PRN Sequence Estimation with a Self-Calibrating 40-Element Antenna Array

Abstract

This work explores the use of a low-cost global navigation satellite system (GNSS) antenna array including front-ends and a global navigation satellite system (GNSS) software receiver to receive signals of opportunity (SoO) whose pseudorandom noise (PRN) code is unknown. The front-ends are only loosely synchronized in time and frequency via hardware elements, and precise synchronization or calibration is achieved by using open service global navigation satellite system (GNSS) signals. After calibration, the raw received signals from all antenna elements are added coherently, which allows the pseudorandom noise (PRN) codes of the unknown signals of opportunity (SoO) to be estimated. The pseudorandom noise (PRN) sequences are then fed into a test receiver with a single antenna element that uses the sequences to acquire and track the signals of opportunity (SoO) in a conventional way. The process of chip estimation combined with the use of these sequences in a test receiver is called blind processing. The paper discusses the used algorithms, limitations, the expected performance in the chip error rate (CER), and effective loss of signal power when tracking the signals of opportunity (SoO) in a test receiver. An experimental setup with an array of 40 antenna elements is described, and results from simulated data and from one real global positioning system (GPS) M-code signal used as the signals of opportunity (SoO) show the feasibility of this concept. Among the types of global navigation satellite system (GNSS) signals of opportunity (SoO), the GPS M-code is more difficult to estimate than its Galileo or BeiDou counterparts due to its high chipping rate. A chip error rate (CER) of 15.1 % is achieved for the M-code signal. Applications of blind processing include receiver prototyping, signal quality monitoring of the signals of opportunity (SoO), and server-side processing for the purpose of signal authentication.