An optimal kinematics-based discrimination algorithm is presented for the nearly real-time discrimination of exoatmospheric active phantom (deception) tracks against other physical targets (PTs). The new approach uses a batch-processing maximum likelihood estimator (MLE) to precisely estimate the deception range of decoys from raw radar tracking measurements by using motion features. Hence once these parameters are estimated, they can serve as direct statistics to initiate a discrimination. By augmenting the state vector with deception parameters, explicit expressions of motion models of decoys in the radar centered East-North-Up (ENU) coordinate system (CS) and spherical-CS are derived. Based on these models, the theoretical Cramer-Rao lower bound (CRLB) and the observability of the deception parameters are also analyzed. A Levenberg-Marquardt method is employed to obtain more robust estimate of these parameters, and the estimated parameters combined with the CRLB are used for designing discrimination algorithm. The simulations verify the feasibility of the algorithm. Furthermore, the discrimination performance due to the influence of radar position, data rate, and radar measurement error are also covered. The advantage of the algorithm lies in that it can position the warhead precisely as well as discriminating active decoys simultaneously when compared with the traditional 6-dimensional orbit determination methods.

Range deception is a common electronic countermeasure technique used for ballistic missile penetration. The well-designed decoys of range deception can even form stable tracks. Discrimination of these decoys is difficult and one potential way is at radar data processing level by using motion features. This study presents a novel method, called acceleration matched discrimination algorithm, which fully utilises the fact that the accelerations of exo-atmospheric active decoys are essentially different from that of physical targets (e.g. warhead), that can discriminate these decoys at the radar data processing level. First, the acceleration model of active decoys is explicitly derived. Secondly, the acceleration matched coefficient (AMC) is defined based on the filtered acceleration and theoretical acceleration. By employing the extended Kalman filter, the instantaneous variance of AMC is also derived. Finally, the discrimination algorithm is designed based on a batch-processing weighted least squares estimate and its estimated variance. Theoretical analysis and simulations indicate that the discrimination method is valid and feasible. Furthermore, the discrimination performance analysis due to the influence of radar position, radar measurement error and data rate are also covered.

Dual-source jamming is an effective way to prevent monopulse radar systems from performing accurate angle measurements. In this investigation, based on noncoherent dual-source jamming, we propose a cooperative deception approach, which has the benefit of causing persistent and large angular measurement errors to protect a true target and therefore can greatly reduce the discrimination capability of a distributed track-to-track radar fusion system. A scenario involving a single target accompanied by a digital radio frequency memory (DRFM)-based repeater jammer countering two radar systems is illustrated. By controlling the amplitude ratio and time delay in a statistical manner, the active decoy signal and target echo signal can form a noncoherent angular glint effect and result in the deviation of the angle tracking loop of the two radar systems from the true target in opposite directions. The track-to-track association distance related to the retransmission parameters is explicitly derived, and its statistical characteristics are analyzed in detail. Simulations verify the feasibility of the approach. The advantage of the approach lies in its ability to destroy the so-called “common origin” signature of the physical target by injecting false angular information. The direct result is that the true target might not successfully complete the process of track-to-track association, thereby enabling us to realize the desirable effect of “disguise the true target and instead show false decoys”.

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Azimuth multichannel synthetic aperture radar (SAR) possesses high-resolution and wide-swath (HRWS) imaging capabilities, posing a significant threat to the survivability of critical targets during wartime. However, due to the constraints of multichannel reconstruction processing, existing jamming technology will produce a periodic jamming effect in the azimuth direction when against HRWS-SAR. This unique periodic repetition related to the number of channels will make the jamming easy to detect and eliminate. Meanwhile, the amplitude of the jamming signal suffers from the modulation of sine functions, which causes a waste of jamming energy. Against the above problems, this article introduces a pre-segmentation idea, effectively circumventing the aforementioned problems. Meanwhile, this article proposes a two-dimensional (2-D) precise controllable smart jamming method designed for HRWS-SAR. The proposed method can generate two jamming effects, barrage and deceptive, at designated locations within the targeted area. First, 2-D expand factors are defined respectively to achieve precise control of the barrage jamming range. Then, by controlling multiple jamming parameters, a highly controllable deceptive jamming effect can be achieved. Additionally, a parameterized jamming model is established, which can achieve 2-D precise control of the jamming position through the cooperation of the signal and spatial domain. Notably, this study focuses not only on the impact of different jamming parameters on imaging results but also on evaluating jamming effectiveness under parameter estimation errors to guide optimal jamming strategies. Finally, extensive numerical simulation experiments and comparisons with other methods are conducted to verify the superiority of the proposed method.