The accurate extraction of micro-Doppler (m-D) curves is crucial for target recognition for space targets. However, the strong overlap of m-D curves and the noise disturbance of multi-component non-stationary signals in the time-frequency domain make it a great challenge to separate these curves using conventional algorithms. To address this problem, a curve extraction algorithm for multicomponent m-D signals based on the reassociation Viterbi algorithm (RVA) is proposed in this paper. First, the Viterbi algorithm is combined with an adaptive curve removal method for curve detection in a multicomponent m-D signal. Then, a two-step association method based on distance and velocity is presented to achieve correct curve separation. In addition, a variable threshold algorithm is introduced to extract both strong and weak components. Finally, the improved validity and robustness of the proposed algorithm have been verified by experiments compared to conventional methods.

Recently, forward-scatter radars (FSRs) utilizing the Global Navigation Satellite System (GNSS) as a radiation source have gained increasing attention. The radar system enables aerial target surveillance by deploying multiple receiving nodes on the ground. It offers a low-cost and easily deployable solution. Therefore, how to deploy the receiving nodes to achieve efficient utilization of node resources is an urgent problem to be addressed. In this paper, a deployment method was proposed for receiving nodes in a single-transmitter and multiple-receiver configuration. First, the problem was reformulated as an optimal equal-circle covering problem via geometric approximation. A multi-objective optimization model was subsequently established with the objective functions of minimizing node cost and maximizing spatial detection area. Second, a method based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was introduced to obtain the sub-optimal solution of node cost, thereby reducing the computational complexity of the optimization process. Finally, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to derive the deployment schemes. Then, these schemes were ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the Entropy Weight Method (EWM). The results indicate that the proposed method can obtain the optimal deployment scheme compared to the existing method and enhance the diversity of the solutions.

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed.

The micro-range curve extraction of scattering centers is significant for estimating the motion and structural parameters of space targets. The curves are often extracted from high-resolution range profile (HRRP) for its range dimension information. However, most of the existing curve extraction algorithms based on HRRPs are with the accuracy of curves limited by range resolution. Moreover, due to noise interference, it is difficult to achieve extraction under low signal-to-noise ratio (SNR). Therefore, a super-resolution micro-range extraction algorithm based on the parameter correlation between micro-range curves and micro-Doppler (m-D) curves is proposed in this paper. Firstly, a parametric curve model is constructed and a rough parameter search of model is conducted to obtain the initial range curve, which ensures the robustness and real-time performance. Secondly, time-frequency analysis is applied to the range bins of the curve, and the m-D curve is refined by local maxima search to further improve the accuracy. The accurate micro-range curve is then reconstructed with the absolute range acquired by a one-dimensional search. Finally, simulation and experiment are carried out to verify the effectiveness and superiority of the proposed algorithm, which can achieve a better accuracy when SNR is -10 dB, compared with existing methods.

Ballistic missile warheads commonly undergo precession during the mid-course phase of their trajectory. The three-dimensional reconstruction can obtain fine features of precession warhead, which is of great significance for target recognition. However, as the foundation of three-dimensional reconstruction, the feature extraction for micro-motion targets faces many challenges. The occlusion effect, accompanied by noise interference, results in discontinuous feature curves, which hinder the correct separation of the curves. To address these issues, a three-dimensional reconstruction method based on radar network and deep learning-assisted feature extraction is proposed. Firstly, a high-resolution range profile (HRRP) repair network is proposed, which incorporates residual swin transformer blocks (RSTB) to obtain a continuous time-range distribution. Then, a phased extraction strategy is adopted to achieve the correct separation of micro-range curves under scenarios with severe overlap. Finally, the distance variance sequence is calculated iteratively for micro-range curves to achieve the matching of the same scattering center from different radars, and the three-dimensional coordinates of scattering centers are reconstructed through matrix decomposition. The dynamic simulation of electromagnetic (EM) calculation shows that the proposed method has superior reconstruction performance compared to existing algorithms.

Target recognition methods based on feature extraction of bistatic radar polarimetric characteristics face the challenge of feature variation caused by independent rotation of transmit and receive polarization bases. Based on Huynen decomposition and Cameron decomposition theories, this paper rigorously derives ten types of bistatic polarization roll-invariants covering scattering intensity, polarization modulation capability, scattering mechanism, symmetry and reciprocity. Simulations are systematically carried out on dihedral, sphere and trihedral structures, followed by anechoic chamber experimental verification using a self-built bistatic full-polarimetric electromagnetic scattering measurement system. Simulation and experimental results demonstrate that all proposed invariants strictly satisfy roll-invariance under independent rotation of transmit and receive polarization bases. Polarization roll-invariants of different structures exhibit distinct variation laws with bistatic angles, which can provide robust and reliable feature support for bistatic polarimetric radar target recognition.