The Global Navigation Satellite System (GNSS) system has a number of disadvantages such as weak signal strength and high cost, while signals of opportunity (SOPs) can make up for these shortcomings. Non-GNSS satellite SOPs offer some advantages in terms of high signal strength, low cost that belong to ground-based SOPs and better coverage than ground-based SOPs. We investigate a new method of instantaneous Doppler positioning with Iridium signals that are treated as satellite SOPs. Initially, the Iridium SOPs positioning system is introduced through a description of its characteristics and components. Next, the instantaneous Doppler positioning algorithm is introduced, and the effects of measurement errors and satellite orbital errors on the positioning solution are analyzed by a new method. Furthermore, the influence of constellation geometrical distribution on positioning performance is analyzed based on the relative position relationship between discrete distribution points of the receiver position. Finally, the simulation data are used for theoretical verification, and the real data experiment is implemented with IRIDIUM NEXT signals. The results show that two-dimensional (2-D) position accuracies of approximately 22 m (1σ) can be achieved with the height aiding when the static receiver has a complete view of the sky.

In order to get rid of the dependence of the navigation and positioning system on the global navigation satellite system (GNSS), radio, television, satellite, and other signals of opportunity (SOPs) can be used to achieve receiver positioning. The space-based SOPs based on satellites offer better coverage and availability than ground-based SOPs. Based on the related research of Iridium SOPs positioning in the open environment, this paper mainly focuses on the occluded environment and studies the Iridium SOPs positioning technique in weak signal environment. A new quadratic square accumulating instantaneous Doppler estimation algorithm (QSA-IDE) is proposed after analysing the orbit and signal characteristics of the Iridium satellite. The new method can improve the ability of the Iridium weak signal Doppler estimation. The theoretical analysis and positioning results based on real signal data show that the positioning based on Iridium SOPs can be realized in a weak signal environment. The research broadens the applicable environment of the Iridium SOPs positioning, thereby improving the availability and continuity of its positioning.

The performance of the integrated global navigation satellite system (GNSS) and inertial navigation system (INS) has been proven to be more accurate, reliable, and continuous than stand-alone systems. The development of micro-electronic mechanical system (MEMS) enables inertial measurement unit (IMU) to meet the low-cost and small-size requirements of vehicular navigation. However, the stochastic error characteristics of the inertial sensors and the instability caused by the GNSS signal outages pose a threat to the MEMS-based GNSS/INS land vehicle navigation system. Within this context, we propose the following two-step GNSS/INS integrated architecture at two levels: 1) enhancing the signal-to-noise ratio (SNR) of MEMS-INS raw measurements utilizing a hybrid denoising algorithm with wavelet transform and support vector machine (SVM) and 2) improving the positioning accuracy by a SVM-based data fusion approach which could predict the accumulated error of the MEMS inertial sensors during the GNSS outages. A rotation platform experiment and a field test were carried out, which suggests that the proposed method can effectively eliminate the stochastic errors of MEMS-IMU, and significantly improve the overall positioning accuracy in land vehicle navigation.

Global positioning system (GPS) technology is well suited for attitude determination. However, in land vehicle application, low-cost single frequency GPS receivers which have low measurement quality are often used, and external factors such as multipath and low satellite visibility in the densely built-up urban environment further degrade the quality of the GPS measurements. Due to the low-quality receivers used and the challenging urban environment, the success rate of the single epoch ambiguity resolution for dynamic attitude determination is usually quite low. In this paper, a micro-electro-mechanical system (MEMS)—inertial navigation system (INS)-aided ambiguity resolution method is proposed to improve the GPS attitude determination performance, which is particularly suitable for land vehicle attitude determination. First, the INS calculated baseline vector is augmented with the GPS carrier phase and code measurements. This improves the ambiguity dilution of precision (ADOP), resulting in better quality of the unconstrained float solution. Second, the undesirable float solutions caused by large measurement errors are further filtered and replaced using the INS-aided ambiguity function method (AFM). The fixed solutions are then obtained by the constrained least squares ambiguity decorrelation (CLAMBDA) algorithm. Finally, the GPS/MEMS-INS integration is realized by the use of a Kalman filter. Theoretical analysis of the ADOP is given and experimental results demonstrate that our proposed method can significantly improve the quality of the float ambiguity solution, leading to high success rate and better accuracy of attitude determination.

How to effectively blend global navigation satellite system (GNSS) and inertial navigation system (INS) data to achieve an optimal solution in harsh environments has always been an urgent task. The main challenges for low-cost GNSS/INS integrated land vehicle navigation system are poor accuracy of GNSS observations in complex urban environments and time-growing position errors of stand-alone micro-electro mechanical system (MEMS)-based inertial sensors during GNSS outages. This paper aims to enhance the positioning accuracy and reliability of low-cost integrated system. To attain this, we propose a two-tier robust fusion scheme with different aspects: 1) The GNSS and INS information are fused through a support vector regression-based adapted Kalman filter (SVR-AKF), with which scaling factors are generated to tune the covariance parameters of KF. 2) The position errors of MEMS-INS during GNSS outages are predicted and compensated by modeling INS error characteristics utilizing an adaptive neuro fuzzy inference system (ANFIS) due to its effectiveness in dealing with the nonlinear and uncertainty problems. To verify the feasibility of the proposed methodology, experimental road tests were performed, which suggested that the proposed methodology can significantly improve the overall reliability and positioning performance of land vehicle navigation in GNSS-challenged environments.

Floor localization plays a crucial role in multi-storey indoor positioning systems. Wi-Fi enabled system and barometer-based system are two well-known systems in floor localization. However, a practical floor localization system should consider the absence of infrastructure, the pretrained database and the influence of complex environment. Inertial sensors-based systems realize floor localization by classifying pedestrian’s motion states, which can avoid the above problems. Nevertheless, the accuracy is limited because of the stochastic noise introduced by device measurement. Besides, only single acceleration feature is utilized in most of the existing systems. Fusion of multiple inertial sensors and other signals such as frequency-modulated (FM) radio signals is a feasible solution considering both practicality and accuracy. In this paper, a multi-feature floor localization algorithm using MEMS inertial sensors and a FM radio receiver is designed. Firstly, based on the MEMS inertial sensors and FM radio signals, we select features which are not affected by types or sites of stairs for the classification of plane walking, going upstairs, and going downstairs moments. Secondly, random forest classifiers are introduced to obtain accurate classification results by combining the above features. Floor is located on the basis of classification results. Field experiments have been conducted at four sites in two office buildings to evaluate the performance of the proposed algorithm. The results demonstrate that the multi-feature approach achieves 96.9% floor localization accuracy, which is 38.4% higher than the method using only single acceleration feature and 24.3% higher than the multi-feature algorithm without FM features.