JOURNAL OF RADAR
Year:2016 | Volume:4 | Issue:3|Papers Count:6

One of the main concerns of processing algorithms used in synthetic aperture radar (SAR) with a ballistic motion is the compensation of all phase errors in the data received from the target to achieve a clear picture of the target. In this article, by using Range Doppler Algorithm and Phase-Gradient Auto focus algorithm, imagery in one SAR with a ballistic motion is considered according to the geometry of SAR with a ballistic motion. This process is a combination of Range-Doppler processing algorithm and phase-gradient auto focus algorithm. The significant characteristic of this method is that it does not need to collect information from navigation systems for motion compensation and any kind of phase error decreasing. Furthermore, this algorithm can estimate speed and acceleration of SAR motion, and, thus, the phase error existing in received data will be tending to zero. The innovation made in this article is to improve phasegradient auto focus Algorithm in choosing the prominent scatterrer stage by the correlation operator. Recent characteristic provides the possibility of imagery from a wide scene from the target.

Stepped frequency radars attract many attentions due to their high range resolution. Conventional processing technique in these radars is based on the IDFT. In detecting moving targets, the IDFT has the problems of range shifting and range spreading, which result in degrading target amplitude, loss of range accuracy and range resolution. To overcome these problems, the velocity compensation method is used. Since the target velocity is unknown, one should compensate the received signal with all possible velocities and choose the one with the highest and sharpest IDFT output. In this paper, by using the compressive sensing based algorithms, a new method for determining the range and the velocity of moving targets in the stepped frequency radar is proposed. The results show that the proposed method does not have any problem with range shifting and range spreading, and has a better performance compared with other methods in literature. Coherent processing concept is also investigated. Simulations show that using the coherent processing, the performance of compressive sensing based radar detectors is considerably improved.

In this article, the linear array (with 20 elements in the horizontal orientation) of microstrip antenna is examined with horizontal polarization. The set includes three micro strip substrates. There is a power network in two lower substrates and micro strip patches in the upper substrate. Excitation of patches has been done as coupled through the power network in two lower substrates. Using the slot between patches and feed transmission lines, the electric fields are coupled from the power network to the microstrip patches. Separation of the feed network from the radiating part of the antenna causes for the cross-polarization component to be less than-50 dB. On the other hand, in order to reach the appropriate side lobe level (SLL) in the horizontal orientation, the array is tapered using Chebyshev weights. After antenna simulation, SLL<-18 dB is achieved in the final structure. This radiation pattern is fan beam and can be used in radar and communication systems.

Based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical Shannon requirements. The sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. On the other hand, in compressed sensing, finding the answer hinges on finding the most sparse solution; thus this technique can separate clean signal from the noise. In FMCW radar, the distance of a target could be obtained from the frequency of the receiver output signal. Since this signal has a sparse representation in the frequency domain, based on compressed sensing theory, it could be reconstructed from a few number of data. In this paper, a new method for signal processing of FMCW radar is presented based on compressed sensing. Moreover, by considering noise removal feature that is in the nature of this technique, it is shown that the effect of noise on the receiver output signal can be reduced and the system performance of the radar can be improved.

Parameter estimation is an important task in the modeling, classification, and detection of radar clutters. Radar clutters have stochastic characteristics. Therefore, Statistical distributions are usually used to describe the features of clutters better. K distribution is one of the most common models utilized to the simulation of clutters. This distribution, which consists of scale and shape parameters, has two speckle and local power components. Because local power component is modeled by gamma distribution, the parameter estimation of K distribution is a high dimensional and nonlinear problem. In this paper, a novel method is proposed based on the gravity searching algorithm for the parameter estimation. This new method has high accuracy and validity in estimating parameters. For the evaluation of the proposed method, the estimated probability density function and power spectrum in two different experiments were compared to actual ones. Finally, the results of the new method are compared to the results of the maximum likelihood method. Furthermore, K-S test is performed to evaluate generated clutters with estimated parameters. Results prove the validity of the proposed method for the parameter estimation.

In this paper, a block-based method in wavelet domain for SAR image coding is presented based on the EZBC algorithm. To better preserve edges and textural structure in SAR images, instead of using traditional dyadic wavelet transform, a packet wavelet is employed. To improve the efficiency of the EZBC algorithm for SAR image coding, a modified version of this algorithm, is proposed. This modified version, which is called MEZBC, is better adapted to wavelet packet coefficients. The proposed algorithm is fully SNR scalable and supports block-based coding of wavelet coefficients. MEZBC is also further modified to support regions of interest (ROI) coding in SAR images. Experimental results show that the proposed algorithm provides a better performance for SAR image coding than other well-known algorithms in almost all bit rates.