JOURNAL OF RADAR
Year:2013 | Volume:1 | Issue:1 (SERIAL NO. 1)|Papers Count:6

Due to the tilt of the platform carrying the Synthetic Aperture Radar (SAR), the image formation algorithms (like RDA) can not yield good quality images. Therefore, it is convenient to use a two-step approach for motion compensation in image formation algorithms. In this paper, using an approximated linear model for the motion error and the impact of range compression step of RDA on the differential error, it is shown that the required phase is changed in the differential compensation, and so it is considering to yield more qualified images. Then the proposed method accompanying with an intermediate stage of compensation is evaluated during the compression range for more reform of range migration. Finally the simulation results are presented to show that the projected method has better performance compared with the conventional two-step procedure.

In this paper, an unsupervised classification method using spatial contextual information is proposed for polarimetric SAR (PolSAR) image categorization. First, an unsupervised classification based on H/Alpha plane was performed, using Cloude/Pottier target decomposition algorithm. Then, the output of the H/alpha classification have been considered In order to compute the initial values of the cluster centers and hence a rapid convergence of the algorithm. After that, using discriminant function derived from Bayes Theory, the classification was carried out based on MAP criteria. In the MAP criteria, the Wishart distribution was used as the distribution of the PolSAR data. We also employed Markov random field algorithm for modeling the spatial information to calculate prior probability of classes. In order to enhance the classes' separability, two image data from two different seasons were utilized simultaneously. The overall accuracy of classification was achieved respectively 70% and 82% for the two methods of Wishart and MAP criteria (WMRF). In this study, the RADARSAT-2 satellite images were gotten from a forested area known as Petawawa, Canada.

In this article, a new method is presented for fusion of high resolution optical and radar data for higher quality road extraction. The proposed methodology consists of two stages. The first stage includes road detection separately from each data and the second stage is knowledge based fusion of images. Neural networks are separately applied on high resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. Knowledge based fusion method is then carried out using some thresholds about gray level of narrow roads and vegetation. In order to evaluate the result accuracy assessment parameters including RCC, BCC and RMSE (Root Mean Square Error) were estimated as 79.42%, 93.51% and 0.27, respectively. The results obtained show the high efficiency of the proposed method.

According to the advantages of passive radars, they have become more popular recently due to development of signal processing technology. To the knowledge of authors, few researches have been reported in the field of sea targets detection using passive radar. This paper introduces proper satellite radiations which can be used as transmitter of opportunity for sea target detection. It is well known that the radar cross section of sea targets in a cell is considerably more than the sea clutter RCS in adjacent cells. Using this fact, this paper presents a new method to detect sea targets using adaptive filter weights variation model. Finally, the performance of proposed method is compared with that of traditional methods. Experimental results show the effectiveness of our new method.

One of the problems in detecting radar targets is the variation of target cross section due to aspect angle change; which is named as "Target Fluctuation". This primarily depends on target geometry and transmitted wavelength and may cause significant loss of detection. A method to deal with this challenge is improving the radar range resolution using stepped frequency signals. In this paper, after introducing this kind of radar signal and explaining how to access high resolution, some techniques have been investigated for improving fluctuating targets detection in the stepped frequency radars. To do so, three distinct methods have been firstly presented for modeling range distributed targets. Then two detectors have been suggested for detecting these kinds of targets. Finally, it is shown that the distribution of target as the result of high range resolution can fully compensate the target fluctuation loss.

The possibility of terahertz generation has been investigated from a photoconductive switch based on GaAs, gated by a femtosecond laser. The emission properties of photoconductive antenna (PCA) have been studied with low-temperature-grown (LT) GaAs. In such GaAs materials, presence of the charged defects induces a redistribution of the electric field between the antenna electrodes. This effect has a huge influence on the amplitude of the radiated terahertz field. In this work, we demonstrate that carrier concentration enhances radiation power. Results obtained as a function of the laser excitation power (with 100 fs rise time and 1 KHz repetition rate) and carrier concentration are discussed and a comparison of the performance of these devices is given with a conventional antenna-type (dipole) emitter.