فصلنامه رادار
سال دهم شماره 1 (پیاپی 27، پاییز و زمستان 1401)

In this research, the scattering of long-wavelength electromagnetic waves from an elliptical cross-section in two different structures and configurations are investigated. The plasma column is exposed to the magnetic field B0.. In the first structure, the plasma column is covered by a dielectric layer, and in the second structure, a dielectric core is coaxial with the plasma column and placed in its center, and the plasma column is covered by a dielectric layer. By solving the equations of continuity, momentum transfer and Poisson's equation, as well as by using the boundary conditions regarding the electric field and potential, we calculate the electric field in different regions. The scattering and radar cross section are calculated. The figures related to the resonance frequency , the electric field , the scattering and radar cross section are plotted

FDTD (Finite Difference Time Domain) numerical method is capable of modeling complex electromagnetic phenomena by calculating electric and magnetic fields using Yee's algorithm. In this method it is possible to model unbounded media using convolutional perfectly matched layer (CPML) absorber But the use of CPML for broadband applications faces a fundamental challenge, and that is the strong reflection of low-frequency harmonics from CPML absorber layers into the FDTD space; Therefore, it is necessary to design the parameters of the CPML absorber accurately in order to prevent the reflection of low frequency harmonics into the simulation space. In this paper, CPML parameters are designed by optimizing a cost function, which is based on reflection coefficient, cutoff frequency and attenuation of CPML layers. The optimization of this cost function is done using the artificial bee algorithm. Result of this algorithm are compared with a conventional values for CPML parameters, which shows more than 15 dB improvement of wave reflection from CPML layers. Also wave incidence modeling for Impulse signals is done using TFSF, so that the scattered fields can be calculated easily.

The main purpose of this article is to design and simulate an algorithm for the buried target from InSAR, and the purpose of this study is to start with the construction and analysis of the subsidence rate of the subway tunnel in the construction of Isfahan city.The main connections of InSAR for the construction and monitoring of subsidence of such goals are in determining the measuring time and reducing the correlation due to the change of the reflective characteristics of the geographical objects over time, which leads to the reduction of the interference coherence.In the design of this algorithm, an attempt has been made to increase the degree of coherence between the interference views by using the interferometer radar time series processing and the selection of widespread stable scatterers, as well as the optimal and continuous graphic design between the images under processing, which causes the stability of the phase of the distributed targets. Also, in order to reduce the amount of noise in the interference of views, in the stage before phase unwrapping, by applying a filter and suitable parameters to increase the signal to noise, in which the average coherence in the interference of the created views is 9% compared to other filters. It is closer to the number 1, which is the highest level of coherence, and in the end, in order to increase the accuracy and not to remove the regions with relatively low coherence, we will implement the optimal fuzzy unwrapping algorithm, which measures the average absolute value of the relative error in phase unwrapping by 12%. Finally it is clear that the subsidence of the target under study is different from the subsidence caused by natural causes such as underground water or drought and has a recognizable subsidence pattern.

This study presents the design and simulation of a 6-bit digital step attenuator utilizing 0.15 um GaAs for phased-array radar applications. The designed attenuator has a bandwidth of 1 GHz to 12 GHz. It has an LSB of 0.5 dB and a dynamic range of 31.5 dB. In addition, in order to reduce insertion loss, the widths of series switches have been made larger. After EM simulation, the insertion loss is better than -5.2 dB over the whole frequency spectrum. Using ADS 2015, EM simulations have been conducted. According to the data, the root mean square RMS of the amplitude and phase is less than 0.4 dB and 2.4 degrees, respectively. The designed attenuator's occupied area is 3,9 mm2.

Today, the importance of electronic warfare in the field of defense is not hidden. Among the various techniques of electronic warfare, chaff can play an effective role, which we discussed in this research. The chaff has many capabilities. Among these capabilities, it can be mentioned that it is ease to make and use, low cost jamming and wide band frequency coverage. It can also refer to deception in angle and distance, angle tracking error, and effect on the victim's radar in time, frequency, and polarization domain. Many features can be used for discrimination of chaff from target (ship). In this research, among the various features to discrimination it, the fluctuation feature, which is the variation of radar cross section, has been used. Also, in this research, in order to achieve better discrimination rate and less error, different deep learning algorithms and networks have been used. In addition, while we were able to discrimination the target of chaff based on deep learning, we achieved better recognition rate than the works that have been published so far. For example, in this study, we achieved a 15% improvement in performance compared to the resent published articles in the signal to noise ratio power of 6 dB and in the signal to noise ratio of 12 dB, the discrimination rate is 99.5%. Furthermore, we proposed the most suitable structure among the various deep learning structures in this application.

Transmitted signal waveform is among the most interesting factors affecting the radar system performance which is important to the development of cognitive radars. In this paper, the problem of adaptive waveform design is studied which specially addresses the waveforms that maximize the signal-to-disturbance ratio that finally leads to an increased probability of detection. The coded waveform is analysed in the class of linear inter-pulse coding and studied in the presence of coloured Gaussian disturbance. For this end, a new framework is introduced that leads to the design of an adaptive radar code in accordance with background distribution covariance matrix under a control on the region of available signal energy, achievable estimation accuracies and imposing a similarity constraint on the devised code periodic autocorrelation function. It is shown that the autocorrelation similarity constraint should be assessed after the whitening process in the optimum receiver. In more details, first waveform design is expressed as a non-convex quadratic optimization problem with a new autocorrelation (ACF) constraint in the whitened domain and then the optimization problem is solved by an innovative iterative algorithm named as GPP (Gradually Penalizing programming) with polynomial computational complexity. It is shown that the convergence of the algorithm is guaranteed and despite considering the suitability constraints of the ACF, improved detection performance can be achieved compared to other code synthesis methods.

Subsidence is a very destructive and dangerous phenomenon that, in addition to endangering human life, can also damage the infrastructure of cities. One of the reasons for its creation is the uncontrolled extraction of groundwater, which occurs widely in the plains of Iran. The time Series InSAR method is one of the important methods for accurate and continuous monitoring of subsidence. But the main problem with this method is the removal of pixels with low correlation in the processing cycle. In this study, to overcome this problem, subsidence of Rafsanjan plain has been investigated using the improved SBAS time series method based on coherence. The data used are 15 images of SENTINEL-1 satellite related to the period from October 2015 to October 2016 and 50 interferograms are generated. The results show the ability of this method to use all pixels of the interferogram, even pixels related to vegetation areas with low correlation. The highest subsidence rate was 284 mm per year for Rafsanjan-Bahrman plain and 252 mm per year for Rafsanjan-Kashkoyeh plain along the satellite line of sight. To investigate the relationship between the improved SBAS results and the water level of wells in the region, Pearson correlation coefficient was used, and to model the relationship, a linear regression model was used. The results indicate a strong direct linear relationship. Also, the linear regression model has the ability to model the relationship with a 95% confidence level. Analysis of variance (ANOVA) was used to test the significance of the linear regression model and Durbin–Watson test was used to evaluate the autocorrelation in the residuals. The results confirm the significance of the model and the independence of the observations.

Classical methods in array processing focus on increasing the separability of near-source sources in noise conditions when the wave propagation is in a flat environment. Among the classical methods, MUSIC and its modification are more common and have received more attention. This paper presents a method for detecting sound sources for passive arrays. First, the classical MUSIC algorithm is described. Then, based on the basic points in this method, the proposed idea is introduced. The idea is to add a virtual source with a desired angle that is independent of the sources and their noise to the data obtained from the sensors. This virtual resource is independent of the sources and their noise. Adding this source at the desired angle increases the order of the covariance matrix by one unit.by changing the angle of the virtual source, wherever its angle is aligned with the angle of one of the main sources, the order of the covariance matrix decreases from the increased value to the actual value. Finally, the idea is simulated for a passive five-element array for both independent and coherent sources to demonstrate detection quality.

In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been cited as one of the most important applications of images classification. Therefore, in order to achieve the best result of PolSAR image classification in this article, a new feature selection method will be proposed based on mutual information theory. In the proposed method, the features that are extracted from PolSAR images will be used to obtain an initial class map. Then, each feature will be ranked based on mutual information. In the next step, the best features will be selected by using the proposed method accurately. The results that are obtained on the real PolSAR image of the Flevoland area prove an increase in the classification accuracy of the proposed method compared with other methods that are used in this research.

The main goal of this article is to investigate the parameters and components influencing the detection of a buried target by a static airborne radar platform by means of loop propagation modeling. In this modeling, the dimensions of the antenna coverage range on the surface and subsurface have been calculated by taking into account all the effective parameters, which allows us to use the radiative transfer theory and determine the amount of input current in each environment, the scattered and absorbed power obtained in the medium. One of the important points in the applications of this type of airborne radars is the proper selection of the central frequency and the frequency bandwidth required for better detection. In this paper, how to choose the parameters of radar design and compromise between having penetration depth and having the resolution are properly presented using delimited diagrams that can be placed as a predefined table in radar memory. Also, with the arrangement of several scenarios, the amount of return signal from the buried target has been measured and verified in CST and GPR_Sim software, and through equations, the depth of its placement has been revealed with an acceptable accuracy of less than 2%.

In this paper, a new charge pump in CMOS technology is designed and implemented. By using the body bias to reduce the threshold voltage and controlling the voltage by using the voltage reference circuit, a DC to DC voltage booster circuit with high efficiency has been achieved to provide an input voltage of 320 millivolts and an output voltage of more than 1 volt. Simulations have been done so that the proposed voltage booster circuit can be analyzed in a state where the results are close to the fabrication results. The results indicate that compared to previous works, smaller capacitors have been used to reduce the area of the circuit as well as high output voltage by using the minimum number of transistors. The simulation results with 25 pF charge pump capacitor in each clock pulse frequency of 200 kHz show that the input voltage of 350 millivolts reaches 1.2 volts in less than 2 milliseconds. The proposed circuit can be suitable for radar and RFID applications with low power consumption.

Due to developments of radars in changing parameters, pulse separation methods in ELINT systems have relied on intra-pulse modulation instead of analyzing common pulse description words. Number of radars in this method, may be estimated incorrectly because a modern radar with the ability in changing intra-pulse modulation type may be detected as multiple radars.The purpose of this paper is to detect multi-mode radars with various types of internal modulation in a dense radar environment. The proposed’solution is to add multi-mode radars detection to the existing pulse separation methods. In this method, by extracting the distinguishing features of each radar, similarity criteria of each feature are calculated to examine the similarity between the two pulse streams. Input data are features that  extract of  real data of a ELINT system with traditional parameters and developed parameters related to the pulse shape provided by a simulator. The dataset is then used to train a LVQ neural network to discriminate between different and multi-mode radars. After training the network, in the new operating conditions, a multi-mode radar’s pulse streams is classified as a single radar.The simulation results show a higher accuracy for classification with the similarity criteria of developed features than the criteria extracted of  classical data at different SNRs. Also, the increase in classification accuracy in single-layer perceptron and multi-layer neural network at SNR equal to 0.5 dB has been shown. In similar articles, only multi-mode radars with the ability to change frequency and PRI have been investigated, but with the proposed method, radars with the ability to change several parameters along with the type of intra-pulse modulation can be identyfied.The innovation of the article is the development of distinguishing features and the idea of ​​similarity criteria for the detection of multi-mode radars.