In this paper, an efficient and robust structured VQ scheme based on an optimal IA version of the SSVQ technique, namely IA-SSVQ, was developed. The performance of SSVQ methods was investigated for quantizing a random highly correlated source and paramete

Journal of Science & Technology 123 (2017) 065-069 65 Low-cost Ionospheric Scintillation Detector using Software-based GNSS Receiver La The Vinh Hanoi University of Science and Technology, No. 1, Dai Co Viet, Hai Ba Trung, Hanoi, Viet Nam Received: May 19, 2016; Accepted: November 03, 2017 Abstract In this study, we investigate in the use of software-based GNSS receiver to detect ionospheric scintillation in Vietnam. Ionospheric scintillation is well-known for its bad effect on the precision of GNSS receivers. Vietnam locates at a low-lattitude region which is one of the most-affected region if any scintillation occurs. Therefore, it is important to be able detect and store the data of the receiver during scintillation periods for later analysis and mitigation. However, professional ionospheric scintillation monitoring machin are often expensive and not easy to access. The main goal of this work is to propose a low-cost method to detect the scintillation and to save its data for later use by utilizing a software-based GNSS receiver. Keywords: Ionosphere, Scintillation, GNSS, GPS 1. Introduction* In Global Navigation Satellite Systems (GNSS), it is well-known that the ionosphere layer strongly influences on the precision of the GNSS receivers, especially at low-lattitude regions due to the high value of the total electron content (TEC). In particular, once a strong ionosphere scintillation happens, it may totally disrupt GNSS signal’s phase and amplitude making the receiver unable to perform satellite aquisition and tracking. A number of publications has shown that strong ionosphere scintillations often happen at the time of strong solar activity, and at near- equator regions, including Vietnam [1-4]. Therefore, ionosphere-related research is gaining more attention from the researchers . Most popular research topics include characterizing TEC [2, 4], modeling the ionosphere layer [5-8], monitoring inosphere scintillations [9-12], etc. It can be seen that, those studies of the ionosphere are typically conducted with precision navigation receivers tracking both the multi-frequency carrier and code phase. However, the carrier and code measurements may not be available when the receiver is not able to accquire any satellite in a seriously strong scintillation. Some commercial GNSS logging equipments are available for capturing raw data in such a situation. Nevertherless, GNSS raw data takes a huge amount of storage space (approximately 16 MB/s). Hence it is not pratical to keep the data logger running continuously for hours or days. * Corresponding author: Tel.: (+84) 985.290.681 Email: vinh.lathe@hust.edu.vn Motivated by the need of a continuosly operating GNSS raw data logger for ionospheric scintillation monitoing, we propose in this work a monitoring systemwhich is capable of: (1) computing scintillation index in realtime, (2) activating data logger if and only if there is a scintillation (the index is over a predefined threshold), and (3) capturing raw GNSS data even if a strong scintialltion disabling the receiver from satellite accquision and tracking. The remaining of our paper is organized as following: in section 2, we give a detail description of our method and preliminary result; our conclusion is drawn in section 3. 2. The proposed methodand results Fig. 1 describes our system architecture which include four main parts: - A low-cost hardware front-end for receiving raw GNSS I/Q samples from satellites - A software-based receiver which is responsible for acquiring satellite and extracting S4 scintillation index of trackable satellites. - An online software ephemeris analyzer to calculate satellites’ position in real-time from IGS (International GNSS Service) data stream. - A software logger to record raw GNSS data to storage for later analysis. Journal of Science & Technology 123 (2017) 065-069 66 For the receiving front-end, we decide to choose a low-cost one from Sparkfun† which is illustrated in Fig. 2 with the below features: - Operating frequency: L1 (1.575 GHz) - Intermediate Frequency: 4.092 MHz - Bandwidth: 2.5 MHz Bit per sample: 2 bits Fig. 1. Block diagram of the proposed monitoring system Fig. 2. SIGE front-end For the software receiver, we use our existing one [13]. What we focus on (in this work) is to develop and integrate the S4 scintillation index computing algorithm into the software engine. Although phase scintillation is another index, we are not using this value because to precisely compute the phase measurement, an expensive oscillator is required, which obviously conflicts with our purpose of a low- cost system. To compute the S4 scintillation index, we directly utilize the output (I/Q samples) from the tracking phase of our software-based receiver. We first compute the narrow band and wide band power of every 20-millisecond period (M=20) from 1kHz I/Q samples (Ii and Qi): 𝑊𝑊𝑊𝑊𝑊𝑊 =�(𝐼𝐼𝑖𝑖2 + 𝑄𝑄𝑖𝑖2)𝑀𝑀 𝑖𝑖=1 (1) and 𝑁𝑁𝑊𝑊𝑊𝑊 = (∑ 𝐼𝐼𝑖𝑖𝑀𝑀𝑖𝑖=1 )2 + (∑ 𝑄𝑄𝑖𝑖𝑀𝑀𝑖𝑖=1 )2 (2) Then we compute S4 index using the below equation: 𝑆𝑆4 = �〈𝑆𝑆𝐼𝐼2〉 − 〈𝑆𝑆𝐼𝐼〉2〈𝑆𝑆𝐼𝐼〉2 (3) where 〈. 〉 denotes the average value over a period of 60 seconds. Finally, we detrend the S4 values using a low-pass filter as suggested in [14]. Fig. 3 illustrates S4 values calculated from a period of I/Q data with a scintillation observed (about 50 minutes at the beginning of the period). It should be noted that when a scintillation affects the amplitude of I/Q samples, the S4 values are significantly higher than those of the period without a scintillation. Before integrating the above algorithm into our real-time software receiver, we validate the algorithm by comparing our S4 values with those of recorded by a commercial-grade GNSS receiver (Septentrio Rx3). The data for the validation was recorded on March, 18th, 2013. Fig. 4 shows the C/N0 of satellites for validating. Fig. 3. I/Q samples and S4 calculated values in 120 minutes † https://www.sparkfun.com/ products/retired/10981 Journal of Science & Technology 123 (2017) 065-069 67 In the comparison, three satellites (PRN 7, 11, 19) are selected to demonstrate different ionospheric scenarios: no scintillation, strong scintillation and partially scintillation. The comparisions are illustrated in Fig. 8. As can be seen, S4 values computed by our post-processing algorithm, software receiver and the professional Septentrio receiver reflect similar trends; though the absolute values are somewhat different due to the detrending strategies of each method. Detrending is used to filter out high-frequency changes and to keep only low-frequency changes probably caused by inospheric scintillation. Fig. 4. C/N0 of satellites (7 – blue, 11 – red, and 19 – black) Fig. 5. Number of scintillations accumulated by satellites and S4 values (x10) Fig. 6. Number of scintillations accumulated by day and S4 values (x10) Fig. 7. Skyplot of scintillations over a month In addition to validating the calculation algorithm, we have developed a simple visualization tools to analyse the scintillation characteristics from the collected dataset. Fig. 5, 6, and 7 demonstrate useful characteristics of scintillation data collected at Hanoi in March, 2013. In Fig. 5 and Fig. 6, we count the total occurrence number of the scintillations in March, 2013 accumulated by satellites and days. It can be seen that March 26 and 28 have the highest numbers of scintillations. This fact can be explained as the effect of the March Equinox. Fig. 7 gives another aspect of the scintillation in March, 2013, where we can see some regions on the sky with a high probability of scintillation. 3. Conclusions In this paper, we have shown that S4 is a good index for detecting ionosphere scintillation and we have completed a properly implementation of S4 calculation algorithmusing raw I/Q samples. This approach does not require high-cost, specific-designed hardware; therefore it can be easily deployed on any personal computer. However, the biggest disadvantage of this approach is the calculation speed since the software-based receiver has to process millions of samples to compute one S4 value for each satellite. To overcome this limitation, we propose to calculate s4 sequentially satellite-by-satellite. Obviously there is a probability of missing short scintialltions if they happend with the satellites which are not currenly processed by the software receiver. The proposed automatic logger system is used in our EU-granted ERICA project in 12 months and has provided a database of more than 4 TB raw GNSS measurement (I/Q samples), which helps finding Journal of Science & Technology 123 (2017) 065-069 68 interesting ionospheric events such as the so-called Saint Patrick magnetic storm [14]. Fig. 8. S4 values of our method (+ black – mean detrending and x blue – lowpass detrending), and Septentrio receiver (o red) Acknowledgement This research was supported by Hanoi University of Science and Technology under the contract number T2016-PC_010. References [1]. Lê Huy Minh, A. Bourdillon, P. Lasudrie Duchesne, R. 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