基于随机文法的多功能雷达识别方法

 针对多功能雷达采用复杂体制而造成的辐射源不能正确识别问题,提出了一种基于随机文法(SG)的辐射源识别算法。该算法基于多功能雷达的句法模型,将威胁数据库中的多功能雷达文法分为随机正则文法(SRG)和随机上下文无关文法(SCFG)两种情况,并分别构造随机有限自动机(SFA)和随机下推自动机(SPDA)对测量辐射源进行识别。仿真实验表明,该方法不仅能识别出多功能雷达辐射源的型号及模式,而且能识别出雷达辐射源的功能状态,并进而推断出雷达辐射源的威胁等级。

Approach to Multi-function Radar Identification Based on Stochastic Grammars

To deal with the problem of emitter identification caused by parameter complexity and agility of multi-function radars, this article proposes a new identification method based on stochastic grammar (SG) deduced from the syntactic modeling of multi-function radars. This method divides the grammar of multi-function radars in the electronic threat libraries into two aspects, stochastic regular grammar (SRG) and stochastic context-free grammar (SCFG), and constructs respectively a stochastic finite automaton (SFA) and a stochastic push-down automaton (SPDA) for identification. A number of simulations are presented to demonstrate the identification capability of the SG algorithm. Simulation results show that the proposed algorithm can not only infer the type and work mode of measured emitters correctly, but also evaluate their functional state and threat level.