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.

This paper presents a comprehensive measurement and calibration methodology for full-polarization characterization of radar targets at sub-terahertz (THz) frequencies. A novel three-object polarization calibration technique is proposed to address amplitude and phase imbalances in the measurement system. The method is implemented using a sub-THz radar cross-section measurement platform with twist waveguides for polarization switching. Experimental validation in an anechoic chamber demonstrates the method's effectiveness, achieving phase mismatch within 20°, polarization isolation better than 15 dB, and co-polarization amplitude imbalance below 2 dB. The calibrated data's accuracy is further confirmed through Cameron decomposition, showing excellent agreement with theoretical predictions. This work provides a robust framework for precise polarization characterization in the sub-THz regime, with potential applications in radar target recognition and imaging.

Forward scatter radar (FSR) can achieve target detection by capturing the signal disturbance caused by targets crossing the baseline, which provides a countermeasure to stealth/small targets. Firstly, the FSR net model based on the Global Navigation Satellite System (GNSS) is constructed. Then, the statistical features in the time and frequency domains are used to describe the fluctuation characteristics of the forward-scatter signal. Moreover, the influences of the relative position of the target and baseline on the statistical features are analyzed through dynamic simulations, and the change regulation of time and frequency features is revealed. Finally, a FSR experiment is carried out in an anechoic chamber and the results validate the theoretical analysis. This work provides a meaningful reference for detection and parameter estimation of targets crossing the baseline in a FSR.

Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.

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.

The parameter estimation in the forward scatter radar (FSR) network is the focus of attention in the radar field. In this letter, a parameter estimation method using the crossing time difference and the Doppler rate is proposed in the FSR network. The continuous position and the velocity estimation of the uniform moving target are realized. Then, the factors affecting the estimation accuracy are theoretically analyzed, including parameter measurement error and receiver locations. The effectiveness and the adaptability of the method are validated through Monte Carlo simulations. Compared with the parameter estimation method only based on the crossing time, higher accuracy can be achieved, and fewer receivers or transmitters are needed, so it should be easier to be deployed in the real application.