Multi-Frame Track-Before-Detect with Adaptive Number of Frame as Noise Level

Multi-frame Track-Before-Detect (MF-TBD) is a batch processing method used to enhance detection and tracking performance in low SNR environments.Unlike traditional filtering techniques, MF-TBD does not apply thresholding and instead uses all observed data to reduce the risk of target loss.By integrating observations across multiple frames, it leverages space-time correlations to improve detection robustness.However, as the number of frames increases, the computational cost grows exponentially due to the need to correlate data over a larger dataset, leading to inefficiencies.Especially in high SNR conditions, where fewer frames are sufficient for accurate detection.To address this, we propose an Adaptive MF-TBD framework that dynamically adjusts the number of frames based on SNR levels.