Cheol-Sun Park

Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM

This paper presents modulation classification method capable of classifying incident digital signals without a priori information using WT key features and SVM. These key features for modulation classification should have good properties of sensitive with modulation types and insensitive with SNR variation. In this paper, the 4 key features using WT coefficients, which have the property of insensitive to the changing of noise, are selected. The numerical simulations using these features are performed. We investigate the performance of the SVM-DDAG classifier for classifying 8 digitally modulated signals using only 4 WT key features (i.e., 4 level scale), and compare with that of decision tree classifier to adapt the modulation classification module in software radio. Results indicated an overall success rate of 95% at the SNR of 10dB in SVM-DDAG classifier on an AWGN channel.

The Fast Correlative Interferometer Direction Finder using I/Q Demodulator

This paper presents the development of the fast correlative interferometer direction finder using I/Q demodulator, which demodulates incoming signals using direct-conversion technique without synthesizer tuning time. Also this paper compares the performance of the two types (i.e. correlation based and LSE based) of CIDF (correlative interferometer direction finding) algorithms with that of the CRLB (Cramer-Rao lower bound) in 5-channel circular equi-spaced array. The CIDF algorithms have several beneficial features that are not shared by conventional DF (direction finding) algorithms such as robust operation in severe environment. This paper presents the results of the field trial using the pilot system with direct-conversion receiving technique. Using fast RF down converter and robust CIDF algorithms, we compare the performance of the pilot system with the result of numerical simulation

Automatic Modulation Recognition using Support Vector Machine in Software Radio Applications

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification. This paper discusses the modulation classifiers capable of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. In this paper, we investigate the performance of the two types of SVM classifiers and compare the performance of these SVM classifiers with that of decision tree based and minimum distance based classifiers. In numerical simulations, SVM classifiers indicate good performance (i.e. probability of correct classification > 95%) on an AWGN channel, even at signal-to-noise ratios as low as 5 dB.

Reconstruction of BCH codes using probability compensation

If a receiver does not have accurate channel coding parameters, it becomes difficult to decode digitized encoding bits correctly from a noisy intercepted bit stream. To perform decoding without the channel coding information, we should estimate the channel coding parameters and identify the channel coding type. In this study, we propose an efficient algorithm for the reconstruction of the BCH (Bose-Chaudhuri-Hocquenghem) code. We use the property of cyclic codes for the reconstruction of the BCH code. In addition, we present a probability compensation method to improve the reconstruction performance. This is based on the concept that a random data pattern can also be divisible by a minimal polynomial of the generator polynomial of the BCH code. We use this property to improve the recognition performance, and we confirm the performance improvement through an intensive computer simulation.

Modulation Classification of Analog and Digital Signals Using Neural Network and Support Vector Machine

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy i¾? 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively.

A Wideband Compressive Receiver for Real-time Signal Detection

A wideband compressive receiver at V/UHF-band is presented. It uses the wideband and high time delay SAW DDL to improve the frequency resolution and scan rate. This type of the DDL has properties of the relative bandwidth of 20 %, the time delay of 49.9 usec, the insertion loss of 38.5 dB, and the time spurious rejection of 36 dB. The presented compressive receiver employs the multiply-convolve structure for real-time frequency measurement. It consists of the RF converter, fast sweep LO, chirp LO, A/D converter, signal processing unit and control unit. The chirp LO is designed by using the two SAW DDLs to improve the chirp linearity and the slope matching to the DDL of the RF converter. The developed system shows the wide instantaneous bandwidth and the fine frequency resolution. This system is useful for monitoring the frequency hopping spread spectrum system suggested to reduce the probability of interference jamming

Recognition of Convolutional Code with Performance Analysis

The use of an error-correcting code is essential in communication systems where the channel is noisy. Unless a receiver has accurate channel coding parameters, it becomes difficult to decode the digitized encoding bits correctly. In this paper, we propose two algorithms for reconstructing convolutional codes: one for general convolutional codes and the other for punctured convolutional codes. And we also verify the algorithms by performing intensive computer simulation in additive white gaussian noise (AWGN) channel.

Indoor sensor array test-bed(SATB) for direction finding applications at communication-band

An efficient sensor array test-bed compatible with direction finding indoor test applications at communication-band is developed. It provides the test flexibility of DF accuracy including the different antenna configuration, DF algorithms and modulation schemes considering with instrumental errors induced by multi-channel receivers. The ability of test-bed is useful for system designers who are involved in the development of DF system at communication-band. Further research will be investigated the DF accuracy of FHSS and the effects of interference signal, frequency offset and integration time.

A Multi-Channel Correlative Vector Direction Finding System Using Active Dipole Antenna Array for Mobile Direction Finding Applications

A fast correlative vector direction finding(CVDF) system using active dipole antenna array for mobile direction finding(DF) applications is presented. To develop the CVDF system, the main elements such as active dipole antenna, multi-channel direction finder, and search receiver are designed and analyzed. The active antenna is designed as composite structure to improve the filed strength sensitivity over the wide frequency range, and the multi-channel direction finder and search receiver are designed using DDS-based PLL with settling time of below 35 us to achieve short signal processing time. This system provides the capabilities of the high DF sensitivity over the wide frequency range and allows for high probability of intercept and accurate angle of arrival(AOA) estimation for agile signals. The design and performance analysis according to the external noise and modulation schemes of the CVDF system with five-element circular array are presented in detail.

Recognition Algorithm for RM Codes Using Fast Hadamard Transform

The use of an error-correcting code is essential in digital communication systems where the channel is noisy. Unless a receiver has accurate channel coding parameters, it becomes difficult to decode the digitized encoding bits correctly. In this paper, estimation algorithm for RM(Reed-Muller) codes using FHT (Fast Hadamard algorithm) is proposed. The proposed algorithm estimates the channel coding parameters of RM codes and then decodes the codes using the characteristic of FHT. And we also verify the algorithm by performing intensive computer simulation in additive white gaussian noise (AWGN) channel.