基于INS/GNSS紧耦合组合的逐步诱导式欺骗检测算法研究

传统惯性辅助GNSS欺骗信号检测方法对小于纯惯导误差漂移的逐步拉偏不敏感。针对这一问题,提出了一种基于INS/GNSS紧耦合组合的逐步诱导式欺骗信号检测方法。基于短时间内纯惯性导航结果和短时间惯导系统位置误差传播模型,利用惯导提供的位置和速度对伪距和伪距率时间序列进行预测估算,并结合伪距、伪距率实际测量结果,分别构造位置/伪距和速度/伪距率时间序列模型做参数拟合。基于真实信号得到的模型参数都是由惯导系统误差造成的,具有一致性;而基于逐步诱导式欺骗信号得到的模型参数还与欺骗策略的设定、载体飞行轨迹的观测以及干扰源的位置有关,具有不一致性。通过对比模型参数可判别欺骗信号的存在,对于真实信号,采用紧耦合组合导航修正INS输出;对于欺骗信号,继续惯性导航并采取相应措施。最后,根据逐步诱导式欺骗原理进行实验仿真,验证了算法的可行性和有效性。

Research on Algorithm of Gradually Induced Spoofing Detection Based on Tightly Coupled INS/GNSS Integration

The traditional inertial aided GNSS spoofing detection method is insensitive to stepwise biasing that is less than the error drift of pure inertial navigation. In order to solve this problem, a gradually induced spoofing detection algorithm based on tightly coupled INS/GNSS integration is proposed in this paper. Based on the pure inertial navigation solutions in a short time and the position error drift model of the short-time inertial navigation system (INS), the position and velocity provided by INS are used to predict the pseudo-range and pseudo-range rate time series, and combined with the measurement results of pseudo-range and pseudo-range rate, the position/pseudo-range and velocity/pseudo-range rate time series models are constructed respectively for parameter fitting. Based on the real signals, the model parameters caused by INS errors are consistent. But based on gradually induced deception signals, the model parameters, which are also related to the setting of the deception strategy, the observation of the carrier flight trajectory and the position of the interference source, are inconsistent. By comparing the model parameters, the spoofing signals can be detected. For real signals, tightly coupled navigation is used to correct the INS output. For spoofing signals, inertial navigation persists and corresponding measures are taken. Finally, according to the principle of gradually inducing spoofing, experimental simulations are performed to verify the feasibility and effectiveness of the proposed algorithm.