At the signal reception level, the terminal needs to be equipped with antennas and RF front-ends that support multiple frequency bands. Modern satellite navigation systems (such as GPS L1/L2/L5, BeiDou B1/B2/B3, Galileo E1/E5, etc.) have signal frequencies dispersed across multiple frequency bands. The antenna needs to have wide bandwidth coverage while suppressing out-of-band interference. The RF front-end needs to perform multi-path parallel down-conversion to convert RF signals from different frequency points into intermediate frequency or baseband signals, providing clear raw data for subsequent processing. For example, some high-end terminals use four-element or seven-element antennas, combined with beamforming technology, to further improve the receiving sensitivity and multipath resistance of multi-frequency signals.
Frequency separation and parallel processing are crucial for multi-frequency processing. The terminal needs to separate the received mixed signals in the frequency or time domain to extract independent observations for each frequency point. Frequency domain separation is typically achieved through Fast Fourier Transform (FFT), converting the time-domain signal into a frequency-domain spectrum, and then extracting the target frequency point using a filter bank. Time-domain separation relies on digital down-conversion and matched filtering to directly demodulate the pseudocode and carrier at each frequency point. The separated signals need to be input in parallel to multiple independent processing channels. Each channel is designed with a signal tracking loop (such as a carrier loop or code loop) for a specific frequency point to maintain stable tracking of the satellite signal. For example, in complex electromagnetic environments, if tracking is lost due to interference at one frequency point, other frequencies can still maintain positioning, ensuring the robustness of the terminal.
Error correction and multi-frequency combination are the core advantages of multi-frequency processing. Ionospheric delay is one of the main error sources affecting positioning accuracy, and its magnitude is inversely proportional to the signal frequency. By simultaneously observing signals at two or more frequency points, the terminal can construct an ionosphere-free combination, eliminating first-order ionospheric errors and significantly improving positioning stability. Furthermore, multi-frequency observations can enhance geometric observability, reduce the correlation between clock bias and orbital error, and further reduce noise amplification effects and improve the efficiency of carrier phase ambiguity resolution through linear combinations such as wide/narrow lane combinations and dual/triple frequency triangular combinations. For example, in short baseline positioning, multi-frequency ambiguity resolution methods can provide centimeter-level positioning results in real time without initialization, achieving the rapid performance of RTK measurements.
Multi-system collaboration is an extension of multi-frequency processing. Modern terminals typically support parallel processing of signals from multiple systems such as GPS, BeiDou, Galileo, and GLONASS. By introducing more satellite observations, spatial geometry is optimized, and the upper limit of variance in positioning solutions is reduced. Multi-system collaboration needs to address issues such as clock consistency and unified observation equations. It requires cross-system multi-frequency observations to more effectively suppress the coupling effect of orbital residuals and clock bias, improve robustness to measurement noise, and shorten convergence time. For example, in dense urban environments, multi-system multi-frequency terminals can significantly increase the number of visible satellites, reduce signal interruptions caused by tall buildings, and improve positioning continuity.
A real-time processing framework is essential for the successful implementation of multi-frequency technology. The terminal requires a high-efficiency hardware architecture, including multi-core processors, FPGAs, or dedicated ASIC chips, to support synchronous acquisition, high-speed transmission, and parallel computing of multi-frequency signals. On the software side, dynamic models such as Kalman filtering or extended Kalman filtering are needed to continuously fuse multi-frequency observations over time, improving timing consistency. Simultaneously, challenges such as carrier phase noise and pseudorange observation drift are addressed through observation quality screening, outlier detection, and adaptive weight allocation to ensure the stability of multi-frequency processing.
Parallel processing of multi-frequency signals is the core path to improving the performance of the satellite navigation system terminal. Through multi-band antenna and RF front-end design, frequency separation and parallel computation, error correction and multi-frequency combination, multi-system collaboration, and optimization of the real-time processing framework, the terminal can fully utilize the information redundancy brought by the frequency dimension to achieve centimeter-level positioning accuracy, second-level convergence speed, and strong anti-interference capabilities, supporting the needs of high-end applications such as autonomous driving, drones, and precision agriculture.
