JOURNAL OF RADAR
Year:2019 | Volume:7 | Issue:1 (SERIAL No. 21)|Papers Count:12

Due to extensive SAR applications and the need to recognize SAR image details, the issue of improving the quality of these images after formation has been widely considered. Due to the nature of SAR image formation, the multiplicative speckle noise is considered as the most important factor in the quality degradation of these images. In this paper, a new method for removing speckle noise is presented. The main ideas of this article are using MAP estimator in accordance with the noise distribution function and presentation of a local convex optimization problem along with employment of adaptive smoothing, sparse representation regularizations and projection to the feature space. The local optimization model and adaptive smoothing provide proper noise removal and strong edges preservation and prevent image over smoothing. Also using sparse representation leads to texture preservation, and projection to the feature space enhances the algorithm against high noise levels. In order to solve the optimization problem, a method based on alternating minimization is introduced. The simulation results show good performance of the proposed method in noise reduction and preservation of image details which is better than many existing methods.

In this paper, a fast-parametric method for ISAR autofocus is proposed which is based on the minimum entropy method and eigenvalue decomposition, and has less computational complexity than that of conventional autofocus methods. In this technique, the covariance matrix of the range compressed and aligned data is formed and by utilizing eigenvalue decomposition, signal and noise are separated. Then, the Fourier transform of the signal eigenvectors which are much smaller than the total eigenvectors is taken. Finally, by applying the conventional autofocus approaches to the image of eigenvectors, the phase error is estimated. In this paper, a parametric method based on entropy is utilized. The simulation results show that although the computational complexity is decreased, the performance of the algorithm is maintained.

In this paper an algorithm based on the real-time sub-aperture (RTS) algorithm is presented for phase error estimation and compensation of synthetic aperture radar (SAR) in the case of non-ideal platform motion. In the proposed algorithm, the signal is input into the RTS algorithm, then extracting the peak time of different sub-aperture processing channels, the phase gradient of peak times is computed and after integration and interpolation, the estimated phase error is compensated in the received signal and finally the RTS algorithm is executed again in order to form the final image. Using the proposed algorithm, the motion compensation (autofocus) ability can be added to the RTS algorithm without changing its processing core whilst keeping the real-time capability. Simulations show the effectiveness of the proposed algorithm for compensating phase errors due to motion fluctuations of the radar platform.

In this article an accurate and efficient procedure to design microwave waveguide filters is presented which is based on the S-parameters method combined with optimization algorithms. The proposed method is fast, since only the full-wave characteristics of a small part of the filter (one building block) are required to design the whole filter. Three different optimization algorithms; genetic algorithm (GA), particle swarm optimization algorithm (PSO) and quantum particle swarm optimization algorithm (QPSO) are used for tuning the filter response and their performances in filter design are compared. This approach is verified through full-wave simulation using commercial software for post filter, iris filter, and E-plane metal insert filter at Ku, X and W frequency bands, respectively.

Designers try to reduce missiles' drag coefficients, but many of the geometrical changes that reduce the drag coefficient can increase the radar cross section of the missile. So, authors decided to solve this problem by missile optimization. In this study, missile Ogive nose is optimized using multi-objective genetic algorithm while the length of missile is kept constant. Objective functions are drag coefficient and radar cross section (RCS). Ogive nose was tested in mach number of 2. 01 and radar systems were designed to operate at high frequencies between 4-6 GHz. The drag coefficient was calculated by CFD code and was compared with experimental results. Then, radar cross section was calculated with the commercial HFSS program. Finally, objective functions were optimized using non-dominate sorting genetic algorithm (NSGA-II) and the objectives were both minimized to establish the Pareto front. Pareto front shows the best possible design points for the objective functions. Compared with the initial model, the optimum model achieves a decrease of 47% and 14% in the drag coefficient and the radar cross section respectively.

In the most of DOA estimation methods, environmental noise model is considered to be uniform spatial white noise. However, in many applications this kind of modeling may not be appropriate and leads to considerable direction finding errors. Non-equal output noise power of array elements that causes nonuniform noise is one of these cases. The most important goal of this paper is the investigation and comparison of DOA estimation in presence of nonuniform noise using simulation as well as presenting a novel and effective method for DOA estimation in the mentioned situation. A novel low complexity algorithm for DOA estimation in presence of nonuniform spatial white noise is proposed. Additionally, the performance of the proposed method is simulated and compared with that of matrix completion based method and also iterative subspace estimation schemes for various parameters. The simulation results demonstrate that the proposed scheme achieves a considerable advantage over the existing schemes with remarkably lower complexity.

In this paper, the goal is theoreticaly investigating and presenting a ground moving target detector in single channel SAR based on detection theory methods. Accordingly, the detector structure for ground moving targets has been developed based on generalized likelihood ratio test (GLRT) from raw received signal. For this purpose, unknown parameters of the ground moving target’ s returned signal, including azimuth location and velocity in both azimuth and range directions are replaced with their ML estimations. Then, generalized likelihood ratio test is performed which leads to estimator-correlator detection structure. Estimation of the unknown parameters of the target reflectivity which is necessary for the proposed detector, needs optimization of an objective function through a grid search in multidimensional space of the unknown reflectivity parameters. To reduce the computational load of this massive multidimensional grid search, conversion of the multidimensional received signal space to equivalent several 1D spaces is used. Effectiveness of the proposed method is demonstrated through experimental results by evaluation of detection performance curves.

A new category of radar systems that have been introduced in the last decade are MIMO radars. These systems have many advantages in terms of detecting and estimating target parameters compared to the previous systems. Due to the limitations of conventional positioning methods, to utilize all the advantages of MIMO radars, it is necessary to use new methods for signal processing. In this paper, the DOA estimation for MIMO radar is investigated using compact sensor methods. Given that in practical DOA estimation applications, there exists a prior information about the location of targets, in the proposed method(P1, 2, w) by applying appropriate weighting, retrieving direction, amplitude and resolution of targets is done with smaller number of measurements and has better results. In this case, a 23% improvement over conventional methods is observed. Also, the recovery problem has been investigated for two measuring matrices, and according to the simulation results, the signal can be recovered with 8% less measurements by selecting the Gaussian measurement matrix instead of the partial identity measurement matrix.

The effect of jammers on radars has been assessed on the basis of detection range reduction whereas in a tracking radar, inducing inconsistency in target tracking ability is more important than range reduction. A new criterion known as relative radar functionality destruction time is introduced and defined as the ratio of functionality destruction time of the radar to one period of the jammer, in which the power of the jammer signal and target echo power are considered. In this article first, the jammer effect coefficient is defined as the relative destruction time in terms of jammer to signal power ratio. Next, this criterion is applied in assessing a simple conical scan radar receiver against a conventional jammer (sweep noise jamming). By implementing the structure of a FMCW tracking radar, a simple target based on the DRFM method and one type of jamming against this radar are simultaneously simulated, and the functionality destruction is extracted for different radar parameters. Simulation results demonstrate that this new criterion outperforms its counterparts.

Low probability of intercept (LPI) radars are difficult to detect and identify by electronic intelligence receivers due to their low power, wide bandwidth and frequency variability. With the emergence of this technology, new methods of signal and image processing are constantly required to first identify, then classify, and finally extract the characteristics of these radar signals. To solve the problem, today deep learning is an important technical method in the signal and image processing fields. Through using this method, this paper will investigate the possibility of detecting and classifying different signals of LPI radars. To do this, using Short-Time Fourier Transform (STFT), we will analyze the received signal in the time-frequency domain, and then to detect and classify the LPI radar signal waveforms we send the output, in image format, to the AlexNet and the LeNet deep convolutional neural network (CNN) models. The simulation results show that, in SNR=-5dB, the accuracy of the AlexNet and the LeNet methods are 97. 34% and 94% respectively, indicating the better performance of the AlexNet method.

In this paper, we consider the problem of sensor placement for passive source localization under conditions where there are multiple sources and noise measurement is distance dependent. To this end, first we derive the Cramer-Rao bound for the angle of arrival (AOA) based source localization for distance-dependent noise model and existence of multiple sources. Then, the optimal arrangement of sensors to achieve this bound is obtained.

Available processing algorithms to extract stripmap SAR images are able to deal with spaceborne case rather than airborne case, because of atmospheric and platform trajectory disturbances. Flight path information should be provided to the processing unit to compensate motion deviations. Motion compensation through navigation data is usually done in two steps and doesn't change the standard form of the traditional algorithms. Adding motion compensation to the approximate – k algorithm is not so easy and similar to other algorithms. It requires major changes in the standard form of the algorithm and has not been done so far. This modification is the main goal of this article. In this paper, we propose a modified form of approximate – k algorithm which is capable to consider motion compensation, unlike the traditional form. To evaluate the performance of the proposed algorithm, a set of simulated data and real raw data of ISRCSAR system are applied. Comparision of the extracted images indicates the high performance of the proposed algorithm.