Li Jian - Chapter 7: MIMO Radar Signal Processing

MIMO radar signal processing constitutes an advanced and complex domain that leverages multiple-input multiple-output technology to enhance radar system performance. Chapter 7, "Slow-Time MIMO Space-Time Adaptive Processing," provides an in-depth exploration of MIMO radar modeling and processing techniques in the slow-time dimension, offering readers a comprehensive perspective from theoretical foundations to practical implementation.

Overview MIMO radar enhances target detection capabilities through spatial diversity while mitigating target fading effects. This chapter introduces fundamental MIMO radar concepts, highlighting advantages over traditional SIMO (Single-Input Multiple-Output) radar systems, particularly regarding processing gain and anti-jamming capabilities. Implementation typically involves optimizing antenna array configurations and signal coding schemes through MATLAB-based simulations.

SIMO Radar Modeling and Processing Before delving into MIMO radar, the chapter revisits SIMO radar modeling approaches, including generalized radar transmission waveforms, target modeling, covariance modeling, and corresponding signal processing techniques. This foundation enables comparative analysis with MIMO systems, where key algorithms like matched filtering and Doppler processing are implemented using matrix operations in computational environments.

Slow-Time MIMO Radar Modeling "Slow-time" refers to signal variations within pulse repetition intervals, where MIMO radar demonstrates unique advantages in target modeling and covariance structure. This section details how slow-time dimension utilization enhances radar resolution and clutter rejection capabilities. Code implementations often involve constructing temporal covariance matrices and applying eigenvalue decomposition for signal subspace extraction.

Signal Processing and Performance Analysis MIMO radar signal processing encompasses pattern optimization, voltage standing wave ratio analysis, and subarray design. Through comparative studies with SIMO systems, the chapter emphasizes MIMO's superiority in directional spectrum estimation. Simulation experiments demonstrate practical results for receive/transmit beamforming, signal-to-interference-plus-noise ratio (SINR) performance evaluation, and transmit-receive spectrum analysis, validating theoretical frameworks through MATLAB-based Monte Carlo simulations.

Applications and Future Prospects The concluding section examines applications in over-the-horizon propagation and radar clutter modeling, while summarizing MIMO radar's potential development trajectories in military and civilian domains. This provides readers with a complete pathway from theoretical research to engineering implementation, where real-world deployment considerations include FPGA-based real-time processing architectures and adaptive filtering algorithms.

Through this chapter, readers gain deep understanding of core MIMO radar signal processing technologies, particularly adaptive processing methods in slow-time domains, providing critical references for practical system design and optimization. Algorithm implementations typically involve space-time adaptive processing (STAP) techniques with covariance matrix estimation and weight vector computation routines.