Sharif Digital Repository

In today’s world, knowledge of the statistical behavior of noise can tremendously affect the accuracy of target detection in radar systems. Therefore, radar systems commonly collect a secondary dataset of homogeneous noise and estimate the statistics of the gathered data, prior to attempting target detection. Specifically, in the case of Gaussian noise with a mean of zero, the entire statistical information of the noise is encoded in its covariance matrix. In practice, however, the challenge is that the training samples do not purely contain homogeneous noise. In fact, some samples contain non-homogeneous outlier signals that do not have the same distribution as the noise samples. In this... 

The resolution is one of the most essential factors in radar imaging. The range and cross-range resolution are inversely proportional to the radar bandwidth and the angular width, respectively. Increasing the bandwidth requires upgrading the hardware of radars and the cost of this procedure is high. Also, to increase the angular width, we need more time to scan the scene from different angles but due to reasons such as movements of targets in the scene, it is unachievable.According to these limitations, we can utilize some signal processing techniques to Improve the radar resolution. Super resolution algorithms are the common approaches to achieving this goal. In this research, we proposed a... 

In this research, we design a compatible waveform for the purpose of high-quality synthetic aperture radar (SAR) imaging in conjunction with sparse recovery methods for image formation. The goal is to make the imaging system tolerable against the wide-band and narrowband electromagnetic interferences. Actually, we consider minimizing the mutual interference between our radar and coexisting licensed emitters and minimizing the jamming signal (unlicensed emitters) power while enforcing some constraints over the waveform features like peak-to-average-power ratio (PAPR). For the constrained optimization problem to design a proper waveform, we introduce a new constraint to the optimization... 

The problem of joint design of transmit code and receive filter is considered in many application scenarios of multiple input multiple output (MIMO) radar systems. The performance of the joint design problem is evaluated with the signal-to-interference ratio (SINR) metric in the presence of noise. In such problems, the optimization problem is to maximize SINR on the receiver side with the constraints that are applied on the transmitted waveform. Our proposed method, RTR, is a manifold-based geometric method that performs better than the SDP method in terms of algorithm execution speed and calculation complexity. For simulation, we took sampled and real TIR with Target Aspect Angle (TAA) in a... 

Multistatic radar has several advantages over monostatic radar (such as better detection), which are due to the use of different viewing angles and the difference in their clutter characteristics. Clutter in many applications (such as marine applications) has the property of being sparse in certain dictionaries. Therefore, the investigation of sparse clutter (such as sea clutter) is of particular importance. It is worth noting that the detection of targets in the vicinity of the sea faces difficulties due to the dynamics of the sea, which causes the Doppler spectrum to change with time and change in space. Considering the fact that the sea clutter is sparse clutter, one of the powerful... 

In this thesis, we address the problem of joint Range, Angle and Doppler frequency estimation in FDA-MIMO radars. Target parameter estimation is among the most important areas of research in radar signal processing. In this field of research, there are numerous challenges to overcome, including low estimation accuracy, a scarcity of snapshots, and the high computational complexity of algorithms. For this reason, diverse methods have been developed to solve this problem. Each of these methods has its own features and capabilities, focusing on one or more of the existing challenges. The application of these methods also depends on various factors, including the signal structure, which is... 

Data analysis is the science of extracting human-understandable knowledge from data. Massive data analysis is usually associated with the challenges of Volume, Velocity, Variety, Veracity and Value. These challenges are often referred to as 5V. With increasing processing power and reducing its cost in the last decade, massive data analysis methods have been used in various fields such as market, insurance and health, telecommunications and network, web search engines, intelligent transportation, etc. In this dissertation, we have tried to extract applied knowledge by using massive data analysis platforms and algorithms as well as traffic data. In this dissertation, a mathematical model for... 

The purpose of Direction-of-arrival(DOA) estimation in MIMO communication networks is to find the direction of signals that are mixed together and mixed with noise by using an array of multiple receiving antennas. DOA estimation is an important issue in array signal processing. The traditional methods for DOA estimation try to solve this problem with mathematical algorithms. For example, some of these methods are based on parameter estimation; such as DOA estimation methods using maximum likelihood estimation methods. Some of other traditional methods are based on subspace decomposition; Like the famous MUSIC method. The traditional methods of DOA estimation usually have a drawback, and that... 

Localization of radio sources has various applications in civil and military communications. Some of important localization methods consist of Angle of Arrival (AoA) estimation of the illuminated signals from the interested sources. Between various Direction Finding (DF) methods, subspace based methods are of special importance, because of their high resolution and accuracy. And from beginning of invention, because of new challenges, have been focus of numerous researches. In this thesis, first we explain the most important subspace based DF methods called MUSIC and root-MUSIC. Then a novel modification for root-MUSIC is introduced. In our new method the computational complexity of... 

During the last few years, the study of urban environment structures is considered as a research field of interest in remote sensing. In satellite observations of the earth's surface, continuous imaging in terms of time and space has caused the remote sensing technique to be proposed as a useful and efficient tool for the analysis of urban areas. Obtaining quantitative spatial information from the urban environment in fields such as determining the height of buildings plays an essential role in urban planning, monitoring damage to buildings, establishing communication bases and digital cities. During the last two decades, the use of Tomosar approach in order to reconstruct the structures of... 

By generalizing dictionary learning (DL) algorithms to Multidimensional (MD) mode and using them in applications where signals are inherently multidimensional, such as in three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging, it is possible to achieve much higher speed and less computational complexity. In this thesis, the formulation of the Multidimensional Dictionary Learning (MDDL) problem is expressed and six algorithms are proposed to solve it. The first one is based on the method of optimum directions (MOD) algorithm for 1D dictionary learning (1DDL), which uses alternating minimization and gradient projection approach. As the MDDL problem is non-convex, the second...