Technique PPP-RTK combines the advantages of both the Precise Point Positioning(PPP)and the Real-Time Kinematic(RTK)positioning.With the emergence of multi-frequency Global Navigation Satellite System(GNSS)observation...Technique PPP-RTK combines the advantages of both the Precise Point Positioning(PPP)and the Real-Time Kinematic(RTK)positioning.With the emergence of multi-frequency Global Navigation Satellite System(GNSS)observations,it is preferable to formulate PPP-RTK functional models based on original(undiferenced and uncombined)observations.While there exist many variants of the undiferenced and uncombined PPP–RTK models,a unifed theoretical framework needs developing to link these variants.In this contribution,we formulate a class of undiferenced and uncombined PPP-RTK functional models in a systematic way and cast them in a unifed framework.This framework classifes the models into a code-plus-phase category and a phase-only category.Each category covers a variety of measurement scenarios on the network side,ranging from small-,medium-to large-scale networks.For each scenario,special care has been taken of the distinct ionospheric constraints and the diference between Code Division Multiple Access(CDMA)and Frequency Division Multiple Access(FDMA)signals.The key to systematically formulating these models lies in how to deal with the rank defciency problems encountered.We opt for the Singularity-basis(S-basis)theory,giving rise to the full-rank observation equations in which the estimable parameters turn out to be the functions of original parameters and those selected as the S-basis.In the sequel,it becomes straightforward to derive for each scenario the user model as it,more or less,amounts to the single-receiver network model.Benefting from the presented theoretical framework,the relationships and diferences between various undiferenced and uncombined PPP-RTK models become clear,which can lead to the better use of these models in a specifc situation.展开更多
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.展开更多
基金This work was partially funded by the National Natural Science Foundation of China(Grant Nos.41774042,42174034)the Key Research and Development Plan of Hubei Province(Grant No.2020BHB014)+1 种基金the Scientifc Instrument Developing Project of the Chinese Academy of Sciences(Grant No.YJKYYQ20190063)The frst author is supported by the CAS Pioneer Hundred Talents Program。
文摘Technique PPP-RTK combines the advantages of both the Precise Point Positioning(PPP)and the Real-Time Kinematic(RTK)positioning.With the emergence of multi-frequency Global Navigation Satellite System(GNSS)observations,it is preferable to formulate PPP-RTK functional models based on original(undiferenced and uncombined)observations.While there exist many variants of the undiferenced and uncombined PPP–RTK models,a unifed theoretical framework needs developing to link these variants.In this contribution,we formulate a class of undiferenced and uncombined PPP-RTK functional models in a systematic way and cast them in a unifed framework.This framework classifes the models into a code-plus-phase category and a phase-only category.Each category covers a variety of measurement scenarios on the network side,ranging from small-,medium-to large-scale networks.For each scenario,special care has been taken of the distinct ionospheric constraints and the diference between Code Division Multiple Access(CDMA)and Frequency Division Multiple Access(FDMA)signals.The key to systematically formulating these models lies in how to deal with the rank defciency problems encountered.We opt for the Singularity-basis(S-basis)theory,giving rise to the full-rank observation equations in which the estimable parameters turn out to be the functions of original parameters and those selected as the S-basis.In the sequel,it becomes straightforward to derive for each scenario the user model as it,more or less,amounts to the single-receiver network model.Benefting from the presented theoretical framework,the relationships and diferences between various undiferenced and uncombined PPP-RTK models become clear,which can lead to the better use of these models in a specifc situation.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
