Parameter Extraction Techniques for Biphase-Coded Radar Pulse Signals

Parameter extraction for biphase-coded radar pulse signals represents a critical challenge in signal processing and feature analysis. This process requires accurate identification of key signal characteristics in noisy environments, particularly the recovery of frequency parameters. During the signal processing stage, noise interference must be addressed first. Since radar signals in practical environments are often contaminated by various noise types, appropriate filtering or noise suppression techniques are essential prior to parameter extraction. Common processing methods include matched filtering and wavelet denoising, which can be implemented using functions like xcorr() for cross-correlation in MATLAB or wavedec() for wavelet decomposition. These techniques significantly improve the signal-to-noise ratio, laying the foundation for subsequent analysis. For biphase-coded signals with phase modulation characteristics, frequency extraction must account for their modulation properties. Unlike simple continuous waves, biphase-coded signals exhibit phase transitions according to specific patterns, requiring frequency estimation methods that can distinguish between carrier frequency and phase changes induced by coding. Common demodulation approaches include instantaneous frequency analysis using Hilbert transforms (hilbert() function) and phase difference methods implemented through angle() and diff() operations. These techniques capture phase variation patterns to indirectly derive baseband frequency components. The final accuracy of parameter extraction depends heavily on the match between signal models and noise characteristics. System design must balance computational complexity with estimation accuracy, selecting algorithms suitable for real-time processing. Modern radar systems often implement these processing pipelines on FPGA or DSP hardware using optimized code structures with parallel processing capabilities to meet real-time requirements. This technology holds practical value for electronic reconnaissance and radar countermeasures, with the core challenge being maintaining parameter estimation robustness under low signal-to-noise ratio conditions. Algorithm implementations typically incorporate adaptive thresholding and statistical validation checks to enhance reliability in challenging environments.