Cooperative Relative Positioning using Signals of Opportunity and Inertial and Visual Modalities


The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by GNSS and thus a combination of positioning and navigation approaches need to be explored. This work investigates how reliable relative positioning can be achieved in GNSS-challenged application scenarios using a combination of signals of opportunity (SOPs), as well as inertial and vision data. The proposed cooperative relative positioning system (CRPS) exploits this data for real-time positioning, and employs a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time without the use of any GNSS information. Experiments conducted in an outdoor setting demonstrate the applicability of the proposed CRPS and its performance against standalone positioning approaches including GNSS.


As vehicular technology advances towards higher levels of automation, a robust and precise navigation system is becoming a necessity for safe and effective operation. Currently, autonomous systems primarily rely on standalone or augmented GNSS for positioning but the GNSS signals frequently become unreliable  (e.g., in the presence of interference, jamming, or spoofing) or unavailable (e.g., in deep urban canyons ). In turn, navigation systems utilize various alternative approaches to obtain position estimates such as inertial data, and light and radar data. Data degradation and loss of signal due to multi-path and antenna obstruction shortfalls, have also led to the exploration of alternative methods for positioning (e.g., signals of opportunity, etc.). A broad classification of positioning techniques is presented in the figure below.

Signals of opportunity (SOPs) have been proposed as a very promising solution for positioning, especially in GNSS-challenged environments. SOPs (AM/FM radio, cellular, TV signals, etc.) are readily available in the surrounding environment and are received at high power. Hence, a number of previous works have investigated utilizing SOPs to address the shortfalls in GNSS-challenged environments, either by assuming a priori knowledge on the transmitters’ and receivers’ position, or by fusing SOP data with GNSS information to improve position accuracy.

Complementary to the aforementioned approaches, this work proposes the utilization of SOPs in combination with inertial and vision data for relative positioning and demonstrates how this can be achieved in a real outdoor environment without the use of any GNSS information.

Fig. 1: - 
Broad classification of positioning techniques.
Broad classification of positioning techniques.

Overall, the contributions of this work are summarized as follows:

  • An innovative relative positioning system is proposed without the need of any a priori information about the transmitters’ location or any GNSS information.
  • A frequency selection algorithm (FSA) is utilized to reduce the computational requirements due to the vast amount of received data obtained from scanning a large frequency spectrum.
  • A novel technique for accurate relative positioning extraction is proposed that fuses data obtained from SOPs, the inertial measurement unit (IMU) and a camera.
  • Experimental implementation and thorough evaluation and validationm of the proposed system in an outdoor environment, following a cooperative system configuration.

The rest of the paper is structured as follows. Related work is included in Section II and the description of the methodology utilized is described in Section IIISection IV elaborates on the proposed framework, while experimental results are presented in Section V. Finally, concluding remarks are presented in Section VI.

IEEE Publication