# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 2 Innovative Positioning and Navigation Technologies
### DGNSS-Based Cooperative Positioning  
In the early noughties, along with an increasing interest towards the localisation technologies, Cooperative Positioning (CP) became very popular in positioning and navigation applied to robotics and automated multi-agent systems [36; 37].  
CP is a general concept that has been investigated in different fields such as in robotics and wireless networks but it rarely addressed GNSS applications [38; 39]. Indeed, GNSS have been always considered a complementary technology to cooperative approaches. In light of this, the problem in a sensorless GNSS-CP framework can be approached by means of known methodologies from such different application fields and the state-of-the-art has to be defined accordingly.  
Several cooperative algorithms have been designed and presented in the literature to solve for the position of multiple agents by exploiting centralised or distributed approaches through direct or indirect connectivity within multi-agent systems [38]. Inter-agent relative distances are typically exploited to support collaborative strategies by means of exteroceptive sensors (e.g., Ultra-Wide Band (UWB), Light Detection And Ranging (LiDAR)). Thanks to the recent disclosure of GNSS raw measurements in the mass market GNSS receivers (such as for the embedded chipsets in the smartphones), developers, and researchers can now access raw GNSS observables (e.g., code/phase raw pseudorange measurements and Doppler measurements) prior to their actual use in PVT computation [40; 41; 42]. Such data can be transmitted, synchronised, and combined among different asynchronous users to reciprocally improve the positioning performance, the mutual situational awareness, and to open a variety of new solutions for the monitoring of GNSS integrity within a network of users [43; 44]. As assessed within the HANSEL project and described in Section 4, the combination of GNSS raw pseudorange measurements can be exploited to determine the inter-agent distance (a.k.a.) _baseline length_ among ultra-low-cost receivers by taking advantage of the well-known cancellation of the common biases [18; 45]. Previous contributions highlighted the possibility to exploit differential measurements to improve accuracy and precision but few on-field tests have been documented. Among these, a cooperative framework to distribute differential corrections was designed and implemented in smartphones [18]. Bayesian estimation was applied to integrate inter-agent distances in [19] within a simulated framework. Similarly, in [46] a tight-integration scheme was presented to improve relative localisation in vehicular networks. A Maximum-Likelihood Estimation was exploited to achieve improved accuracy in [47] with promising results. Recent theoretical analysis have been presented in [48] regarding the theoretical limits of integrating DGNSS collaborative measurements despite their correlation to the available GNSS pseudorange. The proposed DGNSS-CP methodology is inspired by the aforementioned studies and for the first time it is implemented in a real-time-oriented proof-of-concept, based on ultra-low-cost embedded GNSS receivers.