Radar-Based Multi-Class Recognition of Pedestrians, Cyclists, and Vehicles Using Random Forest and LSTM Under Clutter

Reliable radar-based discrimination of pedestrians, cyclists, and vehicles is a core requirement for roadside safety monitoring and autonomous-driving perception, particularly under adverse lighting and weather where camera-only systems degrade. This paper presents a machine-learning framework for three-class radar target recognition human (pedestrian), car, and bicycle evaluated under both an ideal, clutter-free environment and a realistic environment incorporating ground, weather, and urban clutter together with additive noise. A synthetic phased-array radar dataset (9.4 GHz, 500 samples per class per condition) was generated from the monostatic radar range equation, target-specific radar-cross-section and micro-Doppler models, and standard ground/weather/urban clutter distributions. Two classifiers were trained and compared: a Random Forest (RF) operating on aggregated per-dwell handcrafted features, and a Long Short-Term Memory (LSTM) network operating directly on the raw per-pulse feature sequence. Under ideal conditions, RF achieved 98.1% accuracy (F1 = 0.981, AUC = 0.9997) and LSTM achieved 89.3% accuracy (F1 = 0.892, AUC = 0.983). Under realistic clutter, RF accuracy changed only marginally, to 97.9%, while LSTM accuracy fell to 87.2%. After mutual-information-based feature selection and hyperparameter tuning, RF remained at 97.9% (already near ceiling) while tuned LSTM recovered to 89.1% (F1 = 0.891, AUC = 0.972). These results indicate that classifiers built on statistics aggregated over a full radar dwell are substantially more robust to per-pulse clutter than sequence models that consume raw per-pulse observations directly, and that hyperparameter tuning recovers a meaningful share of the accuracy lost by the latter. All results reported are from an actually executed simulation and training pipeline; validation against measured radar data is identified as necessary future work.