Compared with the point-scattering model, the attributed scattering center model (ASCM) is able to describe the frequency and aspect dependence of canonical scattering objects using solutions from both physical optics and the geometric theory of diffraction. As the ASCM is complicated, it may increase the dimension of the parameterized dictionary, which will increase the cost of computation and storage significantly. Aiming at this problem, a novel sparse representation-based algorithm, combined with an alternative optimization and dictionary refinement, is proposed. Utilizing the orthogonal matching pursuit algorithm combined with relaxation algorithm, the solution to the sparse signal recovery problem can be obtained. Numerical results on both electromagnetic computation data and measured SAR data verify the validity of the proposed algorithm.

Taking into account the probabilistic uncertainty on the target radar cross section (RCS) parameter, a robust chance constrained power allocation (RCC-PA) scheme is presented for multiple target localization in colocated multiple-input multiple-output radar system. Such a system adopts a multibeam working mode, in which multiple simultaneous transmit beams are synthesized to illuminate multiple targets independently. We formulate the RCC-PA problem into a chance constrained programming model, where the total power consumption of the multiple beams is minimized to meet a specified multi-target localization accuracy requirement with high probability. Various target RCS fluctuation models have been discussed with different forms of probability distributions. We analytically show that the chance constrained programming problem for each RCS fluctuation model can equivalently be formulated as a deterministic convex optimization problem. Then, by formulating the Karush-Kuhn-Tuckers conditions, we transform the convex optimization problem into a nonlinear equation solving problem, and then solve it by using the bisection method. Simulation results show that our RCC-PA scheme can enhance the power utilization efficiency, and is more robust than the existing deterministic PA schemes.

A manoeuvring target tracking algorithm based on the autoregressive (AR) model is proposed. First, the AR model is incorporated into the Kalman filter (KF) for target tracking. The closed‐form solution of the AR model coefficients is obtained by minimising the mean‐square tracking error, and subject to the polynomial constraint of target motion. Then, based on the AR model, the proposed algorithm is constructed by combining the KF with the interacting multiple model (IMM) filter, coupled with the proposed detection schemes for manoeuvre occurrence and termination, as well as for switching initialisation. Simulations are performed to demonstrate the effectiveness of the AR model, and the proposed algorithm is compared with the IMM filter and variable‐dimension filter in the manoeuvring scenario.

Target detection in the presence of main-lobe deception jamming has received intensive attention. Generally, traditional target detection with additive jamming can be regarded as an adjacent process of jamming suppression and target detection. Since target information can be lost in jamming suppression, the detection performance can degrade severely. Aiming at this issue, an end-to-end anti-jamming target detector is proposed based on convolutional neural network (CNN) in the presence of interrupted-sampling repeater jamming. The CNN method first converts the single-pulse compression filter into a series of feature extraction filters, accumulating the energy of various features in various dimensions and on multiple scales. Then, a feature fusion filter added after the feature extraction stage is utilized to suppress jamming features while simultaneously retaining the actual target features. The two stages can be achieved in the CNN structure, and all the filters in this method are obtained by training the CNN. Numerical simulations show the efficiency of the proposed method.

One emerging issue in modern electronic warfare is the competition between the radar and jammer, which in principle can be viewed as a non-cooperative game with two players. In practice, the interaction between the radar and jammer involves multiple rounds as well as partial observation. This makes the competition become a dynamic game with imperfect information. Anti-jamming strategy design for such kind of game is still unclear. In this work, the competition between a frequency agile (FA) radar and a transmit/receive time-sharing jammer is considered. We utilize an extensive-form game with imperfect information to model the multiple rounds interaction between the radar and jammer. For the established extensive-form game, finding Nash Equilibrium (NE) strategies is a computationally intractable task since the number of information states grows exponentially with respect to game stages. Instead, a sampled based learning method called Neural Fictitious Self Play (NFSP) algorithm is used to find approximate NE strategies. Simulation results show that approximate NE strategies can be obtained and outperform the common elementary and advanced strategies from the perspective of detection performance.