Target Tracking While Jamming by Airborne Radar for Low Probability of Detection

Abstract

Although radiation power minimization is the most important method for an advanced stealth aircraft to achieve the low probability of detection (LPD) performance against the opposite passive detection system (PDS), it is not always effective when the performance of PDS is advanced. In a target tracking scenario, an interference tactic is proposed in this paper to keep the airborne radar in an LPD state. Firstly, this paper introduces the minimization radiation power design of airborne radar based on the distance between the radar and the target, and introduces the minimization radiation power design of the airborne jammer based on the predicted detection probability of the opposite PDS. Then, after consulting the most commonly used constant false alarm rate (CFAR) technologies in passive detection systems, including the cell average CFAR, the greatest of CFAR, the smallest of CFAR and the ordered statistic CFAR, this paper analyzes their relationships and points out the way of interference. Finally, based on the constraints, not only including the predicted detection probabilities of airborne radar and opposite PDS, respectively, but also including the time synchronization which is necessary to avoid the leaked interference power generated by airborne jammer jamming the airborne radar echoes from the target, this paper establishes a math model to minimize the total interference power of airborne jammer without interfering target tracking. Simulation results show that the proposed model is effective.

A two-stage classification algorithm for radar targets based on compressive detection

“…A constant false alarm rate (CFAR) detector adaptively changes the detection threshold as the reference cell changes to achieve CFAR detection of targets [25]. Extensive radar target detection results have been achieved with CFAR [26][27][28]. In radar target recognition, sparse representation research focuses on extracting accurate target feature information from the received echo data [29].…”

“…In [35], Bayesian CS is studied in detail for signal detection, which needs the prior probability information of the sparse signal. However, when the background distribution characteristics of clutter are relatively complicated, it is difficult to detect the target in the clutter background [28]. Therefore, based on the combination of CD and CFAR detection, in this paper, a support vector data description (SVDD) is used to further classify the signal, which can further reduce the amount of data and improve the detection accuracy [36].…”

Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System

“…The tracking system is an important part of UAVs which detects a target of interest rapidly in a large area, and then performs continuous surveillance of the selected target in the tracking phase [14]. In order to achieve target acquisition and localization, military UAVs are usually equipped with airborne radars [15] or guided seekers [16]; however, they are too heavy and unaffordable for most civilian used UAVs. As one of the most popular UAV platforms, quadrotors are more stable and have a lower manufacturing cost than helicopters.…”