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mmWave Radar-Based Wall Classification Using Progressive Feature Selection
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AI
DOI：10.1109/jsen.2026.3716595.png)
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Abstract
En 中文
This article proposes a progressive feature selection framework for millimeter-wave (mmWave) radar-based wall material classification. Raw complex baseband I–Q data are collected using a TI AWR1843 radar over multiple days from eight indoor surface types: transparent glass wall, smooth glass board, cement wall, metal door, vinyl floor, olefin carpet floor, ceramic tile, and concrete wall. We introduce an incremental-data decremental-feature learning (IDDFL) strategy that incrementally aggregates daywise training data while progressively pruning low-contribution features using a posthoc explainer. A preprocessing module extracts 100 handcrafted descriptors covering time-domain, frequency-domain, time–frequency, and radar-specific characteristics from the acquired I–Q measurements. Feature relevance is quantified via SHapley additive exPlanations (SHAP) analysis on a LightGBM (LGBM) classifier, and the least discriminative features are removed across days. The proposed IDDFL process reduces the feature set from 100 to 30 while maintaining high classification performance. The proposed model achieves a final test accuracy of ${79}.{80}\%$ with an $F1$ -score of ${0}.{79}$ , demonstrating improved computational efficiency with minimal performance degradation. The final 30-feature LGBM model outperforms nine state-of-the-art benchmark methods reported in the literature and is deployed on edge platforms (Raspberry Pi 4 and Rock Pi 4 C+) to demonstrate real-time applicability. The proposed feature selection and edge-ready pipeline is well-suited for building inspection, autonomous navigation, and security applications.
Keywords:
Edge deployment
frequency-modulated continuous wave (FMCW) radar
radar-based material classification
SHapley additive exPlanations (SHAP)
wall classification
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4.5
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