GNSS narrowband interference mitigation for IoT devices

Abstract

Global Navigation Satellite System (GNSS) has become a critical part for positioning in Internet-of-Things (IoT) applications. However, GNSS receivers are vulnerable to narrowband interference from unintentional or intentional sources, due to GNSS signals' low power levels on the receiver end. The increasing occurrence of GNSS interference necessitates the development of interference mitigation methods for IoT devices, which typically have strict power consumption and silicon area constraints.

This thesis evaluates applicable GNSS narrowband interference mitigation methods for IoT-constrained devices. Literature study is performed to present various methods from extensive research on the subject. Five mitigation methods were modeled and tested against various interference types in Python simulator. These mitigation methods are pulse blanking, least mean square (LMS) and recursive least square (RLS) adaptive filters, and Huber and consecutive mean excision (CME) frequency domain filters. Complexity analysis and possible implementation methods are analyzed for IoT suitability. Results showed that frequency domain methods achieved superior suppression across all narrowband interference scenarios but require significant computational resources. LMS filtering and pulse blanking provide feasible options for IoT device implementation considering their performance and computational requirements.