One Detection Algorithm for Radar Echo Signal Pulse Compression Processing

Pulse compression processing in radar signal processing serves as a critical step for enhancing range resolution and signal-to-noise ratio (SNR). After completing pulse compression, the selection of target detection algorithms directly impacts the system's ability to identify genuine targets. The mean-based algorithm is a common detection method, whose core principle involves distinguishing targets from noise in the signal by calculating the mean of local regions.

The implementation of this algorithm typically involves the following key steps: First, segment the pulse-compressed signal and compute the mean value for each segment. Second, compare these means against a predetermined threshold, classifying segments with mean values above the threshold as potential targets. Finally, perform further analysis (such as Constant False Alarm Rate processing) to verify the authenticity and precise location of targets.

The advantage of the mean-based algorithm lies in its relatively low computational complexity, making it suitable for radar systems with high real-time requirements. However, its detection performance depends heavily on appropriate threshold setting; therefore, in complex environments or under low SNR conditions, it may require optimization by integrating other detection methods.