فصلنامه رادار
سال چهارم شماره 2 (پیاپی 12، تابستان 1395)

Space-time adaptive processing is a useful technique for clutter mitigation in airborne radars. space-time adaptive processing algorithms usually require estimation of interference covariance matrix with limited training data and high complexity in processing. To overcome these problems, in this paper, deterministic clutter subspace and the direct data domain method are introduced. Two methods are presented. In the first proposed method, filter coefficients are calculated by estimating airborne radar parameters and also calculation of interference covariance matrix. The sensitivity of estimated parameters is also computed. By extending clutter subspace, the suggested method could get more robust against parameter estimation. In the other proposed method, the direct data domain is employed for calculating the coefficients vector by reducing the size of filters. Finally, the performance of the suggested algorithms is improved by utilizing other methods such as using more taps and deterministic clutter subspace.

See Through the Wall Radar, is a relatively new small range radar that uses electromagnetic waves to detect targets behind the wall. Because of its symmetry, a conducting sphere is often used as a reference scatterer to calibrate radars and measure the scattering properties of other radar targets. In this paper, in order to simulate the received signal for See- Through- the- Wall radar, the scattering model of a conducting sphere in free space is presented. Then using a dielectric sheet to represent the wall between the radar system and the sphere behind it, the scattering model of a sphere behind the wall is developed. To create the image of the conducting sphere behind the concrete wall using the signals acquired with the proposed model, the Range Migration Algorithm (RMA) and the coherent background subtraction are used and, as a result, an image of the sphere behind the wall has been achieved. Simulation results reveal the suitability of the proposed model for See-Through-the-Wall Radar received signal simulation.

Speckle noise seriously degrades the quality of SAR images and complicates the image exploitation using automated image analysis techniques. Recently, the application of compressed sensing (CS) is explored in the SAR signal processing. In this paper, first, a linear model is derived for speckled SAR data. Then, using this model and the compressed sensing theory, a speckle reduction method is proposed. In the proposed method, the image backgrounds as well as the bright point targets are also reconstructed together with noise reduction. The important feature of the proposed method is the joint noise reduction simultaneous with the SAR image formation. Moreover, using simulated and real SAR images, the performance of the proposed method in noise reduction and preserving image features is evaluated and compared to the performance of some de-noising approaches.

In this paper, a band-pass filter with cutoff frequencies 1.7GHz and 11.5GHzis is designed and fabricated. To do this task, first, two filters, a low pass and a high pass with respective cutoff frequencies of 1.7 GHz and 11.5GHz are designed and, second, they are combined in series. Then cutoff frequencies are set independently. That is, designing a cutoff frequency is not interfered with the other one. At the end, the serial combination of the two filters is fabricated on a Ro4003 substrate with thickness of 32mil and εr=3.38. The simulation and measurement results are in agreement.

S transform is a time-frequency transform created based on the concept of short-time Fourier transform and wavelet transform. The main property of this transform that separates it from the discrete short-time Fourier transform is the variable-frequency resolution. But in this transform, the resolution change of high frequencies to low frequencies is very significant. So another transform as a Modified S Transform(MST) was introduced to adjust a parameter that controls the rate of change in resolution. In this paper, S and MST has been first discussed.Then, on the basis of the MST transform,the Optimum S Transform (EST) was introduced with a new parameter. Next, calculating the length of the used window, the dependence of it to the proposed parameters was studied and the effect of each of the parameters on the rate of changing the length of window was illustrated in theory and simulation. Finally, with simulation, the performance of the proposed method for detection of FMCW signals was examined , and the effect of each of the parameters on the detection of above signals was shown. Then the better performance of the proposed method than two last methods was proved.

In the SAR image processing, with the use of computers, it is important to choose the algorithm with regard to the type of data. The previously used algorithms in this field, lacked acceptable recognition and had inappropriate speed. The main purpose of this paper is to present an appropriate algorithm to recognize the targets in SAR images which is used by aircrafts, planes, and satellites that observe the target on the ground. It is tried to choose an algorithm that is more adaptive to the distribution of local optima and the proportion of the public data. With regard to the distribution of data, PSO algorithm is relatively suitable considering the point that the most important shortcoming of PSO is to get stuck at local optimum. Accordingly, to remove this shortage GAPSO algorithm is presented in this paper. The results show that the suggested method enjoys more speed and accuracy in comparison with the other suggested methods.