فصلنامه رادار
پیاپی 2 (زمستان 1392)

In this paper, we deal with the problem of clutter and direct signal (C&DS) cancelation in the target detection of TV-based passive radars. We first consider the target detection using the conventional FFT method. We see that for increasing the maximum range of passive radar, we must cancel the C&DS from the received signal. Hence, we investigate some important methods for C&DS cancelation in the TV-based passive radars using some evaluation criteria such as the ability for cancelation of undesired echoes, the effect on the target peak, detection performance, and the computational complexity. Afterward, we propose a new algorithm for C&DS cancelation based on the adaptive filter bank. Our investigations show that the proposed algorithm with much less complexity, behaves close to the ECA.

Due to the damaging effects of Speckle noise in accessing the information on radar images, reduction of these effects has been considered by many researchers. In this study, speckle reduction of radar images has been discussed based on curvelet transform. This paper describes an adaptive method of threshold estimation based on curvelet transform for removing speckle noise from Synthetic Aperture Radar (SAR) images. The estimation of the threshold value is carried out by analyzing the statistical parameters of the curvelet subband coefficients like arithmetic mean and geometric mean. In this algorithm first multiplicative speckle noise is transformed into an additive one by taking the logarithm of the original speckled image. Second curvelet transform is taken of logarithmically transformed image. Then based upon the statistical parameters of the curvelet coefficients of subbands, threshold values are found out. This threshold value is used in hard thresholding technique to remove the noisy curvelet coefficients. Then the inverse transform is applied to get the denoised image. Evaluation parameters like edge preservation factor, Equivalent Number of Looks, Mean Square Error and so on has been used for evaluating the performance of the proposed technique quantitatively. The results have been compared then to those obtained by other widely-used adaptive filters including Frost, Gamma, Kuan, Lee and hard thresholding wavelet based filters. The results of this comparison show that the proposed method offers better results than the above-mentioned filters (Mean Square Error, Normalized Mean Square Error and Mean Absolute Error indices are reduced 32%,32% and 5% respectively). In addition, the curvelet based filter is capable of preserving edges too.

This paper addresses the signal processing principles in passive bistatic radar (PBR) systems. In this case, the detection problem in passive radar systems based on FM radio signals is investigated in the presence of system noise, clutter/multipath, direct signal and interfering target signals. To this light, two types of detectors are studied. In the first one, which is based on intuitive sense and does not have any optimum claim, we examine two detectors. In the second type detector, which is based on detection theory, the detection problem is solved with optimum property out of invariant tests. For the first time, in this paper, the performance of above detectors based on detection probability and false alarm rate probability have been simultaneously compared. This makes it possible to find the advantages and disadvantages of two investigated detectors.

In recent years, Wavelet Packet Modulation OFDM (WPM-OFDM) signals have been introduced for radar applications. Using these signals results in significant properties such as desired inherent high range resolution and high resistance from radar system against jamming reception (Electronic Warfare) in radar systems. This paper investigates into the problem of designing these signals based on the following criteria; constrained maximization of the detection performance in the presence of colored Gaussian noise and clutter with the known covariance matrix and constrained minimization of the least squared error between desired and designed ambiguity functions. In other words, this issue can be formulated in terms of a non-convex multi-objective optimizing problem. In order to solve it, at first, the weighted sum method is used to change the original biobjective problem into a single objective or scalar one. Then, to find the optimal solution, the particle swarm optimization algorithm, which is simple and computationally efficient, will be employed.

Land cover classification is one of the most important applications of polarimetric radar images, especially in urban areas. There are numerous features that can be extracted from these images for the use of their high potential, hence feature selection plays an important role in PolSAR image classification. In this study, three main steps are used to improve the classification: 1) feature extraction in the form of three categories, namely original data features, target decomposition features, and SAR discriminators; 2) selection of minimum number of features to achieve the high classification accuracy; and 3) classification using the best subset of features. In the proposed methods, NSGA-II multiobjective optimization algorithm is employed as the search tool and Support Vector Machine (SVM) or Adaptive Neuro Fuzzy Inference System (ANFIS) is used in the evaluation step. The implementation results on the Radarsat-2 San Francisco Bay image showed that the proposed methods outperform the other approaches tested against them.

Synthetic Aperture Radar (SAR), which has been widely used for earth remote sensing, provides high resolution and day-and-night and weather-independent images for a multitude of applications. Design of SAR sensors has had some considerations for SAR systems issues such as microwave currents propagation, antenna, orbit design and etc.. Altitude and attitude errors of SAR sensor have a direct effect on Doppler centroid frequency. To avoid this effect, error of Doppler centroid frequency should be smaller than half of the Pulse Repetition Frequency (PRF). With analysis of standard deviation of Doppler centroid frequency, Maximum acceptable errors will be available to avoid ambiguity in Doppler centroid frequency. In this paper, as a novel approach, maximum acceptable errors behavior is analyzed and discussed comprehensively based on error propagation law beside their relations to minimum available Antenna area in order for optimum design of sensor.