Signal Analysis Overview

Radar signal analysis can be categorized into three main types:

Time-Frequency Transform-based methods, Time-Alignment-based methods, and Deep Learning-based feature learning.

Unlike the first two methods which measure similarity via explicit mathematical operators, the latter relies on data-driven feature learning.

1. FFT, STFT and Wavelet Transform

These methods analyze signals by decomposing them into components, using basis functions as the reference signals for similarity measurement.

Aspect	FT(FFT)	STFT	Wavelet Transform
Basis Function
Varying Parameters	Frequency ω	Time τ, Frequency ω	Time b, Scale a
Meaning	Extract frequency components over the entire signal	Extract frequency components within a local time window	Extract time-scale components at a specific time and scale

2. DTW

This method analyzes signals by aligning them to a target, using a template waveform as the reference signal for similarity measurement.

3. Deep Learning Model

Unlike traditional methods that require manual feature engineering,

Deep Learning models automatically learn hierarchical representations directly from raw or pre-processed radar data.