 1 depicts a flowchart of the plete filter algorithm. At the beginning, the forward filter is coarse values from the IGS ultrarapid product are used as a priori values for the satellite clock offset and drift. All other elements of the state vector are set to the process noise for the filter state and the measurement noise are set during this step. The selection of the process noise and measurement noise determines whether the filter adds more weight to the propagated state based on the system model or to the actual measurements. That is, if the process noise is low pared to the measurement noise, the filter will rely more on the system model and will only gradually correct the filter state during the