radar
**Radar (Radio Detection And Ranging)** uses transmitted electromagnetic waves and their echoes to detect, locate, and characterize targets. Modern automotive and military radar systems are among the most demanding signal-processing applications, requiring real-time processing of 3D point clouds at millimeter precision.
**FMCW (Frequency Modulated Continuous Wave)** is the dominant waveform for short-to-medium range radar. A linear chirp sweeps bandwidth B over time T; mixing the echo with the transmitted signal produces a beat frequency fb proportional to range (R = c·fb·T/2B). A 2D FFT over fast-time (range) and slow-time (Doppler) dimensions produces the Range-Doppler map showing both position and velocity of targets simultaneously.
**Phased arrays** steer beams electronically by applying differential phase shifts across antenna elements, achieving millisecond beam switching versus mechanical seconds. MIMO radar multiplies virtual aperture: Tx_count × Rx_count virtual elements dramatically improve angular resolution without additional hardware.
**CFAR (Constant False Alarm Rate)** detection adaptively thresholds the range-Doppler map by estimating local noise from surrounding cells. CA-CFAR averages reference cells; OS-CFAR (ordered statistic) is more robust to clutter edges. The goal is constant false alarm probability regardless of varying noise floor.
**Key waveform parameters**: Range resolution ΔR = c/2B (finer with wider bandwidth); velocity resolution Δv = λ/2NT; maximum unambiguous range Rmax = c·T/2; maximum unambiguous velocity vmax = λ/4T. These create fundamental trade-offs: wider bandwidth → better range resolution but more ADC bandwidth; longer coherent integration → better velocity resolution but slower update rate.
**AI/ML context**: Deep learning is transforming radar signal processing at every layer. CNNs classify targets from micro-Doppler signatures (pedestrian gait, hand gestures). PointNet architectures process sparse radar point clouds. Transformer-based sensor fusion combines radar, LiDAR, and camera for L4 autonomous driving. Radar SoCs (TI AWR, NXP S32R) now integrate ARM cores with hardware accelerators for on-chip neural network inference.
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