Adaptive Multi-Source Signal Fusion Algorithm for Enhanced Digital Signal Processing in Heterogeneous Systems

In practical applications, multi-source systems such as radar, cameras, and infrared sensors provide diverse and complementary signal information. However, traditional Digital Signal Processing (DSP) algorithms often fail to fully exploit the synergy among these heterogeneous sources, resulting in low information utilization and limited processing accuracy. To address this issue, this paper proposes an Adaptive Multi-source Signal Fusion Algorithm (AMSFA) designed for efficient digital signal processing in multi-source systems. AMSFA is a lightweight, non-neural model-based approach that combines optimization-driven weight computation and rule-based temporal alignment. AMSFA dynamically adjusts the fusion weights of each signal source based on quality indicators such as Signal-to-Noise Ratio (SNR) and inter-source correlation, enabling adaptive and collaborative signal integration. The proposed method incorporates a weighted dynamic update mechanism and a temporal alignment module to enhance robustness and real-time performance in complex environments. Experimental results on simulated multi-source signals and publicly available multi-modal datasets demonstrate that AMSFA significantly outperforms conventional methods in signal enhancement, target recognition, and interference suppression. This work offers a novel and effective solution for signal fusion in multi-source systems and holds promising potential for real-world deployment.