A Frequency Estimation Method Based on Swarm Intelligence and Digital Frequency Mixing

Estimating signal frequency is a popular area of research in the test instrument industry. This paper proposes an estimation algorithm based on swarm intelligence and digital frequency mixing technology. Firstly, an energy estimation algorithm of frequency mixing is proposed as the objective function and the problem is converted into an optimisation problem. Then, the objective function is solved using three swarm intelligence algorithms: the particle swarm algorithm, the simulated annealing algorithm and the tuna algorithm. Experimental results demonstrate the significant superiority of the proposed swarm-intelligence-based approach over traditional methods. Comprehensive evaluations focusing on computational accuracy, convergence speed and algorithm robustness (including sensitivity to parameter settings and noise) reveal that the proposed method achieves significantly higher precision than traditional methods. The convergence characteristics and robustness vary among the three tested swarm intelligence algorithms (PSO, SA and TSO). Further performance improvements are achievable through parameter optimisation.