Cognitive radars can possess intelligent active sensing ability and adapt complex variable environment. Inspired by this technology, a cognitive frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is proposed to apply for surveillance and detect the moving target timely. FDA-MIMO radar is a novel array structure which employs the small frequency offset across the transmit waveform to produce the range-angle dependent beampattern. The range-angle dependent beampatterns have ability to suppress the jammers when they are located at the same angle of the target. To obtain the maximum signal-to-interference-plus-noise-ratio (SINR) and suppress the jammers effectively, we consider the transmit and receive beamforming filters design and proposed an iterating optimization algorithm to obtain optimal beamforming filters. For the moving target, the Extended Kalman filter (EKF) method is utilized to update the position of the target and the new position information is applied for the filters design to obtain optimal performance of SINR timely. In addition, expressions for the detection probability and SINR are deduced for the FDA-MIMO radar target detection. Finally, numerical results show that the proposed cognitive FDA-MIMO radar can effectively suppress the jammers and detect the moving target.

Beamforming plays an important role in the array radar anti-interference. However, the interference targets with fast speed may move out of the narrow null area of the beampattern. A null broadening method with robust adaptive beamforming for frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) systems is proposed in this paper. The projection preprocessing and windowing functions are applied to obtain stable beampatterns. Then, a novel null broadening method based on the covariance matrix reconstruction is realized by setting artificial fluctuations around each actual jammer. Finally, a new convex optimization model is established and solved to estimate the desired steering vector. Numerical simulations results verify the effectiveness of the proposed method.

Frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) radar is characterized by a range-angle-dependent beampattern owing to extra frequency offset across the transmit array elements. In this respect, the ideal beamforming performance can be obtained by designing the transmit and receive array parameters. On this basis, this paper aims to design the ideal beampattern for FDA-MIMO radar by introducing a joint array parameters optimization design method including receive filter (RF) and transmit frequency offset-receive element spacing (TFO-RS). The cyclic iteration is introduced, and this joint optimization method is divided into three sub-problems, including RF, TFO, and RS design. As for the TFO and RS design subproblems, function scaling is applied to transform the objection function to the quadratic form. Furthermore, the quadratic programming (QP) optimization problem with linear constraints can be constructed. The simulation results show that the proposed optimization design method can improve the ability of FDA-MIMO radar to detect multiple targets at the same time compared with the given conventional TFO and RS schemes.

The realization of anti-interference technologies via beamforming for applications in frequency-diverse arrays and multiple-input and multiple-output (FDA-MIMO) radar is a field that is undergoing intensive research due to its two-dimensional range-angle-dependent beampattern characteristics. To solve the missing covariance matrix problem and improve the anti-interference capability of FDA-MIMO radar, we present a two-stage based intelligent anti- interference scheme for FDA-MIMO radar under the nonideal condition. The scheme consists of two parts: signal covariance matrix missing data recovery and intelligent beamforming vector estimation. A dual-channel generation adversarial network (DC-GAN) structure is proposed to effectively recover both real and imaginary parts of data from a covariance matrix. Based on the recovered covariance matrix, the beamforming vectors are accurately estimated by constructing a one-dimension convolution network (1D-CNN). Meanwhile, a multiple target process scheme combined with the 1D-CNN is introduced to deal with multi-target situation. In the numerical simulation part, the simulation results reveal that the DC-GAN network can effectively recover the missing data from the covariance matrix, and the lower the missing data rate, the better the data recovery performance. In addition, in the simulation of beamforming vector estimation, the effects of two different input modes on network training performance are evaluated, and the performance differences between a fully connected neural network (FCNN) and 1D-CNN are analyzed and compared. The numerical simulation results verify the effectiveness of the proposed FDA-MIMO radar anti-interference scheme under different number of interference signal scenarios, and improve the interference suppression capability of FDA-MIMO radar.

Recently, the conformal array radar has attracted wide attention compared with the conventional array radar due to its flexible element configuration and more diverse signal processing methods. During the development of the jamming equipment, the electromagnetic environment becomes more complex, which brings more challenges to the radar system. To overcome the variable interference environment's negative influence on moving target tracking, we propose two adaptive power allocation methods based on two criteria to improve target tracking accuracy. Firstly, we build the data model of the conformal subaperturing frequency diverse array multi-input and multi-output (CSFDA-MIMO) radar and the interference signal. Moreover, the Bayesian Cramer-Rao bound (BCRB), signal-to-interference-plus-noise ratio (SINR) are derived and taken as the objective functions in proposed optimization problems. Based on it, we build two different power allocation optimization problems under BCRB minimization criteria and SINR maximization criteria, respectively, and two different power constraints are considered. Finally, simulation results verify the effectiveness of the proposed adaptive power allocation methods. The power allocation strategies under two criteria can obtain better tracking accuracy compared with uniform allocation, and the tracking accuracy of the adaptive power allocation method can be influenced by different power constraint conditions.