A fast and accurate synthetic aperture radar raw data generator of moving targets has high importance, especially in the applications of the ground moving target indication. In this paper, by the hybrid time-frequency domain, a fast algorithm has been proposed for strip mode SAR raw data generation of moving targets. Using this simulator, in different conditions in terms of target motion speed, acceleration and direction, radar raw data has been generated and its final image has been extracted by Range-Doppler Algorithm. Then, the obtained images have been studied using the formula that have been extracted for predicting the effects of target motion parameters in the SAR images. The results show that the proposed simulator has a better performance in terms of speed than other existing simulators.

For acoustic imaging of underwater moving target, multi-static inverse synthetic aperture sonar idea can be used. Although, proper imaging algorithm has been derived but related parameters have not been discussed. In this paper, after reviewing multi-static inverse synthetic aperture sonar idea and relevant imaging algorithm, effective factors on underwater imaging of moving target such as target dimensions, range, depth and movement, sonar depth and specifications and underwater acoustic channel characteristics are analyzed and practical imaging limitations are derived for the first time. Effects of mentioned factors on target image are simulated and accuracy of analysis is validated.

With the development of satellite technologies and the development of processing algorithms, coupled with the use of SAR sensors, allowing for ground moving target detection and velocity determination has been provided by remote sensing satellites. Although previously airborne SAR systems had been used specially for military uses, nowadays this feasibility has been provided from space born SAR systems for civil applications such as traffic monitoring and search and rescue. In this study, the process of SAR image formation and the effects of moving components have been simulated in MATLAB and the results analyzed. Also the methods of detecting moving targets such as ATI and DPCA have been implemented on simulated images. Finally, these methods were implemented on real TerraSAR-X images to detect moving targets.

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.

synthetic aperture radar (SAR) for ground moving target indication (GMTI) and imaging (GMTIm) have been gaining increasing interests for both civilian and military applications. Because SAR is generally designed for imaging a stationary scene, the SAR image of a moving target will be both displaced and smeared. More specifically, by exploiting the inherent sparsity of the moving targets in the clutter-suppressed SAR image domain, in this article. the intended SAR-GMTIm problem is solve by a sparse Bayesian perspective. The theory of CS has been successfully applied to SAR/ISAR imagery to achieve high cross-range resolution with a limited number of pulses In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The proposed algorithm shows a 10-dB higher TCR compared to the conventional algorithm.

Tracking moving targets using mobile robots is a crucial aspect of robotics. This paper presents a novel approach for tracking a moving target in an uncertain environment with various obstacles, even when the target’s trajectory and speed are continuously changing and unknown. The proposed method utilizes reinforcement learning, a widely used technique for motion planning problems. However, applying reinforcement learning in uncertain dynamic environments poses a challenge due to the continuous state space. To address this issue, our algorithm employs an ART2 neural network for classifying the state space. Additionally, to enhance the speed of reaching the target, a point is predicted based on the target’s speed, direction, and the robot’s speed. The robot then selects its next move to approach this predicted point while avoiding contact with both static and dynamic obstacles. Simulation results demonstrate the efficiency of the algorithm, as the robot successfully reaches the target without colliding with any obstacles.

In this paper, based on resampling particle filter algorithm, a new method to track moving object is proposed. Determining the number of levels needed for resampling in particle filter algorithm, plays an important role in the time duration of each video frame processing. By estimating the Gaussian kernel density the weighted histogram of the target model is obtained and by considering random noise variance at target place the position of candidate particles for the next frame will be predicted. In the current work, the candidate particles are weighted using Bhattacharyya distance, while the number of resampling levels is determined in accordance with the particle weights, adaptively. The radius of the kernel will be matched on moving target variations by using edge detection. Comparison of the result of proposed algorithm with the result of fixed level resampling particle filter algorithm, shows the increasing of moving target detection accuracy up to 88% without excess changes in the particles distribution variances. Moreover, the average process time decreases to 22ms.

Moving target tracking is a process in which an object is tracked and its location is determined in each frame. The goal of this process is facilitating the subsequent process in order to analyze the behavior or detect moving objects. In this paper, a new approach has been proposed for aerial moving targets detection and tracking based on feature matching algorithms. By this way, we propose spectral density for target detection and ASIFT feature matching algorithm for tracking. The challenge is selecting features that are robust against the changes of brightness, noise, rotation, scaling and viewing angle. To solve this problem, key points and their correspondence on the patterns extracted from consecutive frames, are calculated by the ASIFT algorithm. Also, to reduce false matches in consecutive frames, the RANSAC algorithm is used. In addition to strengthening the proposed algorithm against the scale change of target, the object history scale in the 10 previous frames is used. The proposed algorithm was performed on an AIRCRAFT TRACKING standard database. Experimental results demonstrate the robustness and accuracy of our proposed technique.

In this paper an automated and robust method for detection and tracking of moving target with assumption of camera mobility using scale invariant feature transform is proposed. By automated registration technique of current frame on the previous frame, a panorama based background model of scene is generated. Then for real-time panorama-based background subtraction, frame registration into the panorama and global homographic matrix estimation are used. After that for each new frame, moving targets are detected and background model is updated and the key points where detected in the boundary region around the target are used for tracking. By matching description vectors of at least three key points where extracted from scale invariant feature transform of current frame to previous frame, initial estimation of target location on current frame are obtained. In the next step, more accurate estimation of target location on current frame is obtained by using Kalman filter. This approach is able to be used in different statues, such as variable climate condition, closing or faring the target, partial and global occultation. Comparison to expert operator, this approach improved performance results and achieved high accuracy in detection and tracking of aerial and ground target.

In this study, a new guidance law for high maneuver targets is designed by using state dependent Riccati equation. The reason of using this equation is: no need to linearization of equations and simplification of implementation. The aim of this research is presenting a guidance law that compensate the depletion of classic guidance laws for high maneuver targets and in the second, the comparison among proportional navigation law, pure pursuit law and new guidance law. It is worth noting that the criterions of analyzing are: total of incoming acceleration and required time for hitting the target. So these laws have been compared for fixed target, moving target with constant speed and moving target with variable speed and for different initial conditions. Finally a sensitivity analysis has been done for investigation of laws behavior.