فصلنامه رادار
سال پنجم شماره 3 (پیاپی 17، پاییز 1396)

InSAR technique can be used for extracting topography and estimating ground surface displacements. Phase unwrapping is one of the most important parts of displacement estimation from an interferogram affecting the results. Because of the nature of the phase unwrapping problem, finding the number of missed integer cycles is critical and demanding. If the conventional method is considered for interferometry, phases will spread regularly. In recent years, several algorithms have been presented to unwrap data in regular spaces. Since a comprehensive study has not been done for unwrapping methods evaluation yet, the main goal of this paper is to introduce and investigate the performance of the most important unwrapping algorithms. Moreover, unwrapping methods presented in regular space are thus investigated. Moreover, their advantages and disadvantages are fully discussed. Because regular-space methods are usually considered to unwrap phases, a number of efficient conventional unwrapping approaches have been implemented on an interferogram processed from ENVISAT ASAR satellite images acquired over the Tehran basin northeast.

In this paper, the design, synthesis, and simulation of 12 linear arrays of microstrip antenna is considered with cosecant squared pattern in 9.8 GHz. The structure involves 3 layers: the feed network has 2 bottom layers and the top layer is the microstrip patch excited by a slot. One of the advantages is low cross-polarization (less than -50 dB) that is due to removing the radiation fields of the feed network. For synthesis of cosecant squared pattern, the genetic algorithm method is used while the mutual coupling is considered. In this method, the far fields of each array are applied to the optimization. Proportional to the amplitudes and calculated phases by genetic algorithm, the feed network is designed. The simulation results verify the accuracy of design and synthesis.

Ground penetrating radar (GPR) method has the capability to present high-resolution images of subsurface structures; however, the accurate interpretation for the response of subsurface buried targets and structures need the recognition and awareness of the response of various types of subsurface targets. One of the most common objects used as subsurface installation is the horizontal cylindrical target representing kinds of pipes. In the current study, a two-dimensional finite difference forward modeling method has been employed to simulate the GPR response of horizontal cylindrical synthetic models based on different conditions of the reality, in order to use for interpreting actual survey. Finally, the detection of the location of a buried Qanat located in the north-west of Shahrood city, as a case study, proved the effectiveness of the GPR method for this purpose.

In this paper, we analyze and compare the tracking performance of cognitive co-located MIMO and phased-array radars. The cognitive MIMO radar optimizes its transmitted signal based on information from previous observations, and meanwhile obtains an estimate of the target parameters. The optimization criterion for the design of the transmitted signal is minimizing the Bayesian Cramer-Rao Bound (BCRB). The monopulse tracking method is assumed to be used in the phased-array radar. The simulation results show that the phased-array radar outperforms the cognitive MIMO radar in the tracking performance, despite the waveform diversity presented in MIMO radars. The phased-array radar has a much lower complexity and achieves a better estimate of the target angle in terms of the mean-square error and bias of the estimation.

In this paper, we consider the problem of cognitive transmit code and receive filter design for target detection in clutter. Since the previous works have considered clutter statistics to be a priori knowledge, in this paper, assuming unknown clutter covariance matrix, we propose two methods for estimating the parameters of the clutter covariance matrix (both based on secondary data and clutter model), and use them to develop cognitive algorithms for the joint design of transmit code and receive filter. Simulation results demonstrate that the clutter covariance matrix estimation error will lead to negligible performance loss. Moreover, as the number of secondary data increases, the average convergence rate of the proposed algorithms reaches the rate of the known clutter covariance matrix case.