Browsing by Author "Alp, Y. K."

Now showing 1 - 18 of 18

Open Access
Classification of intra-pulse modulation of radar signals by feature fusion based convolutional neural networks
(IEEE, 2018) Akyon, Fatih Çağatay; Alp, Y. K.; Gök, Gökhan; Arıkan, Orhan
Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.
Open Access
Compressive digital receiver: first results on sensitivity, dynamic range and instantaneous bandwidth measurements
(Institute of Electrical and Electronics Engineers Inc., 2019) Korucu, A. B.; Alp, Y. K.; Gök, G.; Arıkan, Orhan
In this work, sensitivity, one/two-signal dynamic range and instantaneous bandwidth measurement results of the recently developed Compressive Digital Receiver (CDR) hardware for Electronic Support Measures (ESM) applications, will be reported for the first time. Developed CDR is a compressive sensing based sub-Nyquist sampling receiver, which can monitor 2.25 GHz bandwidth instantaneously by using four ADCs (Analog-to-Digital Receiver) each of which has a sampling rate of 250 MHz. All the digital processing blocks of the CDR are implemented in Field Programmable Gate Array (FPGA) and they work in real time. It is observed that the sensitivity and dynamic range of the CDR changes with respect to input signal frequency. For 2.25 GHz bandwidth, the best and worst sensitivity values of the CDR are reported as -62 dBm and -41 dBm, respectively. One-signal dynamic range of CDR is measured as at least 60 dB for the whole band. The best and worst values of the two-signal dynamic rage values are observed as 42 dB and 20 dB, respectively.
Open Access
Deep learning for radar signal detection in electronic warfare systems
(IEEE, 2020) Nuhoglu, M. A.; Alp, Y. K.; Akyön, Fatih Çağatay
Detection of radar signals is the initial step for passive systems. Since these systems do not have prior information about received signal, application of matched filter and general likelihood ratio tests are infeasible. In this paper, we propose a new method for detecting received pulses automatically with no restriction of having intentional modulation or pulse on pulse situation. Our method utilizes a cognitive detector incorporating bidirectional long-short term memory based deep denoising autoencoders. Moreover, a novel loss function for detection is developed. Performance of the proposed method is compared to two well known detectors, namely: energy detector and time-frequency domain detector. Qualitative experiments show that the proposed method is able to detect presence of a signal with low probability of false alarm and it outperforms the other methods in all signal-to-noise ratio cases.
Open Access
Deep learning in electronic warfare systems: Automatic intra-pulse modulation recognition
(Institute of Electrical and Electronics Engineers, 2018) Akyön, Fatih Çağatay; Alp, Y. K.; Gök, G.; Arıkan, Orhan
Detection and classification of radars in electronic warfare systems is a major problem. In this work, we propose a novel deep learning based method that automatically recognizes intra-pulse modulation types of radar signals. We use reassigned short-time Fourier transforms of measured signals and detected outliers of the phase differences using robust least squares to train a hybrid structured convolutional neural network to distinguish frequency and phase modulated signals. Simulation results show that the developed method highly outperforms the current state-of-the-art methods in the literature.
Open Access
Estimation of inter-pulse phase/frequency stepping parameters in electronic intelligence systems
(IEEE, 2018) Alp, Y. K.; Gök, Gökhan
In this work, a new method for estimating the modulation parameters of radar pulses which make interpulse phase/frequency stepping for pulse compression. Proposed method first applies special filters, which magnifies phase/frequency stepping instants, to the instantaneous phase of the measured radar pulse. Then, Robust Least Squares (RobLS) algorithm is utilized for anomaly detection. The local maximum of the detected anomaly points provides the high resolution estimates of the phase/frequency stepping instants. Extensive experiments conducted on synthetic data sets for different SNR (Signal-to-Noise Ratio) levels and phase/frequency stepping values show that proposed method can successfully estimate phase/frequency stepping instants.
Open Access
FIR filter design by convex optimization using directed iterative rank refinement algorithm
(Institute of Electrical and Electronics Engineers Inc., 2016) Dedeoğlu, M.; Alp, Y. K.; Arıkan, Orhan
The advances in convex optimization techniques have offered new formulations of design with improved control over the performance of FIR filters. By using lifting techniques, the design of a length-L FIR filter can be formulated as a convex semidefinite program (SDP) in terms of an L× L matrix that must be rank-1. Although this formulation provides means for introducing highly flexible design constraints on the magnitude and phase responses of the filter, convex solvers implementing interior point methods almost never provide a rank-1 solution matrix. To obtain a rank-1 solution, we propose a novel Directed Iterative Rank Refinement (DIRR) algorithm, where at each iteration a matrix is obtained by solving a convex optimization problem. The semidefinite cost function of that convex optimization problem favors a solution matrix whose dominant singular vector is on a direction determined in the previous iterations. Analytically it is shown that the DIRR iterations provide monotonic improvement, and the global optimum is a fixed point of the iterations. Over a set of design examples it is illustrated that the DIRR requires only a few iterations to converge to an approximately rank-1 solution matrix. The effectiveness of the proposed method and its flexibility are also demonstrated for the cases where in addition to the magnitude constraints, the constraints on the phase and group delay of filter are placed on the designed filter.
Open Access
Ionogram scaling using Hidden Markov Models
(Institute of Electrical and Electronics Engineers, 2018) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arikan, F.
In this paper, a novel method for electron density reconstruction using ionosonde data is proposed. Proposed technique uses Hidden Markov Models for extracting echoes that provides valuable information about electron density distribution in order to provide input to a model based optimization technique that reconstructs the electron density distribution by solving model parameters. Analysis on real ionosonde data shows that proposed technique outperforms standard techniques in the literature.
Open Access
Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity
(Elsevier Ireland Ltd, 2017) Öztoprak, H.; Toycan, M.; Alp, Y. K.; Arıkan, Orhan; Doğutepe, E.; Karakaş S.
Objective Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems. Method A novel classification approach that discriminates ADHD and nonADHD groups over the time-frequency domain features of event-related potential (ERP) recordings that are taken during Stroop task is presented. Time-Frequency Hermite-Atomizer (TFHA) technique is used for the extraction of high resolution time-frequency domain features that are highly localized in time-frequency domain. Based on an extensive investigation, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain the best discriminating features. Results When the best three features were used, the classification accuracy for the training dataset reached 98%, and the use of five features further improved the accuracy to 99.5%. The accuracy was 100% for the testing dataset. Based on extensive experiments, the delta band emerged as the most contributing frequency band and statistical parameters emerged as the most contributing feature group. Conclusion The classification performance of this study suggests that TFHA can be employed as an auxiliary component of the diagnostic and prognostic procedures for ADHD. Significance The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD.
Open Access
Machine-based learning system: classification of ADHD and non-ADHD participants
(IEEE, 2017) Öztoprak, H.; Toycan, M.; Alp, Y. K.; Arıkan, Orhan; Doğutepe, E.; Karakaş, S.
Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is confronted with many problems. In this paper, a novel classification approach that discriminates ADHD and non-ADHD groups over the time-frequency domain features of ERP recordings is presented. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain best discriminating features. When only three of these features were used the accuracy of classification reached to 98%, and use of six features further improved classification accuracy to 99.5%. The proposed scheme was tested with a new experimental setup and 100% accuracy is obtained. The results were obtained using RCV. The classification performance of this study suggests that TFHA can be employed as a core component of the diagnostic and prognostic procedures of various psychiatric illnesses.
Open Access
A method for automatic scaling of ionograms and electron density reconstruction
(IEEE, 2021-10-19) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan; Arıkan, F.
Ionogram scaling is the process of reconstructing electron density with respect to height by using the measurements of a remote sensing instrument known as ionosonde. In this study, a novel two stage ionogram scaling technique, ISED, is proposed. In the first stage, Hidden Markov Models (HMMs) are used to identify the actual ionospheric reflections in the ionosonde measurements. In the second stage, an IRI-Plas model based optimization problem is solved to obtain the vertical profile that generates the best least squares fit to the reflections identified in the first stage. To show the performance of ISED in global scale, experiments are conducted on 14,812 ionograms recorded at the three different stations which are Pruhonice in Czech Republic, Eielson in USA and Sao Luis in Brazil. Application of ISED to raw ionograms indicate 97.6% of the cases, ISED provides accurate electron density reconstructions, which is an improvement about 8.7% over ARTIST, most commonly used ionogram scaling technique.
Open Access
A new method for specific emitter identification with results on real radar measurements
(IEEE, 2020) Gök, Gökhan; Alp, Y. K.; Arıkan, Orhan
Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mode signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.
Open Access
Online calibration of modulated wideband converter
(IEEE, 2016) Alp, Y. K.; Korucu, A. B.; Karabacak, A. T.; Gürbüz, A. C.; Arıkan, Orhan
In this work, we propose a new method for online calibration of recently proposed Modulated Wideband Converter (MWC), which digitizes wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. Our method requires a single frequency synthesizer card, which can generate clean tones along the operation band of the system, rather than much expensive measurement instruments such as network analyser or vector spectrum analyser, which are not appropriate for online calibration. Moreover, low computational complexity of the proposed method enables its implementation on FPGA so that it can be embedded into the system. Hence, on each power on, the system can utilize self calibration without requiring any additional measurement instruments.
Open Access
Radar fingerprint extraction via variational mode decomposition
(IEEE, 2017) Gök, Gökhan; Alp, Y. K.; Altıparmak, F.
In this paper, a novel method for extracting radar fingerprint using the unintentional modulation on radar signals is proposed. Proposed technique decomposes the unintentional modulations into its components using Variational Mode Decomposition (VMD) technique. Then, features that characterize each component are calculated. Simulations using real radar data show that proposed technique can classify radars in the dataset with high performance.
Open Access
Sıkıştırılmış algılama tabanlı sayısal almaç: ilk donanım uygulaması sonuçları
(IEEE, 2018-05) Korucu, A. B.; Çakar, O.; Alp, Y. K.; Gök, Gökhan; Arıkan, Orhan
In this work, first real hardware implementation results of CDR (Compressive Digital Receiver) are detailed. CDR is a digital receiver technology, which estimates frequency, amplitude, pulse-width etc. parameters of the incoming signal without ambiguity in frequency, by sampling the signal at rates far below the Nyquist limit based on compressive sensing theory. It is observed that the implemented hardware structure can resolve an instantaneous bandwidth of 2 GHz by using only two ADCs (Analog to Digital Converter) running at 200 MHz sampling frequency. It is argued that the CR system can be used as a digital receiver especially for Electronic Support Systems since it can monitor a wide spectrum by sampling at a very low rate.
Open Access
SNR improvement in electronic support measures systems via pulse integration
(IEEE, 2017) Gök, Gökhan; Alp, Y. K.
In ESM (Electronic Support Measures) systems, detection of intentional or unintentional modulation on pulses requires high SNR. By integrating the collected pulses emitted from the radar, SNR can be increased. For utilizing pulse integration, all the pulses should be aligned in time very accurately. In this work, we propose a new method, which estimates the time shifts between the pulses with very high accuracy and resolution. Experiments on both synthetic and real data sets show that proposed method aligns the radar pulses very successfully.
Open Access
Sub-band equalization filter design for improving dynamic range performance of modulated wideband converter
(IEEE, 2017) Alp, Y. K.; Gök, Gökhan; Korucu, A. B.
In this work, we propose an iterative method to improve the dynamic range performance of the Modulated Wideband Converter (MWC), which is multi-channel sampling system for digitizing wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. Our method jointly designs FIR filters for each subband to equalize the frequency response characteristics of the all sub-bands of the MWC. Obtained results from the extensive computer simulations of the MWC system show that the proposed method improves the dynamic range performance of the MWC system significantly.
Open Access
Sub-band equalization of modulated wideband converter for improved dynamic range performance
(IEEE, 2017) Korucu, A. B.; Alp, Y. K.; Gök, Gökhan; Arıkan, Orhan
In this work, we propose a new method to improve the dynamic range performance of the Modulated Wideband Converter (MWC), which is multi-channel sampling system for digitizing wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. MWC achieves high dynamic range assuming that subband frequency responses of the system are identical. However, in hardware implementations of MWC, the resulting sub-band frequency responses are not identical and dynamic range performance of the system drops significantly which makes it unusable in practical applications. Proposed method iteratively designs FIR filters for equalizing frequency responses of the all sub-bands. Obtained results from the extensive computer simulations of the MWC system show that proposed method improves the dynamic range performance of the MWC system significantly.
Open Access
Time-frequency analysis of signals using support adaptive Hermite-Gaussian expansions
(Elsevier, 2012-05-18) Alp, Y. K.; Arıkan, Orhan
Since Hermite-Gaussian (HG) functions provide an orthonormal basis with the most compact time-frequency supports (TFSs), they are ideally suited for time-frequency component analysis of finite energy signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG function expansion provides optimal representation by using the fewest number of basis functions. However, for signal components whose TFS has a non-circular shape away from the origin, straight forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot provide reliable estimates to the individual signal components. To alleviate these problems, by using expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which identifies and transforms TFSs of individual signal components to circular regions centered around the origin so that reliable signal estimates for the signal components can be obtained. The HG expansion order for each signal component is determined by using a robust estimation technique. Then, the estimated components are post-processed to transform their TFSs back to their original positions. The proposed technique can be used to analyze signals with overlapping components as long as the overlapped supports of the components have an area smaller than the effective support of a Gaussian atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap region is larger than this threshold, the components cannot be uniquely identified. Obtained results on the synthetic and real signals demonstrate the effectiveness for the proposed time-frequency analysis technique under severe noise cases.