Represent Micro-Doppler Signature in Orders

TL;DR

The Chebyshev-time map is proposed in this paper, which is a method characterizing micro-Doppler signature using polynomial orders and demonstrates the capability to characterize armed and unarmed indoor human activities while effectively compressing the scale of the time-frequency spectrum to achieve a balance between recognition accuracy and input data dimensions.

Abstract

Non-line-of-sight sensing of human activities in complex environments is enabled by multiple-input multiple-output through-the-wall radar (TWR). However, the distinctiveness of micro-Doppler signature between similar indoor human activities such as gun carrying and normal walking is minimal, while the large scale of input images required for effective identification utilizing time-frequency spectrograms creates challenges for model training and inference efficiency. To address this issue, the Chebyshev-time map is proposed in this paper, which is a method characterizing micro-Doppler signature using polynomial orders. The parametric kinematic models for human motion and the TWR echo model are first established. Then, a time-frequency feature representation method based on orthogonal Chebyshev polynomial decomposition is proposed. The kinematic envelopes of the torso and limbs are extracted, and the time-frequency spectrum slices are mapped into a robust Chebyshev-time coefficient space, preserving the multi-order morphological detail information of time-frequency spectrum. Numerical simulations and experiments are conducted to verify the effectiveness of the proposed method, which demonstrates the capability to characterize armed and unarmed indoor human activities while effectively compressing the scale of the time-frequency spectrum to achieve a balance between recognition accuracy and input data dimensions. The open-source code of this paper can be found in: https://github.com/JoeyBGOfficial/Represent-Micro-Doppler-Signature-in-Orders.

Figures (8)

• Figure 1: Overall concept of representing micro-Doppler in orders.
• Figure 2: Human kinematic model for normal walking and armed walking.
• Figure 3: Schematic diagram of the proposed DTM envelope extraction and ChTM generation method.
• Figure 4: Visualizations for synthetic motions: The first row is the DTMs, the second row is the macro ChTMs, and the third row is the micro ChTMs.
• Figure 5: Simulated and measured examples for tester P1: (a) Simulated result under armed condition, (b) Simulated result under unarmed condition, (c) Measured result under armed condition, and (d) Measured result under unarmed condition.