MATLAB Simulation of Peak Detection Combined with CA-CFAR Detector

The detector combining peak detection with CA-CFAR plays a crucial role in radar signal processing, primarily used to effectively extract useful signals from noisy backgrounds. This detector integrates local peak extraction with Constant False Alarm Rate (CFAR) technology, significantly improving detection accuracy and stability.

Peak Detection The core task of peak detection is to identify local maximum points within a signal. This is typically implemented using a sliding window comparison method: for each data point, check whether it represents the maximum value within a specified neighborhood. The advantage of this approach lies in its computational simplicity, but when used alone, it becomes susceptible to noise interference, leading to false alarms. In MATLAB implementation, this can be achieved using functions like findpeaks() with appropriate parameter settings or by creating custom sliding window algorithms using array operations and comparison functions.

CA-CFAR Technique CA-CFAR (Cell Averaging Constant False Alarm Rate) is an adaptive threshold algorithm that dynamically adjusts detection thresholds based on background noise levels. Its principle involves averaging the reference cells surrounding the cell under test and multiplying by a scaling factor to generate the threshold. This process effectively suppresses false alarms caused by noise fluctuations, ensuring stable detector performance in complex environments. MATLAB implementation typically involves creating reference windows, calculating noise estimates using mean() functions, and applying scaling factors to set adaptive thresholds.

Combined Advantages Integrating peak detection with CA-CFAR involves first performing preliminary screening of candidate signals through peak detection, followed by secondary verification of these candidate points using CA-CFAR. This two-stage processing mechanism preserves the efficiency of peak detection while eliminating false peaks caused by noise through CFAR technology, substantially improving the system's detection accuracy. The implementation requires careful sequencing of operations and proper data handling between detection stages.

MATLAB Simulation Key Points When implementing this detector in MATLAB, several aspects require attention: The window size for peak detection needs adjustment according to signal characteristics - too large may miss details, while too small increases noise sensitivity. The number of reference cells and scaling factor in CA-CFAR directly affect false alarm probability, requiring optimization through Monte Carlo simulations. Additionally, simulations can inject noise and interference signals of varying intensities to verify detector robustness. Key implementation considerations include proper array indexing for window operations, efficient memory management for large datasets, and statistical analysis functions for performance evaluation.

This combined detector finds widespread applications in radar, sonar, and other fields, particularly suitable for target detection tasks under low signal-to-noise ratio conditions. Through MATLAB simulation, developers can intuitively analyze algorithm performance and rapidly iterate design improvements using MATLAB's visualization tools and comprehensive signal processing toolbox functions.