Browsing by Subject "Radar"

Now showing 1 - 13 of 13

Open Access
Approximate computation of DFT without performing any multiplications: application to radar signal processing
(IEEE, 2014) Arslan, Musa Tunç; Bozkurt, Alican; Sevimli, Rasim Akın; Akbaş, Cem Emre; Çetin, A. Enis
In many radar problems it is not necessary to compute the ambiguity function in a perfect manner. In this article a new multiplication free algorithm for approximate computation of the ambiguity function is introduced. All multiplications (a × b) in the ambiguity function are replaced by an operator which computes sign(a × b)(a + b). The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of correlations and DFT computations. This new additive operator enables an approximate computation of the ambiguity function without requiring any multiplications. Simulation examples involving passive radars are presented.
Open Access
Autofocused spotlight SAR image reconstruction of off-grid sparse scenes
(Institute of Electrical and Electronics Engineers Inc., 2017) Camlıca, S.; Gurbuz, A. C.; Arıkan, Orhan
Synthetic aperture radar (SAR) has significant role in remote sensing. Phase errors due to uncompensated platform motion, measurement model mismatch, and measurement noise can cause degradations in SAR image reconstruction. For efficient processing of the measurements, image plane is discretized and autofocusing algorithms on this discrete grid are employed. However, in addition to the platform motion errors, the reflectors, which are not exactly on the reconstruction grid, also degrade the image quality. This is called the off-grid target problem. In this paper, a sparsity-based technique is developed for autofocused spotlight SAR image reconstruction that can correct phase errors due to uncompensated platform motion and provide robust images in the presence of off-grid targets. The proposed orthogonal matching pursuit-based reconstruction technique uses gradient descent parameter updates with built in autofocus. The technique can reconstruct high-quality images by using sub Nyquist rate of sampling on the reflected signals at the receiver. The results obtained using both simulated and real SAR system data show that the proposed technique provides higher quality reconstructions over alternative techniques in terms of commonly used performance metrics.
Open Access
Cross-term-free time-frequency distribution reconstruction via lifted projections
(Institute of Electrical and Electronics Engineers, 2015-01) Deprem, Z.; Çetin, A. Enis
A crucial aspect of time-frequency (TF) analysis is the identification of separate components in a multicomponent signal. The Wigner-Ville distribution is the classical tool for representing such signals, but it suffers from cross-terms. Other methods, which are members of Cohen's class of distributions, also aim to remove the cross-terms by masking the ambiguity function (AF), but they result in reduced resolution. Most practical time-varying signals are in the form of weighted trajectories on the TF plane, and many others are sparse in nature. Therefore, in recent studies the problem is cast as TF distribution reconstruction using a subset of AF domain coefficients and sparsity assumption. Sparsity can be achieved by constraining or minimizing the l(1) norm. In this article, an l(1) minimization approach based on projections onto convex sets is proposed to obtain a high-resolution, cross-term-free TF distribution for a given signal. The new method does not require any parameter adjustment to obtain a solution. Experimental results are presented.
Open Access
Manoeuvring-target tracking with the Viterbi algorithm in the presence of interference
(IET, 1989) Demirbaş, K.
The Viterbi algorithm is used to track a target in the presence of random interference, such as jamming. A nonlinear target motion and an observation which is modelled in a spherical coordinate system are considered. The observation model is a nonlinear function of interference. The components of the state vector are the range, bearing angle, and elevation angle of the target location. The state vector is estimated, component-by-component, by a parallel use of the Viterbi algorithm in blocks. Simulation results, some of which are presented, have shown that the proposed estimation scheme performs well, whereas classical estimation schemes, such as the extended Kalman filter, cannot, in general, handle target tracking in the presence of random interference.
Open Access
Multiperson tracking with a network of ultrawideband radar sensors based on gaussian mixture PHD filters
(Institute of Electrical and Electronics Engineers Inc., 2015) Gulmezoglu, B.; Guldogan, M. B.; Gezici, Sinan
In this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach. © 2014 IEEE.
Open Access
Optimum Power Allocation for Average Power Constrained Jammers in the Presense of Non-Gaussian Noise
(Institute of Electrical and Electronics Engineers, 2012-08) Bayram, S.; Vanli, N. D.; Dulek, B.; Sezer, I.; Gezici, Sinan
We study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman-Pearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results.
Open Access
A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction
(IEEE, 2010) Uğur, Salih; Arıkan, Orhan
A new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated as a solution to an optimization problem in terms of the positions of the reflectivity centers of the target. Particle swarm optimization is used to obtain phase error estimates efficiently. The quality of the reconstructions is demonstrated by using simulation studies. © 2010 IEEE.
Open Access
Pasif bistatik radarlarda seyreklik temellli ters evrişim kullanılarak hedef tespiti
(IEEE, 2015-05) Arslan, Musa Tunç; Tofighi, Mohammad; Çetin, A. Enis
Bu bildiride pasif radar (PR) sistemlerinin menzil çözünürlüğünü artırmak için seyreklik tabanlı bir ters evrişim yöntemi sunulmaktadır. PR sistemlerinin iki boyutlu uyumlu süzgeç çıktısı bir ters evrişim problemli gibi düşünülerek incelenmektedir. Ters evrişim algoritması, hedeflerin zaman kaymaları ve l1 norm benzeri dışbükey maliyet fonksiyonlarının epigraf kümelerini temsil eden hiperdüzlemler üzerine izdüşümü temellidir. Bütün kısıt kümeleri kapalı ve dışbükey olduklarından dolayı yinelemeli algoritma yakınsamaktadır. FM tabanlı PR sistemleri üzerinde benzetim sonuçları sunulmuştur. Algoritma frekans uzayı tabanlı ters evrişim yöntemlerine göre daha yüksek performansa sahiptir.
Open Access
Pulse doppler radar target recognition using a two-stage SVM procedure
(IEEE, 2010-07-07) Eryildirim, A.; Onaran, I.
It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented.
Open Access
Range resolution improvement in passive bistatic radars using nested FM channels and least squares approach
(SPIE, 2015) Arslan, Musa Tunç; Tofighi, Muhammad; Sevimli, Rasim Akın; Çetin, A. Enis
One of the main disadvantages of using commercial broadcasts in a Passive Bistatic Radar (PBR) system is the range resolution. Using multiple broadcast channels to improve the radar performance is offered as a solution to this problem. However, it suffers from detection performance due to the side-lobes that matched filter creates for using multiple channels. In this article, we introduce a deconvolution algorithm to suppress the side-lobes. The two-dimensional matched filter output of a PBR is further analyzed as a deconvolution problem. The deconvolution algorithm is based on making successive projections onto the hyperplanes representing the time delay of a target. Resulting iterative deconvolution algorithm is globally convergent because all constraint sets are closed and convex. Simulation results in an FM based PBR system are presented.
Open Access
Successive cancelation approach for doppler frequency estimation in pulse doppler radar systems
(IEEE, 2010) Soğancı, Hamza; Gezici, Sinan
In this paper, a successive cancelation approach is proposed to estimate Doppler frequencies of targets in pulse Doppler radar systems. This technique utilizes the Doppler domain waveform structure of the received signal coming from a point target after matched filtering and pulse Doppler processing steps. The proposed technique is an iterative algorithm. In each iteration, a target that minimizes a cost function is found, and the signal coming from that target is subtracted from the total received signal. These steps are repeated until there are no more targets. The global minimum value of the cost function in each iteration is found via particle swarm optimization (PSO). Performance of this technique is compared with the optimal maximum likelihood solution for various signal-to-noise ratio (SNR) values based on Monte Carlo simulations.
Open Access
Target detection and classification in SAR images using region covariance and co-difference
(SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.
Open Access
Three-dimensional FDTD modeling of a GPR
(IEEE, 2000) Oğuz, Uğur; Gürel, Levent
The power and flexibility of the Finite-Difference Time-Domain (FDTD) method are combined with the accuracy of the perfectly-matched layer (PML) absorbing boundary conditions to simulate realistic ground-penetrating radar (GPR) scenarios. Three-dimensional geometries containing modes of radar units, buried objects and surrounding environments are simulated. Simulation results are analyzed in detail.