Among the various types of jamming, active deception jamming has similar characteristics to the transmitted signal. The jamming seriously affects the detection and tracking of real targets by radar. In recent years, waveform design has become an effective anti-jamming method with the maturity of waveform generator technology. However, most anti-jamming waveforms are only designed to combat a single kind of jamming. To compensate for the shortage of existing waveforms that cannot simultaneously combat compound deception jamming, we construct an interpulse-intrapulse joint frequency coded(I-IJFC) anti-jamming waveform and analyze the anti-jamming process flow. Moreover, to reduce the influence of residual jamming, we propose an intrapulse frequency-coded method based on jamming parameters estimation. The waveform can be used against cross-pulse repeater deception jamming(CRDJ) and interrupted sampling jamming(ISJ). Then, to solve the problem that the existing algorithm cannot realize the coherent accumulation of the proposed waveform, we propose a coherent accumulation algorithm of subpulse rearrangement compensation and nonuniform discrete Fourier transform (SRCNDFT). The simulation examples show that the waveform proposed in this paper performs better against compound deception jamming than existing waveforms, and the proposed algorithm can realize the coherent accumulation of I-IJFC echo signals.

Traditional networked radar systems for target tracking usually suffer from a heavy data processing burden, and do not consider the sensor location uncertainties (SLUs), by assuming that radar locations are known perfectly, which is applicable only for static platforms. In this paper, considering sensors mounted on moving platforms, we propose a joint power allocation and measurement selection (JPAMS) strategy for multitarget tracking and data reduction in radar networks with the SLUs. The mechanism is to optimize the transmitted power and select the propagation paths with informative measurements, simultaneously. First, we adopt a distributed fusion architecture to estimate both states of targets and radars in clutter. Based on the distributed fusion architecture, the predicted conditional Cramér-Rao lower bound (PC-CRLB) considering the SLU and the measurement origin uncertainty is derived. Second, the JPAMS strategy is formulated as a bi-objective optimization problem, where the sum of weighted PC-CRLBs and the number of selected propagation paths are used as the performance metrics with respect to tracking and data reduction. The corresponding optimization is a NP-hard problem containing both continuous and binary variables. Third, to solve this nonconvex problem, we propose a sparsity-enhancing sequential convex programming algorithm. Finally, numerical simulations demonstrate the superiority of the proposed JPAMS strategy over the traditional allocation strategies.

In this letter, a joint radar scheduling and beampattern design (JRSBD) strategy is proposed to track multiple targets by a netted colocated multiple-input multiple-output (C-MIMO) radar system in clutter. The mechanism is to jointly optimize the radar scheduling and the waveform correlation matrix of each C-MIMO radar to maximize the tracking performance. First, we develop the deterministic covariance, as the performance metric, to quantify the actual target state estimate accuracy. Second, the JRSBD strategy is formulated as an optimization problem with some system constraints. The resulting optimization problem contains both binary and continuous variables and is nonconvex. Finally, we propose a sequential convex programming method to solve it. Numerical simulations demonstrate that the JRSBD strategy can effectively improve the target tracking accuracy.

In this paper, an effective joint measurement selection and power allocation (JMSPA) scheme is proposed for the distributed multi-target tracking task in radar networks with missing data. Missing data may occur during data exchange between radars and a fusion center (FC) due to unreliability of communication channels. First, we derive the predicted conditional Cramér-Rao lower bound (PC-CRLB) in the presence of missing data and measurement origin uncertainty, which are scaled by an improved information reduction factor (IIRF). Second, an overall cost function is formulated based on the derived PC-CRLB to quantify the global multi-target tracking performance. Combined with the practical resource constraints of the multi-radar architectures, the formulated JMSPA problem is shown to be an NP-hard optimization problem containing coupled continuous and binary variables. Third, we propose an efficient solver integrating convex relaxation with accelerated penalized sequential convex programming (AP-SCP) algorithm for solving this problem. Finally, numerical simulation results demonstrate that the proposed JMSPA has better performance than the traditional resource allocation strategy that ignores missing data, and is robust to resource allocation in different missing data scenarios.

Traditional forward-looking imaging methods of real aperture radar yield unsatisfactory performance in the presence of outliers. In this article, a method based on variational Bayesian (VB) is proposed to obtain forward-looking imaging in the presence of outliers. First, considering the non-Gaussian property of the imaging noise due to the outliers, we propose to use the Student- $t$ distribution to model noise. In this model, the echo signal does not need preprocessing for the outliers. Second, the Laplace hierarchical distribution is introduced to describe the sparsity of the target. Then, the forward-looking imaging problem converts to the optimal problem. Finally, we give the VB derivation to solve the imaging parameter. To illustrate the imaging performance in the presence of outliers, the outliers are randomly added to some angles and the whole scene of the echo signal in the simulations, respectively. From the simulation results, we can see that the proposed method achieves excellent performance for forward-looking imaging in the presence of outliers.

In control engineering, numerical stablility and real-time are the two most important requirements for the matrix inversion. This paper presents an efficient and robust method for the field-programmable gate array (FPGA) calculation of the matrix inversion. We initially consider the scenario that the matrix to be processed is a non-singular Hermitian matrix. The proposed computation procedures are composed of the matrix decomposition, triangular matrix inversion and matrices multiplication. The first procedure is completed by LDL factorization based on the outer form of Cholesky'smethod, while the recursive algorithm for block sub-matrices is adopted to achieve the triangular matrix inversion. The new method has the high level in the parallel pipelining mechanism and steals the characteristics of both the upper triangular matrix and its inversion to reduce the computation load and improve the numerical stability. Meanwhile, the non-Hermitian matrix inversion can be easily solved if another procedure is added in the new method. Finally, we compare our method with the exiting FPGA-based techniques on one Xilinx Virtex-7 XC7VX690T FPGA. Furthermore, it has solved one array antenna control problem of the adaptive digital beam forming for one phased array radar successfully.