Fourth International Conference on Computer Science and Information Technology (CoSIT 2017)

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In this paper, a novel wavelet-based detection algorithm is introduced for the detection of chipless RFID tags. The chipless RFID tag has a frequency signature which is identical to itself. Here a vector network analyser is used where the received backscatter signal is analysed in frequency domain. Thus the frequency signature is decoded by comparing the wavelet coefficients which identifies the bits accurately. Further, the detection algorithm has been applied for the tag detection under different dynamic environments to check the robustness of the detection algorithm. The new method doesn’t rely on calibration tags and shows robust detection under different environments and movement.
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    Dhinaharan Nagamalai et al. (Eds) : CoSIT, SIGL, AIAPP, CYBI, CRIS, SEC, DMA - 2017 pp. 29– 38, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70404  A N OVEL  A  DAPTIVE  - W   AVELET  B  ASED D ETECTION  A  LGORITHM   FOR   C HIPLESS RFID S  YSTEM Meriam A. Bibile and Nemai C. Karmakar Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia  A  BSTRACT     In this paper, a novel wavelet-based detection algorithm is introduced for the detection of chipless RFID tags. The chipless RFID tag has a frequency signature which is identical to itself.  Here a vector network analyser is used where the received backscatter signal is analysed in  frequency domain. Thus the frequency signature is decoded by comparing the wavelet coefficients which identifies the bits accurately. Further, the detection algorithm has been applied for the tag detection under different dynamic environments to check the robustness of the detection algorithm. The new method doesn’t rely on calibration tags and shows robust detection under different environments and movement.  K   EYWORDS   Chipless RFID, wavelet, backscatter signal, frequency domain 1. I NTRODUCTION The chipless RFID reader extracts the backscattered signal and decodes the tag ID. This is an ongoing challenge, as the detection procedure for a chipless RFID tag has more complexities compared to a conventional RFID tag. The signal collides with other scatterers or tags which give a ‘clutter' signal with interference. A number of detection techniques have been applied to achieve an accurate result of its tag ID. The basic detection technique is based on comparing the received data with threshold values obtained by calibration. It is, therefore, a basic approach and it does not possess the flexibility and adaptability required in the detection process to address errors due to a dynamic environment [1]. Different types of detection algorithms and decoding techniques have been revealed in the past few years. Moving average technique is a simple de-noising technique which removes noises by acting as a low pass filter. An 11 sample averaging moving average filtering has been successfully implemented on a low-cost mid-range microcontroller having low processing power capabilities, and a smoothened waveform is resulted after using this filtering technique. Hilbert transform  30 Computer Science & Information Technology (CS & IT) (HT) is a complex analytical signal processing technique [2]. This technique has been used to reconstruct the frequency signatures of the chipless tags. It has been experimentally proven that HT provides the extraction of the amplitude and phase functions of the frequency signature. The signal space representation of chipless RFID tags uses an efficient mathematical model to decode information in a chipless RFID tag [3-4]. The frequency signatures are represented by a matrix which is composed of orthonormal column vectors and a singular value matrix. The constellation of signal points are plotted with a basis function. It can be seen that as the number of bits increase this method will face limitations. Matrix pencil method (MPM) and Short time matrix principle method (STMPM) are two more detection techniques that have been applied for chipless RFID systems [5-6]. These two techniques are applied in the time domain and are mentioned as accurate detection techniques in extracting the carrier to noise ratio (CNR) of the response. Detection is performed by extracting the poles and residues from the backscattered signal using the Matrix Pencil Algorithm. A Maximum Likelihood (ML) based tag detection technique and Trellis decoding technique has been developed where detection error rate is compared with the bit to bit detection [7]. It has been found that ML detection has the best performance. It also reports that the computational complexity is higher in ML detection technique than Trellis detection technique. The main aim of this paper is to develop a detection algorithm which can be practically applied in firmware. As many of the above algorithms are highly complexed in implementing in the chipless RFID reader. Also, most of them have the limitation in the number of bits that can be detected. In this paper, we have developed a novel wavelet which suits the chipless RFID received signal to detect the frequency ID of the chipless RFID tag. 2. S YSTEM  M ODEL   AND  D ESIGN   In this section the system model of the chipless RFID system used in this analysis is discussed. A description of the backscattered signal is also given and the backscattered signal of the used tag is analysed using simulation results obtained through CST microwave studio. 2.1. Experimental Model Figure 1 –Chipless RFID system  Computer Science & Information Technology (CS & IT) 31 The experiment is performed using a 5-bit spiral resonator design. Each resonator has its own resonance frequency and has a diameter of 5mm. The tag with 5 bits has a dimension of 2cm 󰃗  2cm.The tag design is not presented in this paper to due to the confidentiality of the tag design. The tag RCS is shown in Fig 2. A patch antenna is used for the measurements, and the reading is taken by loading the antenna with a tag using vector network analyzer (VNA) as shown in Fig 1. The tag is loaded above the patch antenna and is placed on a piece of foam with 1cm thickness. Backscattered signals from chipless RFID tags are very weak, and the detection process at the RFID reader is extremely susceptible to noise. This is because the information is represented as analog variations as opposed to a modulated digital stream of data as in conventional wireless communication of information where error correction schemes can be used to detect the presence of errors and discard erroneous data packets. According to Fig 2, the resonant frequencies of the tag are located at 4.15, 4.70, 5.37, 6.10 and 6.95GHz respectively. The other local maxima detected in the signal are due to the amplitude spectrum of the background and antenna S11. These spurious peaks need to be carefully filtered to detect the correct frequency signature of the chipless RFID tag. Figure 2 – S11 Magnitude (dB) vs. frequency of the 5-bit resonant tag 2.2. Wavelet Design In this paper, a novel wavelet is adopted to detect the peaks of the backscattered signal. It is based on the Gaussian function which is given by (1).   󰀽  󰃗     (1)   where a is the height, b is the position and c  is the width of the wavelet. The values a, b and c  are adjusted to maximize the detection. The novel wavelet design is shown in Fig 3. The width of the wavelet is adaptive according to the number of bits in the tag. In Fig 3, the width of the wavelet has been taken as 250MHz giving a  32 Computer Science & Information Technology (CS & IT) wider bandwidth to the wavelet. Further for the detection of the results this has been changed to 100MHz giving better resolution of the detected bits. This is an advantage of this algorithm as higher number of bits can be detected with better resolution. The detected signal is compared with the wavelet, and a coefficient is defined to represent how closely correlated the wavelet is with each part of the signal. The larger the coefficient is in absolute value, the more the similarity appears. The wavelet is shifted until it covers the whole signal. The threshold coefficient is defined after performing the experiment for the tag with bit ‘11111’ number of times under different environmental changes. By the variation in the detected signal under different circumstances a minimum and maximum coefficient is set giving rise to a confident band for detection. Figure 3 – New adopted wavelet pattern using Gaussian function 2.3. Flowchart for Detection Algorithm The flowchart given in Fig 4, shows the steps of the developed algorithm and how it is applied to the received signal. First the measured results using VNA are loaded into Matlab. The post processing is performed using Matlab programming software. The program can be directly downloaded to the microcontroller of the frequency domain reader developed by the research group which will be implemented in the next step of this research. A baseline removal is performed on the first results to remove the nulls of the antenna S11. Then the wavelet is defined and is integrated with the resulting signal. The tag ID is decoded based on the condition band defined for the detection. Experiments have been performed under different indoor dynamic environments such as placing clutter objects above the chipless RFID tag. Fig 8, shows the placing of hand 7cm above covering the tag adding back reflection to the received signal. Similarly, a copper plate was also placed above the tag at 7cm, and the received signal was observed using the VNA.
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