Sharif Digital Repository

The Savitzky-Golay (SG) filtering is a widely used denoising method employed in different applications. The SG filter has two design parameters: the window length and the filter order. As the window length increases, the estimation variance is reduced, but at the same time, the bias error is increased. In this article, we obtain the optimal window length of an SG filter with an arbitrary order, based on minimizing the mean square error (mse), a well-known performance measure considering both the estimation bias and variance. To achieve the optimal window length, we propose an algorithm the performance of which is much better than the existing methods. In this article, we follow the viewpoint... 

In this paper, we focus on the moving target localization problem in a multiple-input multiple-output radar with widely separated antennas. By exploiting jointly different types of information including time delay, Doppler shift and azimuth and elevation angles of arrival, we develop an algebraic closed-form two-stage weighted least squares solution for the problem. The proposed algorithm is shown analytically to attain the CramerRao lower bound accuracy under the small Gaussian noise assumption. Numerical simulations are included to examine the algorithm's performance and corroborate the theoretical developments  

In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information matrix (FIM), which is equivalent to minimizing the error ellipse area. The square root of the average determinant of the FIM can be expressed as a product of three parameters, namely the equivalent single radar gain, coherency gain and geometry gain. It is shown that the coherency gain of... 

Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of well-known convexity/concavity properties in the continuous domain. Submodular functions exhibit strong structure that lead to efficient optimization algorithms with provable near-optimality guarantees. These characteristics, namely, efficiency and provable performance bounds, are of particular interest for signal processing (SP) and machine learning (ML) practitioners, as a variety of discrete optimization problems are encountered in a wide range of applications. Conventionally, two general approaches exist to solve discrete problems: 1) relaxation into the continuous domain to obtain an... 

The sensor selection is a technique to reduce the cost, energy consumption, and complexity of a system by discarding redundant or less useful sensors. In this technique, one attempts to select a subset of sensors within a larger set so as to optimise a performance criterion. In this work, the authors consider the detection of sources in the 3D space when the source space could be sparsely represented. The application of compressive sensing (CS) methods in this setting has been extensively studied. Their aim in this work is to carry out the task of sensor selection in a planar array without sacrificing the detection performance. Their approach is to adopt the equivalent CS model and reduce... 

In this letter, we surpass the jamming effect and also estimate the jamming channel state information by using Kalman filtering (KF) approach. Furthermore, we design a closed-form beamforming weight for maximizing the achievable rate subject to the condition that the total relays power transmission would be below a predefined threshold level, which is formulated as a Quadratically Constrained Quadratic Program (QCQP). Simulation results show efficient performance of the proposed method for different destruction powers of jammer by achieving the Cramer Rao Lower Bound (CRLB). IEEE  

In this letter, we surpass the jamming effect and also estimate the jamming channel state information by using the Kalman filtering approach. Furthermore, we design a closed-form beamforming weight for maximizing the achievable rate subject to the condition that the total relays power transmission would be below a predefined threshold level, which is formulated as a quadratically constrained quadratic program. Simulation results show the efficient performance of the proposed method for different destruction powers of the jammer by achieving the Cramér-Rao lower bound. © 1997-2012 IEEE  

In this paper, an adaptive channel estimation algorithm is proposed for the multi-user robust relay beamforming problem. We propose a norm-bounded channel uncertainty model for all of the channels. We employ the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) for joint estimation of channel coefficients and beamforming weights, and propose a Markov model for source-relay and relay-destination channels as well as the beamforming weights in the relays. The channel coefficients and bemforming weights are shown to be well-estimated in order to minimize the total relays power transmission subject to worst-case signal to interference and noise ratio (SINR) criterion at each... 

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to... 

In this study, the optimal sensor placement problem for multi-source angle of arrival localisation is investigated. The authors adopt the A-optimality criterion, maximising the trace of Fisher information matrix, to determine the optimal sensor-target geometry under distance-dependent Gaussian noise model. A recursive representation of the Cramer-Rao lower bound is derived to recast the sensor placement problem into a sequential method, obtaining the optimal sensor geometries in a step by step manner. Note that the state-of-the-art methods are highly sensitive to the source location changes such that they should be relocated by any later changes in target geometries, which is practically... 

In this study, the optimal sensor placement problem for multi-source angle of arrival localisation is investigated. The authors adopt the A-optimality criterion, maximising the trace of Fisher information matrix, to determine the optimal sensor-target geometry under distance-dependent Gaussian noise model. A recursive representation of the Cramer-Rao lower bound is derived to recast the sensor placement problem into a sequential method, obtaining the optimal sensor geometries in a step by step manner. Note that the state-of-the-art methods are highly sensitive to the source location changes such that they should be relocated by any later changes in target geometries, which is practically... 

Localization of an emitting or reflecting target is one of the most important issues in a wide range of applications including radar, sonar, wireless communication and sensor networks. Due to significant effect on the positioning accuracy, designing the optimal sensor-target geometry has been considered as an important problem in the localization literature. The existing sensor placement methods mainly solve the problem in the cases without any constraints on the sensors locations. In the realistic scenarios, however, the sensors cannot be placed simply in arbitrary locations due to such constraints as the geographical limitations, communication problems between the sensor pairs and the... 

The sensor-source geometry has a significant effect on accuracy of source localization problems. In a sensor placement problem, one attempts to optimally place the sensors in the surveillance area so as to optimize a performance criterion. Sensor placement methods mainly solve the associated problems without taking any specific constraint on permissible location of sensors into account. In practical applications, however, possible location for deployment of the sensors are subject to such limitations as environmental, industrial, and communication constraints, which affects the optimal sensor-source geometry. In this article, we consider the problem of optimal sensor placement, based on time... 

Least squares estimation is a widely-used technique for range-based source localization, which obtains the most probable position of mobile station. These methods cannot provide desirable accuracy in the case with a non line of sight (NLOS) path between mobile station and base stations. To circumvent this drawback, many algorithms have been proposed to identify and mitigate this error; however, they have a large run-time overhead. On the other hand, new positioning systems utilize a large set of base stations, and a practical algorithm should be fast enough to deal with them. In this paper, we propose a novel algorithm based on subspace method to identify and eliminate the NLOS error.... 

This paper considers the localization of a moving target using a forward scatter radar consisting of a transmitter and an array antenna receiver. A direct positioning method based on maximum likelihood (ML) estimation is proposed and compared with the conventional two-step method in which the primary parameters, including Doppler shift and angle of arrival, should be determined in the first step. Moreover, closed-form expressions for Cramer-Rao lower bound of both methods are derived. The aforementioned methods are comprehensively compared in terms of positioning accuracy and computational complexity. Theoretical performance study, including determining the minimum required observation time... 

In this article, localization of a maritime target using a passive multistatic radar system, consisting of a single receiver and a number of satellites as its transmitters, is investigated. The set of communication satellites and also the global navigation satellite system (GNSS), including GPS, Glonass, and Galileo, are employed as illuminators of opportunity. In the receiver, the reflected signals from the target along with the direct signal from the satellites are employed to extract a set of bistatic range measurements. Leveraging these measurements and the satellite's position information, a novel localization method considering zero height of maritime target and appropriate combination... 

The relative velocity between ground surface and receiver platform leads to clutter expansion in the range-doppler surface. The expanded clutters can mask slow moving targets. The passive radar is a low cost and light weight radar with high detection ability, due to its multistatic geometry. In this paper, we employ the MISO passive radar with at least four transmitters of opportunity to constitute an over-determined system of equations. We propose an algorithm to localize moving targets in the presence of clutters. In the first step, the clutters are separated from moving targets and in the second one, the position and velocity of the target are computed by solving the system of equations.... 

In this paper, a fundamental investigation on the performance of localization problem in a distributed multiple-input multiple-output radar system using bistatic range (BR) and angle of arrival (AOA) measurements is conducted. The study is based on the Cramer-Rao lower bound (CRLB) for the target position under Gaussian measurement noise. We attempt to approximately compute the CRLB separately in the cases of BR-only and joint BRlAOA localization schemes and determine which factors can affect the CRLB values. The three factors the transmitter/receiver placement, the number of transmitters and receivers, and noise power levels are shown to be effective on the localization performance.... 

This paper concentrates on the target localization problem in a distributed multiple-input multiple-output radar system using the bistatic range (BR) measurements. By linearizing the BR measurements and considering the relationship between the nuisance parameter and the target position, a constrained weighted least squares (CWLS) problem is formulated, which is an indefinite quadratically constrained quadratic programming problem. Since the constraint is non-convex, it is a nontrivial task to find the global solution. For this purpose, an improved Newton's method is applied to the CWLS problem to estimate the target position. Numerical simulations are included to examine the algorithm's... 

In this paper, the problem of locating a target in a distributed multiple-input multiple-output radar system using bistatic range measurements is addressed. An algebraic closed-form two-stage weighted least squares solution for the considered problem is developed and analyzed. In the first stage, we establish a set of linear equations by eliminating the nuisance parameters first and then we apply a weighted least squares estimator to determine the target position estimate. In the second stage, in order to improve the localization performance and refine the solution of the first stage, an estimate of the target position estimation error is obtained. The final solution is obtained by...