Abstract: To address the issue external noise interference on airborne transient electromagnetic detection signals, which significantly impacts the quality and reliability of measurement data, the utilization of independent component analysis (ICA) is proposed for signal processing. The ICA method separates the effective signal from the noisy signal in order to achieve denoising. Additionally, to overcome step size setting challenges, a chaotic particle swarm optimization algorithm (CPSO) is introduced into ICA (CPSO-ICA). This approach dynamically adjusts the ICA step size function in real time using particle swarm optimization algorithm and further optimizes global optimization of step size using standard particle swarm optimization algorithm. As a result, it accelerates convergence speed, reduces steady-state error, and enhances denoising effect of ICA algorithm. Experimental results with simulated signals and measured data demonstrate that CPSO-ICA algorithm not only ensures deno effectiveness but also fully retains original signal features while providing reliable and effective airborne transient electromagnetic data for subsequent inversion.