In order to provide the possibility of defense against maneuvering targets and providing better opportunity for decisions, a general structure for different maneuvers of targets have been provided in this paper. Moreover, variety of trajectories is simulated from RADAR perspective and the maneuver determination is considered carefully. In order to detect and track the targets trajectory to perform a proper defense, the targets motion must be simulated so that the tracking is to be done with minimal error. In order to present a general structure for maneuvers of targets, in this paper, different models for targets trajectory in one, two and three-dimensional forms for maneuvering and non-maneuvering targets are studied and simulated. As an innovation, effects of detection and false alarm probabilities were applied to the simulations. For all the presented models, trajectory simulation was done and the results are summarized in diagrams in this paper. The results can be used for evaluating different tracking algorithms in different SNRs.

In this paper, RADAR detection based on Monte Carlo sampling is studied. Two detectors based on Importance Sampling are presented. In these detectors, called Particle Detector, the approximated likelihood ratio is calculated by Monte Carlo sampling. In the first detector, the unknown parameters are first estimated and are substituted in the likelihood ratio (like the GLRT method). In the second detector, the averaged likelihood ratio is calculated by integrating out the unknown parameters (like the AALR method). Thanks to the numerical nature of these methods, they can be applied to many detection problems which do not have analytical solutions. Simulation results show that both the proposed detectors and the GLRT have approximately the same performance in problems to which the GLRT can be applied. On the other hand, the proposed detectors can be used in many cases for which either no ML estimate of unknown parameters exists or their prior distribution is known.

RADAR network deception through phantom track generation using cooperative unmanned aerial vehicles (UAVs) is an effective way of encountering sophisticated RADAR networks. In previous studies a method based on virtual motion camouflage (VMC) and path control parameter (PCP) has been developed to design the trajectory of cooperative UAVs during the phantom track generation mission. In these studies, the RADAR locations are assumed to be known. In real situations, the knowledge of RADAR locations has an inaccuracy. The RADAR position inaccuracy leads to an incoherent phantom track. The RADAR network can recognize the phantom track according to the distance between the false targets created by each UAV. In this paper, the effect of the inaccuracies of the RADAR position on the generated phantom track is considered. By adding a constraint that limits the flyable range of UAVs, the phantom track is generated such that the probability of the RADAR network detecting the deception is minimized. The mentioned constraint is specified based on the maximum RADAR position inaccuracy, the RADAR error, and the false target trajectory. The simulation results show that using this constraint in the trajectory design for UAVs, reduces effectively the recognition rate of the phantom track for various ranges of inaccuracies.

In this article, first, the significance of group jamming is stated. The jamming resources can be classified as follows: time, space, power, displacement, bandwidth, jamming pattern, number, switching, and beam forming. Next, some examples from the work background are presented, followed by an overview of the key components of the group jamming algorithm. In the explanation of the main components of the group jamming algorithm, there are eight types of indicators that are considered and explained. These indicators are used to evaluate the effectiveness of the jamming. Among these indicators, the issue of reducing RADAR detection has been considered a criterion for evaluating jamming effectiveness. The group jamming approach proposes and solves two scenarios using the auction algorithm. The first proposed scenario involves insufficient data on RADAR locations, and the subsequent solution is mathematically analyzed and discussed. In the second scenario, we include the assumption of not knowing the precise working frequency of the systems. This additional assumption modifies the mathematical relationships under the new conditions. Finally, the simulation results and topic summary are presented. Using the proposed principles, it is feasible to design and evaluate various assumptions and scenarios of group jamming.

The optimal control is very important in electronic warfare, and has a critical role to achieve military successes. In this paper, we have tried to help decision support for this part by focusing on some important issues in the management of electronic warfare. We introduce and improve three efficient algorithms including based on "Random", "Auction" and "Game Theory-Based" for RADAR jamming resource assignment. and, we want to know whichof our jammers have to allocate to which enemy RADARs to create maximum jamming in them? We use principles of dynamic game theory to develop Auction algorithm and introduce Random algorithm to illustrate the performance of two other algorithms by making possibility to compare. In other effort, we improve the algorithms by cost-based and time-based algorithms for non real time and real time combats. Eventually, we have supposed some scenarios to demonstrate that our efforts are operational through simulations such as our jammers located on vehicles. Therefore they can move in the battlefield to increase the effectiveness of group jamming, and also in the other scenario we have enough jammers. Hence, we can switch them on and off to achieve the best jamming condition with regard to economic considerations.

While many research papers exists about RADAR signal estimation using ELINT gathered data, no deep research exists about RADAR performance evaluation, using collected data. This important topic is regarded in this paper using some physical basic laws of RADAR and signal processing as well as some logical interpretations. Estimated parameters include antenna beam width, gain and physical dimension, range and angle resolution, clutter attenuation level, minimum detectable RCS, maximum detection range, transmitted and consumed power. Some estimation about RADAR performance parameters, real data about some well-known RADAR systems are presented and it is shown that, the proposed algorithms prepare almost accurate estimation of RADAR parameters.

Due to increasing growth of air traffic, it is very important to have a reliable air traffic control system. Usually secondary surveillance RADAR systems are employed for this task. In this paper, a new approach is presented to identify interrogators location of this system. The proposed method works passively and uses just a single receiver. This goal has been reached using angle and time difference of arrival information. How to determine the desired angle and calculate the time difference of arrival from the received data is described. knowing this information, a closed-form solution in polar coordinate is presented and its sensitivity is measured. Then a method is proposed based on maximum likelihood estimation of the target location. Afterward angle and time difference of arrival equations are linearized and then weighted least squares estimation is achieved. The suggested approach is evaluated on real and practical information and the results have finally been reported.

Multistatic RADAR systems have many applications such as homeland security, anti-air defense, anti-missile defense, and ground target detection. In this article, a multistatic RADAR system, capable of detecting a flying object within the coverage area and computing its position and velocity in a three-dimensional coordinate system, is simulated.Calculating the RADAR cross section, the kind of the object is recognized. If the object's path follows a specific equation of motion, the path can be estimated only after the first detection. Otherwise, the simulation procedure estimates the object's path by tracking it. Simulation results show that the proposed method can effectively be applied to applications such as anti-air defence systems. To increase the signal to noise ratio and vastness of the coverage area around a specific point, a solution to optimally locate the RADAR sites, is presented. As the design parameters are conflicting, this is achieved using a multi objective design technique.

In this paper, we design an attractive algorithm aiming to classify moving targets including human, animal, vehicle and drone, at ground surveillance RADAR systems. The non-stationary reflected signal of the targets is represented with a novel mathematical framework based on behavior of the signal components in reality. We further propose using the generalized linear chirp transform for the analysis stage. To enhance the classification performance, the rotation invariant pseudo Zernike-Moments are extracted from the time-frequency map. Consequently, the obtained features are trained to the k-NN classifier. In the numerical experiments we show the superiority of the proposed method in comparison with the existing recent counterparts, for both performance as well as the computational complexity. The results indicate that the proposed method obtains the rate of 95% accuracy in classification performance, when the signal to noise ratio is higher than 25dB. Index Terms— Automatic Target Recognition (ATR), General Linear Chirplet Transform (GLCT), Moving Target Detector (MTD), RADAR Target Classification, Short Time Fourier Transform (STFT).