Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network

Author:

Ai Xiaofeng,Zheng YuqingORCID,Xu Zhiming,Zhao Feng

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on adaptive selection method of radiation sources in passive radar based on GNSS signal;Journal of Computational Methods in Sciences and Engineering;2023-12-15

2. Multistatic Localization Algorithm for Moving Object with Constant Acceleration Eliminating Extra Variables;Signal Processing;2023-08

3. Characteristics of Target Crossing the Baseline in FSR: Experiment Results;IEEE Geoscience and Remote Sensing Letters;2023

4. Detection method of forward-scatter signal based on Rényi entropy;Journal of Systems Engineering and Electronics;2023

5. Target Acceleration Estimation in Active and Passive Radars;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

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