Emanuel Radoi

Some radar imagery results using superresolution techniques

The key problem in any decision-making system is to gather as much information as possible about the object or the phenomenon under study. In the case of the radar targets, frequency and angular information is integrated to form a radar image, which has very high information content. Salient features can then be extracted in order to characterize or to classify radar targets. The quality of the reconstructed image is mainly related to the resolution performed by the radar system both in slant range and in cross range. The Fourier-based reconstruction methods are fast and robust, but they are limited in resolution and dynamic range. Subspace eigenanalysis based methods, such as multiple signal classification (MUSIC) or estimation of signal parameters by rotational invariance techniques (ESPRIT), are able to provide superresolution and to accurately recover the scattering center locations even for a small number of correlated samples. The aim of the paper is to present some results of our ongoing research on the application of these techniques for radar imagery.

Multiple-Antenna-Based Blind Spectrum Sensing in the Presence of Impulsive Noise

Cognitive radio (CR) was proposed as a solution to the spectrum scarcity problem. One of the basic functions of any CR is spectrum sensing. Most existing works on spectrum sensing consider the Gaussian noise assumption. In practice, this assumption is not always valid since several existing noise types exhibit non-Gaussian and impulsive behavior. Hence, it is very beneficial to study spectrum sensing in the presence of impulsive noise. In this paper, we propose two new multiple-antenna-based spectrum sensing methods, assuming that the underlying noise follows a symmetric α-stable distribution. This assumption is justified by a distribution fitting of some measurements of the noise acting on the GSM-R antennas onboard trains. The first proposed sensing method is based on the covariation properties of α-stable processes, whereas the second proposed method has the strategy of filtering the corrupted signals before applying a traditional spectrum sensing method. These two methods do not require a priori knowledge about the primary-user signal. Simulation results show that the proposed algorithms provide good spectrum sensing performance in the presence of α-stable distributed impulsive noise.

Superresolution algorithms for spatial extended scattering centers

Scattering centers model (SCM) is usually considered for modeling target backscattered signal in high-resolution radar. In this case the impulse response of each scattering center is represented by a time-delayed Dirac pulse. Some of most popular superresolution imagery techniques, such as MUSIC or ESPRIT, are well-matched to this model. Under this hypothesis, they outperform Fourier-based techniques in terms of both spatial and dynamic resolutions. However, the behavior of real-world targets is often very different from that of the SCM. Indeed, their reflectivity function is not confined just to several perfectly localized scattering centers, but it can be rather approximated by a set of scattering regions having different spatial extent. SCM becomes then inappropriate and the superresolution methods may provide unexpected results. Furthermore, the amplitude information is difficult to interpret in this case. In this paper we propose an extension of two superresolution methods, MUSIC and ESPRIT, to cope with extended scattering centers (ESC). According to this model, the impulse response of an ESC is not a Dirac pulse, but a window of finite support. Besides the position, the size (spatial extent) of this window is also recovered. This additional information about the target structure can be used for increasing ATR (automatic target recognition) accuracy and robustness.

Supervised self-organizing classification of superresolution ISAR images: an anechoic chamber experiment

The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN ( nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

Overlap-Save and Overlap-Add Filters: Optimal Design and Comparison

Overlap-save (OLS) and overlap-add (OLA) are two techniques widely used in digital filtering. In traditional OLS and OLA implementations, the system is compelled to be time-invariant and conventional filter synthesis techniques are used for designing the block filter. In this paper, based on the OLS and the OLA structures, we develop a fast algorithm for designing the optimal OLS and OLA block filters using a quadratic criterion. Comparing OLA to OLS optimal design, we demonstrate that, as in classical design approaches, they show no difference when the filters are time-invariant. However, when aliasing is not zero, although the global aliasing is the same, its components with respect to frequency are different. This conclusion is supported by simulation results, and a comparison between the optimal approach and some other standard approaches is also provided.

Non-parametric multiple-antenna blind spectrum sensing by predicted eigenvalue threshold

In this paper, we consider the problem of sensing a primary user in a cognitive radio network by employing multiple antennas at the secondary user. Among the many spectrum-sensing methods, the predicted eigenvalue threshold (PET) based method is a promising non-parametric blind method that can reliably detect the primary users without any prior information. Then, a simplified PET sensing method, which needs to compare only one eigenvalue to its threshold, is introduced. Compared with the original PET sensing algorithm, the simplified algorithm significantly reduces the computational complexity without any loss in performance. A performance comparison between the proposed method and other existing methods is provided.

An overview of synchronization algorithms for IR-UWB systems

Ultra wideband (UWB) radio has emerged as an attractive candidate for short-range wireless communications in recent years due to its unique features. However, implementation of UWB radios in order to utilize these features is coupled with some pronounced design challenges. To achieve precise timing synchronization is one of the most crucial task among them and is a key factor to ensure a reliable performance of such systems. In this paper, we present a brief review of IR-UWB synchronization algorithms which are based on either correlation or code matching or energy detection. The performance is then analyzed under realistic propagation environment that takes into account the severe multipath, inter-frame interference (IFI) & inter-symbol interference (ISI) and multi-user interference (MUI).

Energy detection based blind synchronization for pulse shape modulated IR-UWB systems

Synchronization is a key performance-limiting factor in any communication system and a challenging task to accomplish. In this paper, an energy detection based non data-aided (NDA) algorithm for orthogonal pulse shape modulated (PSM) impulse radio ultra wideband (IR-UWB) system is proposed. Relying on unique signal structure, simple overlap-add operation followed by energy detection enables synchronization. The algorithm remains functional under practical scenarios i.e. in the presence of inter-frame and inter-symbol interference (IFI & ISI) and with M-ary modulation. Simulation results are provided to demonstrate the performance of proposed algorithm.

Polynomial phase signal modeling using warping-based order reduction

The high-order ambiguity function (HAF) was introduced for the estimation of polynomial-phase signals (PPS). Currently the HAF suffers from noise-masking effects and from the appearance of undesired cross terms in the presence of multi-components PPS. The multi-lag product HAF concept was then proposed as a way to improve the performance of the HAF. Nevertheless, the performance of the new methods are affected by the error propagation. This effect is due to the technique used for polynomial order reduction, common for current approaches: signal multiplication with the complex exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the polynomial order, based on the successive unitary signal transformation, according to each polynomial order. We prove that this method considerably reduces the effect of error propagation.

Multiple-Votes Parallel Symbol-Flipping Decoding Algorithm for Non-Binary LDPC Codes

A novel decoding algorithm for non-binary low density parity check (NB-LDPC) codes is proposed. The algorithm builds on the recently designed parallel symbol-flipping decoding (PSFD) algorithm and combines a technique of error estimation and a method of multiple voting levels from each unsatisfied check-sum to the corresponding variable nodes. Simulations results, performed on a number of NB-LDPC codes of various lengths and column weights constructed using several methods, show that the new algorithm not only avoids using code-dependent voting threshold but also improves the error rate performance of the PSFD algorithm, particularly for low column weight parity-check matrices.