A New Thresholding Method for IR-UWB Radar-Based Detection Applications

Abstract

In this paper, we proposed a new thresholding method for impulse radio ultra-wideband (IR-UWB) radar-based detection applications by taking both the false alarm and miss-detection rates into consideration. The thresholding algorithm is the key point of the detection application, and there have been numerous studies on these developments. Most of these studies were related to the occurrence of false alarms, such as the constant false alarm rate algorithm (CFAR). However, very few studies have considered miss-detection, which is another crucial issue in detection applications. To mitigate this issue, our proposed algorithm considered miss-detection as well as the false alarms occurring during thresholding. In the proposed algorithm, a threshold is determined by combining a noise signal-based threshold, in which the focus point is the false alarm, with a target signal-based threshold, in which the focus point is a miss-detection, at a designed ratio. Therefore, a threshold can be determined based on the focus point by adjusting the designed ratio. In addition, the proposed algorithm can estimate the false alarm and miss-detection rates for the determined threshold, and thus, the threshold can be objectively set. Moreover, the proposed algorithm is better in terms of understanding the target signal for a given environment. A target signal can be affected by the clutter, installation height, and the angle of the radar, which are factors that noise-oriented algorithms do not consider. As the proposed algorithm analyzed the target signal, these factors were all considered. We analyzed the false alarm and miss-detection rates for the thresholds, which were determined by different combination ratios at various distances, and we experimentally verified the validity of the proposed algorithm.

Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems

“…In Figure 2, NP criteria is adopted to calculate the detection probability (P D ) and false alarm probability (P FA ) in STH-UWB systems. Figure 2 was simulated using (23) and (27). The value for µ a , µ p , σ a and σ p are calculated using ( 29), ( 36), ( 34) and (41), respectively.…”

“…However, these techniques have drawbacks such as low accuracy, low data-rate and/or short range communication [ 18 , 19 , 20 , 21 , 22 ]. Furthermore, when using narrow-band device-free indoor communication, multipath fading and a cluttered environment make it difficult to design a low-cost and low-complexity receiver [ 23 , 24 , 25 ].…”

Detection Error Contaminated by Outliers to Classify Density Profiles Dependent on the Relative Speed Between a MIMO Sensor and a Human Hand

“…However, in dynamic conditions, a constant false alarm rate method leads to incorrect detection, which must be alleviated to ensure safety [29]. Therefore, some studies have focused on pre-processing techniques that can complement misdetections, which lead to dangerous side failure [30], [31].…”

Optimal Central Frequency for Non-Contact Vital Sign Detection Using Monocycle UWB Radar

“…Figure 12c shows the corresponding 2D-matrix (7985 × 512) of fast-time versus slow-time plots. The antenna coupling as well as the signal-to-noise ratio (SNR) can be improved by using basic filters to remove static signals, like linear least-squares, smooth filter, and bandpass filter for slow and fast-time domains [22,31,44]. The fast Fourier transform (FFT) technique was used to compute the Doppler shift of the vital sign signals [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

“…As opposed to (6), which assumes m r to be constant, the amplitude of human chest movement does not follow perfect sinusoidal form, as measured and shown in Figure 11a. Another reason could be the loss of symmetry of the monocycle pulse that normally comes from the limited bandwidth of the antennas and other RF components, which is very typical in the real-world situations [44].…”