Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method

Abstract

Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm for clutter elimination, harmonic suppression and random-noise de-noising. To improve detection performance, two filters are used to improve SNR of these life signs. The automatic gain method is performed in fast time to improve the respiration signals. The spectral kurtosis analysis (SKA)-based windowed Fourier transform (WFT) method and an accumulator in the frequency domain are used to provide two distance estimates between the radar and human subject. Further, the accumulator can also provide the frequency estimate of the respiration signals. These estimates are used to determine if a human is present in the detection environment. Results are presented which show that the range and respiration frequency can be estimated accurately in low signal to noise and clutter ratio (SNCR) environments. In addition, the performance is better than with other techniques given in the literature.

1-Tx/5-Rx Through-Wall UWB Switched-Antenna-Array Radar for Detecting Stationary Humans

“…with nth channel, σ p , σ o , σ v denotes stationary, non-stationary, and human objects, s(t-t n ) is time-shifting of the transmitted signal, and t and τ are fast-time and slow-time domains [34][35][36][37][38][39]. σ v s(t − t v (τ)) denotes response due to the respiration movement time t v (τ), which is dependent on the respiration amplitude [6], namely…”

“…Given that the challenges of measurement of reflected signal in continuous time (Equation ( 2)) are quite complex, this research thus determines the reflected signal in discrete time [34][35][36][37][38][39]…”

Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

“…Many works have proposed improvements to surmount the main drawbacks of these techniques, giving way to the more sophisticated MCSA methods, but they require expensive computations [3]. Nevertheless, WFT is still considered a powerful tool for several applications when the window shape is adapted or chosen properly [26]. From an engineering point of view, it is suitable for developing low-cost feature extractions on a regular basis.…”