The blind attack strategy based on Principal Component Analysis (PCA) is only effective for the measurement data with Gaussian noise. In the presence of outliers, the attack strategy will be detected by the traditional bad data detection module. Aiming at the problem of outliers, a blind attack strategy based on Robust PCA (RPCA) was proposed. Firstly, the attacker collected the measurement data with outliers. Then, the outliers and the real measurement data were separated from the measurement data containing outliers by the sparse optimization technique based on the Alternating Direction Method (ADM). Secondly, the PCA technique was carried out on the real measurement data, and the relevant information of the system was obtained. Finally, the acquired system information was used to construct the attack vector, and the false data was injected according to the attack vector. The experimental results show that the traditional attack method based on PCA will be detected by the bad data detection module in the presence of outliers, and the proposed method based on robust PCA can avoid the detection of bad data detection module. This strategy makes it possible to successfully implement False Data Injection Attack (FDIA) in the presence of outliers.

To deal with the emitter identification problem in Multi-Function Radar (MFR) based on Stochastic Context-Free Grammar (SCFG) model, a MFR emitter identification method based on Stochastic Infinite State Automata (SISA) was proposed on the basis of syntactic modeling. The grammar production rules in "Mercury" MFR control module and the characteristic production rules in "Mercury" MFR system were used in this method to reconstruct an SCFG, which was further used to construct an SISA for identification subsequently. Theoretical analysis and simulation results show that the proposed method can realize MFR emitter identification. Within a certain range, the average recognition rate can be improved by adding the amount of grammar production rules, and the identification performance is superior to Stochastic Push-Down Automata (SPDA) constructed by SCFG. The experimental results validate the reliability and effectiveness of the proposed method.

To deal with the probability learning problem in Multi-Function Radar (MFR) based on Stochastic Context-Free Grammar (SCFG) model, a new fast learning algorithm of grammatical probabilities in MFR based on Earley algorithm was presented on the basis of traditional Inside-Outside (IO) algorithm and Viterbi-Score (VS) algorithm. The intercepted radar data was pre-processed to construct an Earley parsing chart which can describe the derivation process. Furthermore, the best parsing tree was extracted from the parsing chart based on the criterion of maximum sub-tree probabilities. The modified IO algorithm and modified VS algorithm were utilized to realize the learning of grammatical probabilities and MFR parameter estimation. After getting the grammatical parameters, the state of MFR was estimated by Viterbi algorithm. Theoretical analysis and simulation results show that compared to the conventional IO algorithm and VS algorithm, the modified algorithm can effectively reduce the computation complexity and running time while keeping the same level of estimation accuracy, which validates that the grammatical probability learning speed can be improved with the proposed method.