The temporal-spatial focusing characteristic of time reversal technology has unique advantages in spatial power coherent synthesis. This paper studies the adaptive spatial power coherent synthesis technology based on time-reversal, discusses and analyses the influence of random phase errors and sampling time width on the signal synthesis effect at the target radar, and combines with simulation experiments to provide the principles for controlling random phase errors and selecting sampling time width, thereby providing a theoretical basis for the engineering application of time-reversal coherent synthesis technology.
In order to solve nonlinear problem caused by different coordinates in measurement and target state models during radar target tracking, a converted measurement Kalman filtering algorithm based on Taylor re-expansion with the predicted information (TREP-CMKF) is proposed in this paper. Based on second-order Taylor expansion, the first and second moments associated with the measurement conversion error are obtained, which eliminates the effect of measurement noise on error covariance by using predicted information. At the same time, the first-order expansion error in second order Taylor expansion is considered based on the predicted information to further reduce the conversion error and improve the consistency. In addition, the conversion and estimation error ellipses in Cartesian and polar coordinates are analyzed, and the hybrid Kalman filtering (HybridKF) algorithm is proposed, which combines the advantages of converted measurement method and square-root cubature Kalman filtering. Simulation results show that the proposed TREP-CMKF algorithm has good consistency and credibility in both measurement conversion and filter estimation, and the HybridKF algorithm has the highest tracking accuracy in all dimensions compared with existing converted measurement methods and nonlinear filter algorithms.
This study addresses electromagnetic scattering modeling of irregularly shaped particle clouds formed from air target disintegration at high altitude and proposes an integrated approach that couples dynamics with electromagnetic computation. The dynamic distribution of particles under the influence of both gravity and air resistance is simulated by means of the explosive motion equation. The geometric shape of the particle clouds is constructed using a three-dimensional convex hull algorithm; the particle clouds are partitioned based on the Kmeans clustering technique to calculate the local density. A generalized Rayleigh-Gans (GRG) approximation is used to obtain the scattering characteristics for single particles. To account for multiple scattering within the cloud, the Monte Carlo technique is employed under the framework of vector radiative transfer theory, which involves statistically sampling key photon parameters (entry points, free paths, and scattering angles) to estimate the full-angle scattering echoes. Moreover, simulation indicates that the radar cross-section of particle clouds is positively correlated with factors such as frequency and particle size. This method is effective for analyzing the electromagnetic scattering characteristics of particle clouds with complex shapes.
Array reconfiguration is significantly effective in decreasing the complexity and design cost of polarization-sensitive arrays (PSAs) applied to anti-interference. Nevertheless, PSA reconfiguration relies on prior information of the direction of arrival (DOA) and polarization characteristics of incoming signals, which frequently exhibit unforeseen inaccuracies in practical applications. These deviations can induce steering vector (SV) mismatches, adversely affecting both PSA reconfiguration and adaptive processing, making it unattainable to achieve an optimal reconstructed PSA with maximum anti-interference performance. This work presents a robust PSA reconfiguration method to address the issue of decreased anti-interference performance in reconfigurable PSAs in scenarios with SV mismatches induced by spatial-polarization deviations. Firstly, a polarization spatial mismatch coefficient (PSMC) is defined to analyze the sensitivity of array configuration to SV deviations and establish the relationship between PSMC and the optimal signal-to-interference-plus-noise ratio (SINR). Subsequently, an uncertainty set encompassing errors in both DOA and polarization parameters is defined. Given the scenario of maximum SINR degradation due to SV mismatch, a PSA reconfiguration problem model is formulated to maximize SINR. A sequential convex programming (SCP) algorithm is presented to solve the reconstructed PSA with high SINR despite SV mismatch. Numerical simulations validate the correctness and effectiveness of the proposed method.
In this paper, the issue of resource management of multiple input multiple output radar network for improving multitarget tracking performance with consideration of low probability of intercept (LPI) is investigated. The resources, which include node selection, system power, and dwell time, are dynamically allocated to improve multiple targets tracking performance under the requirement of a certain LPI level. Specifically, the tracking performance is described by the posterior Cramér-Rao lower bound, and the LPI level is expressed by the radar Schleher intercept factor. A resource allocation problem based on the MinMax criterion is established. Since the MinMax method needs to achieve self-adjustment and optimization in a dynamic environment to cope with constantly changing situations, this leads to a complex solving process and difficulty in solving. Towards that end, a two-stage optimization algorithm based on semi-definite programming is developed. Finally, numerous simulations are developed to verify the effectiveness of the proposed algorithm.
