TDOA based on 2-step weighting Chan method

The accurate management of dairy cow locations is critical for enhancing farm efficiency and economic returns in the age of agricultural automation. Traditional GPS systems often fall short in environments with dense vegetation or varied terrains due to signal multipath effects and occlusions. This paper presents an exploration of the Time Difference of Arrival (TDOA) positioning method, which measures the time differences between signals received at multiple base stations, thus overcoming the limitations of GPS. By applying the Chan algorithm, this method achieves low power consumption and real-time capabilities suitable for extensive farm monitoring. The paper discusses the fundamentals of TDOA, the conversion of time differences into positional data, and the challenges of signal path diversions and obstructions. We demonstrate the Chan algorithm’s two-step weighting process, which improves computational accuracy and noise resilience. A practical implementation is described, involving five receiving stations on a farm powered by solar panels and using Raspberry Pi and SDRplay RSP1a receivers. Various simulations of receiver distributions highlight the diamond-shaped layout as the most accurate for positioning. The study concludes with suggestions for enhancing TDOA systems through advanced signal processing and optimized receiver distribution.