فصلنامه رادار
سال ششم شماره 2 (پیاپی 20، پاییز و زمستان 1397)

Array antennas have many applications in civil and military systems such as: radar, surveillance systems, direction finders, electronic warfare (EW), etc. Feed network is one of the main parts of array antennas. In this paper a 1:8 corporate feed network based on the new waveguide technology referred to as ridge gap waveguide (RGW) at Ku band is designed and simulated which can be extended to any arbitrary 1:N feed network. The main advantages of RGW technology are: low loss, broad bandwidth, low sensitivity to manufacturing errors, usability at high frequencies like millimeter waves, easy integration of active components, etc. Return loss of the simulated feed network is better than -15 dB at 15-18 GHz frequency band. Furthermore, the insertion loss from the input to each output is almost -9dB which is as expected, and also the change of phase difference from input to each output is less than 1 degree.

In MTI tracking radars, one of the main factors for canceling clutter at the radar transmitter output and increasing MTI filter improvement factor is the reduction of cathode power supply voltage ripples. Voltage ripples can be compensated by inserting a post regulator circuit at the output of the amplifier tube. In this paper, the post-regulator circuit needed to reduce the intra-pulse ripple of a X -band 75KW Klystron transmitter, in order to achieve a 50dB MTI improvement factor is first simulated and then designed and implemented and the test results are presented. The main feature of the presented work is the design and implementation of a post-regulator circuit for a 0.05% voltage ripple level in a high frequency X -band 75 KW Klystron transmitter with a 20KV voltage level and 20A current with a 7% duty cycle. The presented circuit has been implemented with semiconductor technology, which by itself, is an innovation in the circuits of this type.

In this paper, a new method is proposed to estimate the direction of arrival (DOA) using non-uniform linear array structure and modeling the measurement matrix as a DFT matrix. In order to estimate the DOA using compressive sensing (CS), continuous angle space should be divided into a discrete set using small steps. This division, leads to the increment of mutual coherence between columns of the measurement matrix and performance of the sparse recovery algorithms is degraded. To solve this problem, we propose a new method in which DFT matrix with mutual coherence of zero is used as the measurement matrix. In order to increase the accuracy of estimation, the size of DFT matrix or the number of antennas should be increased. Implementation of an array with large number of antennas is complex and expensive. A solution to decrease the number of antennas is using a non-uniform linear array and constructing a virtual uniform linear array. A virtual uniform linear array can be constructed by vectorizing the correlation matrix of the received signal of a non-uniform linear array. Increasing the number of antennas in the virtual array will increase the size of DFT matrix. Therefore, the accuracy of DOA estimation will be increased. Simulation results show that DOA estimation using compressive sensing, based on DFT measurement matrix, has a good performance in terms of mean square error of estimation.

Estimating the direction of arrival and beamforming are among the most important issues in array signal processing for which a variety of methods have been proposed. With few exceptions, these methods require an exact knowledge of array response including the knowledge of sensors’ positions, sensors’ gain/phase responses and mutual coupling coefficients between sensors. There are uncertainties about these array response parameters as we usually have their nominal values which are different from the actual metrics. The performance of DOA estimation and beamforming algorithms degrade severely because of these uncertainties. To solve this problem and reduce the performance degradation, it is necessary to estimate these unknown parameters. In this paper, we use simulations to study and compare the performance of several so-called self-calibration methods in the presence of array shape error. However, before performance investigation, it is attempted to improve the performance of these methods by manipulating their structure. In addition, in this paper, a self-calibration method based on the gradient search is proposed. Various simulations are used to evaluate the performance of this method and compare it with other self-calibration methods.

In this paper a method for optimal digital beamforming in ground-based circular synthetic aperture radar in order to maximize the image signal to noise ratio (SNR) is proposed. In this method to maximize the image SNR, an array of receiver elements is used and the complex weighting coefficients of receiver channels are computed by solving an optimization problem and eigenvalue decomposition. The optimization problem is formed based on data modeling in each range and matched filter image formation and SNR computation in each image pixel versus excitation coefficients. Finally, optimal weighting coefficients of each range are computed as the eigenvector corresponding to the largest eigenvalue of a matrix related to the geometry and parameters of the system. Simulations illustrate the superiority of the proposed method in increasing image SNR over other digital beamforming methods, especially at long ranges.

We consider the problem of transmit code design to enhance the detection performance of an extended target embedded in clutter. We model the target impulse response (TIR) in two frameworks, either via the product of a deterministic TIR with an unknown reflection factor or as a Gaussian random vector (with known covariance). For both frameworks, we impose either the peak-to-average-power ratio or the similarity constraints on the sought code, separately. In the former framework, the performance of the generalized likelihood-ratio test depends monotonically on the signal-to-interference-plus-noise ratio (SINR) of the detector. Hence, we cope with the code design, maximizing the SINR. The resulting optimization problem is non-convex, and we propose a novel approach to tackle it. In the latter, dependence of the optimal detector’s performance on the metrics is too complex for code design. Consequently, we employ the mutual information between the TIR ensemble and the received echo as the design metric. We devise an iterative method based on majorization-minimization technique to deal with the resulting non-convex constrained problem. We make the proposed method robust to deal with uncertainties about prior knowledge of clutter and interference. Numerical analyses highlight the effectiveness of the proposed methods comparing to their counterparts.