MATLAB Simulation Analysis of Digital Down Conversion Steps for LFM Signals

In the field of radar signal processing, Linear Frequency Modulated (LFM) signals are widely adopted due to their large bandwidth characteristics. Digital down conversion serves as a critical step for shifting high-frequency signals to baseband. This article employs MATLAB simulation to analyze the implementation logic of core modules:

NCO (Numerically Controlled Oscillator) Functions as the local carrier generator for down conversion, producing quadrature local oscillator signals through phase accumulators and lookup tables. Simulation requires attention to frequency resolution and phase truncation effects on mixing performance, with CORDIC algorithm implementations commonly used for optimization. MATLAB implementation typically involves calculating phase increments and using trigonometric functions or precomputed lookup tables.

Mixing and Frequency Spectrum Shifting After multiplying LFM signals with local oscillator signals, high-frequency components are converted near baseband. Simulations must monitor for mirror frequency components and verify whether the center frequency of mixed signals returns to zero. Code implementation involves complex multiplication between input signals and NCO outputs.

CIC (Cascaded Integrator Comb) Filter Used for anti-aliasing filtering during high-rate decimation, efficiently achieving downsampling through integrator-comb structures. Simulations require tuning decimation factors and cascade stages to balance passband flatness and stopband attenuation while preventing signal distortion. MATLAB's dsp.CICDecimator or similar functions facilitate proper parameter configuration.

FIR Compensation Filter CIC filters exhibit "sinc roll-off" in amplitude-frequency response, necessitating FIR filter design for passband attenuation compensation. Simulations utilize equiripple or least-squares methods to optimize FIR coefficients, with emphasis on verifying post-compensation amplitude-frequency characteristic flatness. Implementation involves firpm or firls functions for filter design and frequency response analysis.

Simulation results of this workflow visually demonstrate time-frequency domain changes at each processing stage, providing parameter design references for practical hardware implementations, including critical indicators like filter order and quantization bits.