This paper addresses the problem of moving source localization in 3-D using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without accurate sensor location information. Different from the previous studies, we focus on improving the localization performance by introducing two approaches with a limited number of sensors, including optimal sensor placement and velocity configuration, as well as successive measurements for a period. The paper first determines the sensor placement and velocity (including speed and orientation) configuration to achieve optimal localization based on a criterion devised from the Fisher information matrix (FIM), in which the restrictions for the two-stage weighted least squares (TSWLS) positioning algorithm and the geographical conditions are taken into consideration. Then, to utilize successive measurements while the source is in motion, we separate the sensor velocity into two components for achieving the localization optimality and compensating the source motion, and propose a practical sensor placement and velocity configuration strategy. Finally, combining with the TSWLS based prelocalization, Kalman filtering and the proposed sensor motion strategy, we develop a source tracking scheme that simultaneously obeys the optimal sensor placement and velocity configuration during tracking. Mathematical analyses and numerical simulations validate the optimal sensor placement and the efficiencies of the source tracking scheme.

In this paper, we propose an efficient direction of departure and direction of arrival estimation method for bistatic MIMO radar with faulty arrays. A third-order tensor model is built, and the measurement three-dimensional structure can be better utilized than the traditional matrix model. Subsequently, the atomic norm minimization technique is used to further improve the angle estimation performance. Furthermore, we found in the research process that when the faulty arrays still maintain the symmetry property, the measurement tensor can be converted to the real-valued domain by the forward-backward averaging technique and the unitary transform technique. The new algorithm we proposed exploits the multidimensional structure of the signal without estimating the signal subspace. Comparing with traditional matrix completion methods, it has better performance in terms of robustness and resolving correlated targets. And the algorithm proposed in this paper does not require angle pairing. Simulation results verify the effectiveness of the proposed algorithm.

Three-dimensional (3D) imaging based on metamaterial radar system is a cutting-edge technique which elevates the hardware burden in traditional real-aperture radar and synthetic aperture radar (SAR), particularly with the enhanced systematic degree of freedom (DOF) by the emerging technique of digtal coding metasurface. This paper proposes a single-channel real-aperture 3D imaging method based on digital coding metasurface, which is simple and low-cost. The proposed system can generate random radiation field, and the target scene can be obtained by sparse reconstruction as in microwave correlation imaging (MCI) system. In this paper, we first establish the system signal model. Then, we propose the index of the DOF of the radiation field, and propose the accordingly optimization method by optimizing the digital code. And in practical applications, the coupling between metamaterial units introduces additional signal components, which affects the quality of the sparse recovery. In order to solve the coupling problem, we take the coupling coefficients into account in the priori assumptions for all the parameters in the sparse Bayesian learning, and use the maximum posterior probability method to estimate the unknown parameters. Iteratively, the MCI model can be updated with the obtained coupling information, guaranteeing a more accurate reconstruction of the target scene. The simulation results prove the effectiveness of our method.

Faced with increasingly complex electronic jamming environments, intra-pulse agility has become a primary method of anti-interrupted-sampling repeater jamming (ISRJ) for radar systems. However, existing intra-pulse agile signals suffer from high autocorrelation sidelobe levels and limited jamming suppression capabilities. These issues restrict the performance of intra-pulse agile signals in complex electromagnetic environments.This paper proposes an anti-interrupted-sampling repeater jamming method based on Simulated Annealing–2-optimization (SA-2opt) parallel optimization of waveforms and fractional domain extraction. Firstly, the proposed method employs the SA-2opt parallel optimization algorithm to optimize the joint frequency and chirp rate encoding waveform. Then, the received signal is subjected to the fractional Fourier transform (FrFT) and inverse transform to extract the target signal. Finally, jamming detection is conducted based on the multi-dimensional features of the pulse-compressed signal. After this detection, a time-domain filter is constructed to achieve jamming suppression. The optimized waveform exhibits the following advantages: the sub-pulses are orthogonal to each other, and autocorrelation sidelobe levels are as low as -20.7dB. The method proposed in this paper can achieve anti-ISRJ in the case of a high jamming-to-signal ratio (JSR). Simulation experiments validate both the effectiveness of the optimized waveform in achieving low autocorrelation sidelobes and the anti-ISRJ performance of the proposed method.