# The UWB Radar Application in the Aviation Security Systems
## 2 Materials and Methods
### Localization of the Target by the Straight-Line Method for Two Independent Radar Sets
#### 2.1.2 Pre-Processing of the UWB Radar Signal  
In this contribution, we introduce the possibilities of the UWB radar application in the determining the position of people behind an obstacle. The UWB radar signal is processed in several stages [29; 30]. The first task that needs to be solved in signal processing is pre-processing. At this stage, it is necessary to focus on eliminating or maximally suppressing the adverse effect of the radar itself on the received measurement signals. This phase of processing the radar signal with a pseudo-random M-sequence involves performing a correlation between the transmitted radar measurement signal and the received measurement signal. The procedure for pre-processing the received measurement signals was analyzed in [27]. In this work, we used the deconvolution method. Using this method can bring favorable results when tracking the location of multiple targets. The deconvolution task can be expressed as the process of obtaining an estimate of the actual primary output signal of the radar \(\hat{h}(t)\): \(\hat{h}(t)=\textit{deconvolution}[h_{R}(t)^{*}h_{T}^{\prime}(t)]\), where \(h^{\prime}\tau\) (\(t\)) the actual primary output of the radar and \(h_{R}(t)\) are the impulse responses (excluding the response from the target) obtained by the measurement. The next phase in the processing of UWB radar measurement signals is background elimination. Our aim is to achieve the highest possible ratio of the power of the received useful signal to the noise after the pretreatment. Background subtraction algorithms are aimed at suppressing unwanted responses from targets, standing waves at the receiver input due to antenna mismatch, and false targets caused by reflections from the environment. These procedures allow us to detect moving targets. We analyzed the background reading methods in more detail in [18]. Which method we use for pre-processing the received measurement signals of the UWB radar and for reading the background depends on the nature of the task and the type of a signal used. Real-time operation is one of the most common requirements for processing received UWB radar measurement signals. We used the mentioned phases of UWB radar measurement signal processing in our research when determining the position of people behind an obstacle. Since the signals of UWB radars propagate through obstacles, we must consider the non-line-of-sight (NLOS) radio propagation and other interfering effects when processing these signals. Pre-processing of the data and suppression of the background are used to suppress these effects. These signal processing steps are described in [29; 35]. When processing UWB radar signals from TxA and TxB transmitters, it is necessary to remove the background in these signals. For this purpose, it is appropriate to use the method of exponential averaging [35]. When subtracting the background, we proceed by determining the mean, median, or modus. We can use n previously received signals \(h(t,\tau)\) to estimate the background \(\hat{h}(t,\tau)\)[29]:  
\[ \begin{array}{l}\hat{h}(t,\tau)=E[h(t,\tau)]_{\tau 1}{}^{\tau 1+n},\\ \hat{b}(t,\tau)=\textit{median}[h(t,\tau)]_{\tau 1}{}^{\tau 1+n}.\end{array}  \tag{1}\]  
If we use the exponential method of determining the mean value, then we use the previous estimate of the background \(\hat{b}(t,\tau-1)\) to determine it. We determine the actual background value \(\hat{b}(t,\tau)\) using the following value of the function \(h(t,\tau)\), using the relations:  
\[ \begin{array}{l}\hat{b}(t,\tau)=\alpha\hat{b}(t,\tau-1)+(1-\alpha)h(t,\tau) \\ =\hat{b}(t,\tau-1)+(1-\alpha)[h(t,\tau)-\hat{b}(t,\tau-1)]\\ =\hat{b}(t,\tau-1)+(1-\alpha)h_{B}(h,\tau),\end{array}  \tag{2}\]  
where \(\alpha\) is the scalar weight factor acquiring the values within \((0\div 1)\) and \(\tau\), denoting the actual time of observation. From Relation (1), it is clear that the new estimate includes information from the previous and actual estimate. Based on this, the previous estimate is emphasized, or frequent signal fluctuations are suppressed.