In order to overcome the vulnerability of the global navigation satellite system (GNSS) and provide robust position, navigation and time (PNT) information in marine navigation, the autonomous positioning system based on ranging-mode Automatic Identification System (AIS) is presented in the paper. The principle of the AIS autonomous positioning system (AAPS) is investigated, including the position algorithm, the signal measurement technique, the geometric dilution of precision, the time synchronization technique and the additional secondary factor correction technique. In order to validate the proposed AAPS, a verification system has been established in the Xinghai sea region of Dalian (China). Static and dynamic positioning experiments are performed. The original function of the AIS in the AAPS is not influenced. The experimental results show that the positioning precision of the AAPS is better than 10 m in the area with good geometric dilution of precision (GDOP) by the additional secondary factor correction technology. This is the most economical solution for a land-based positioning system to complement the GNSS for the navigation safety of vessels sailing along coasts.

Localization technology is a critical element in obtaining spatial data for the Internet of Things (IoT) and represents one of the most promising development areas for the next generation of IoT. In this regard, this paper proposes a multi-feature-based outdoor fingerprint localization technique with accuracy enhancement for the cellular network. The fingerprint collection scenarios are carefully designed to include diverse urban environments and seasonal characteristics. Based on these scenarios, a new set of cellular network parameters is introduced as a multi-feature composition of fingerprint, resulting in marked improvements in localization accuracy. Furthermore, to alleviate the interfering information brought by multi-feature, an adaptive bistage feature processing (BFP) method is proposed. At the stage of location matching, a hybrid model combines deep learning and k-nearest neighbors (KNN) algorithms is implemented to enhance localization accuracy. At last, a unique error detection (UED) method is proposed to check the predicted real-time fingerprint position. Experimental results demonstrate that the proposed technique achieves a median localization error of about 8 m and an average localization error of 15.4 m in a complex urban outdoor environment, improving localization accuracy by 41.6% compared to other state-of-the-art fingerprint localization techniques. The proposed technique shows potential to be an effective alternative to outdoor IoT nodes utilizing other localization sensors.

A new position estimation method by using the signals from two automatic identification system (AIS) stations is proposed in this paper. The time of arrival (TOA) method is enhanced with the displacement correction, so that the vessel’s position can be determined even for the situation where it can receive the signals from only two AIS base stations. Its implementation scheme based on the mathematical model is presented. Furthermore, performance analysis is carried out to illustrate the relation between the positioning errors and the displacement vector provided by auxiliary sensors. Finally, the positioning method is verified and its performance is evaluated by simulation. The results show that the positioning accuracy is acceptable.

This paper investigates the positioning error using different positioning techniques for two-dimensional location system. Based on the measurement models of TOA with/without the time synchronization and TDOA techniques, the performance comparison analysis between TOA with/without the time synchronization and TDOA is presented respectively. The performance of TOA with the time synchronization and TDOA is influenced by the specific distribution of the reference nodes. Hence, it is hard to determine which one is better. The performance of TDOA is always superior to TOA without the time synchronization using the same reference nodes. These conclusions are finally illustrated by the simulation results presented in two scenarios.