OVERVIEW OF UWB TECHNOLOGY
Introduction to UWB technology
- Applications of UWB technology
- UWB technology for measuring the distance and positioning
- Modulation techniques for the UWB signals
In addition, high-speed data transmission is possible for the ultra-wide bandwidth of UWB signals. In the UWB system, s(t) is generated based on Gaussian [32] or Hermite [33] functions and their derivatives.
![Figure 1.1: Absolute bandwidth of UWB signal [1].](https://thumb-ap.123doks.com/thumbv2/1libvncom/9143384.0/28.892.225.726.616.924/figure-absolute-bandwidth-of-uwb-signal.webp)
The UWB system model used for investigating and positioning
The errors of the estimated distance become smaller as the length of the PN sequence used in the UWB system increases [40], [41]. Therefore, the length of the PN sequence should be changed according to the sensing distance to optimize the processing time of the system.
Evaluation of positioning systems using UWB technology
In the following chapters of the thesis, UWB signals are applied to locate buried objects in 2-D space. The performance of these systems is evaluated in terms of positioning error in the same way.
The related works
- The related studies abroad
- The domestic studies
- Research objectives of the thesis
With the high spatial resolution range, the UWB-based penetration system is one of the potential candidates for the non-destructive testing (NDT). Therefore, the performance of the UWB GPR system mainly depends on the method of the received signal processing. Therefore, different UWB signal modulation types have significantly affected the quality and application of the UWB system.
In addition, several studies on improving the quality of the receiver for UWB-PPM signals were presented in. Furthermore, the estimation of the layer thickness based on the processing of the GPR's data with the optimized techniques (such as neural networks) was discussed in [ 75 ], [76]. The existence of the buried object can be detected using the GPR scan or combined with the spectral analysis of the radar data [84].
In [J1], the solution of changing the pulse width of the UWB signal adaptively with the probed depth was proposed. Moreover, in the case of a multi-layered heterogeneous medium, the estimation of the reflection coefficient is very complicated. The CFST method can be used to locate and distinguish adjacent buried objects and improve the resolution of penetrating systems [J5].
Analysis of a UWB penetrating system
UWB signal propagates from point A → B → C → D and is reflected back from D to A, and the propagation distance of the signal is equal to 2(AB + BC + CD). The strength of the received signals or their corresponding arrival times can be used to calculate the location of the discontinuity (layer thickness and the position of buried object). This section solves the first problem in positioning engineering: to determine the transmission distance, which is the thickness of the layers in the studied environment.
Based on the parameters of the received signal, the propagation distance can be determined based on the value of travel time or RSSI as indicated in Fig.
Determination of the propagation distance based on RSSI
- Evaluation of RSSI ranging performance
- Gauss-Newton method
The propagation distance and path loss exponent η of the transmission medium are estimated by the Gauss-Newton algorithm. The Gauss-Newton method is used to estimate the path loss exponent and the location of the buried object using RSSI. To describe the Gauss-Newton method, let's consider a scenario with a circular buried object of radius R in a homogeneous path-loss environment.
The Gauss-Newton method was used to estimate the path loss exponent and the location of the buried object by using RSSI and TOA of reflected signals. These errors include the RSSI (or TOA) calculation error and the Gauss-Newton method error. 2.5 it can be seen that by using the Gauss-Newton non-linear estimation method for the RSSI and TOA of the penetration UWB system, the path loss exponent of the propagating medium and the location of the buried object can be determined.
Distance estimation based on the RSSI and TOA methods is applied to a homogeneous medium. In addition, modulation techniques for the UWB signal, including UWB-PPM pulse position modulation, are used to increase the ability to correctly detect the reflected UWB signal. PPM modulation is often used for UWB system, and in communication, PPM means that the position of the pulse carries the transmitted information.
Proposal of UWB-PPM with an additional time shift
- Distance estimation procedure
- Evaluation of the UWB-PPM-ATS technique
- Comparison of the computational complexity
Examples of conventional UWB, UWB-PPM and UWB-PPM-ATS waveforms are illustrated in Figs. For the proposed UWB-PPM-ATS, the new shift level is set to (TPPM + ζ) instead of the normal time shift TPPM. The performance of the UWB-PPM-ATS technique is evaluated by mathematical analysis and Matlab simulation.
The UWB-PPM-ATS technique is compared with other modulation techniques including UWB-OOK and UWB-PPM according to the accuracy of distance estimation in heterogeneous medium. To evaluate the UWB-PPM-ATS system, view the heterogeneous medium diagram in Fig. An example of the form of the correlation function used to estimate the total depth d of the UWB-PPM-ATS system is shown in Fig.
Therefore, the average error in the PPM-ATS with ζ = −0.08 ns is smaller than in the UWB-PPM. Otherwise, with other values of ζ, it is possible to make the error of distance estimation of the UWB-PPM system higher than that of the conventional UWB-PPM. The complexity of the UWB-PPM-ATS technique is investigated by the number of floating point operations (FLOPS) performed for the correlation function in the receiver side.
Summary
In the proposed method, the values ofdob, ε, andZob are estimated based on the travel time τi and the position ZDei of the device. 3.4 (a) shows the travel time dependence curve τ of the reflected signal at the location of the ZDe device. In the other case, two buried objects are far apart and the distance between them is much larger than the system resolution.
Based on the sample set of the correlation values in the case of positioning a single buried object, the results of locating two objects are illustrated in Fig. The movement distance of the second object from the first object is estimated by the CFST. The error in estimating the movement distances of the second buried object is shown in Fig.
3.17, the signals reflected from the first buried object, the boundary and the second buried object at the ith displacement of the device have the following forms. Moreover, the evaluation is based on the average absolute error of the estimated distance. Finally, the multi-buried objects in a heterogeneous environment are detected by shifting the position of the transmitted pulse with different motions.
CORRELATION FUNCTION SEPARATION AND
A proposed method of positioning a single buried object
- Estimation algorithm
- The results of positioning a single buried object
To simplify the positioning problem, in this chapter, the position of the buried object is defined at the upper middle point of the object, which is considered the point of the reflected wave. Here, the position of the object is considered in two-dimensional space (2-D) and is considered as the reflection point of the propagation. Thus, the equation for determining the parameter values can be rewritten as follows.
The parameters of the system model and the initialization vectors of the LMF algorithm are listed in Table 3.1, where the environment is homogeneous and has a buried object. In the IR-UWB systems, their performance for using the fourth-order Gaussian monocycle is better than the others with respect to . The accuracy of the IR-UWB system depends primarily on the determination of the travel time, which is calculated from the maximum value of the correlation function (see Eq.
This leads to determining the travel time based on the correlation function of the third order Gaussian unicycle with a larger error than when using other unicycles. This behavior follows directly from the characteristics of the correlation functions of different signals shown in Fig. Using the UWB-PPM-ATS technique with the optimal value of ζ, the estimated values of the travel time have a smaller error than .
Positioning multi-buried objects in a homogeneous environment
- Proposed multi-buried objects positioning method in homoge-
- System model
- Positioning method
- Numerical results and comparisons
In the first case, it is assumed that two buried objects are very close to each other, this means that the distance between them is approximately a resolution of the system. In addition, as described in Section 3.1, the position of buried objects and characteristic of propagation environment (the relative permittivity) depends on the travel time, which is determined from the values of the correlation function of system. In the case of two objects close to each other, the thesis proposes a method to determine the second object by using the correlation function values and the position of the first object determined according to Section 3.1.1.
Determining the travel time τ2 using Eqs. 3.10 illustrates examples of the shapes of the correlation function of the received signal with the reference wave when there are two closely buried objects. Therefore, based on the values of the correlation function in the case of positioning a single object, the travel time τ2 can be estimated according to Eqs. 3.21) and (3.22), and the position of the second object can be set in the same way as presented in the section. The actual position of the second buried object The estimated position of the second. 3.21) The estimated position of the second buried object from Eq.
Thus with the proposed CFST method, two buried objects can be fully distinguished when the distance between them is close to the possible resolution of the system (∆r = 2.1 cm). At the ith transmitter displacement, d1i, L1i are the propagation distances from the device to the first buried object, and the boundary and L2i. To simplify the presentation of the parameters, the geometry of the system model in Fig.
Summary
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The computational complexity
Initialization parameters of the model [2], [3]
The results of the estimated parameters
Simulation parameters [2], [3]
Estimated results
The comparison of results
![Figure 1.2: The EIRP limits for UWB systems in indoor environments are set by the FCC [1].](https://thumb-ap.123doks.com/thumbv2/1libvncom/9143384.0/30.892.225.716.173.554/figure-eirp-limits-uwb-systems-indoor-environments-fcc.webp)
