The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as Pulse Repetition Interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This paper introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts, the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs, and that of encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.
The structure design and switching regulation of pulse groups in multi‐function radars (MFRs) are closely related to the work mode. The sequential extraction and recognition of MFR pulse group structure is a fundamental task to analysing and interpreting the work modes and behaviour intentions of an MFR. In this study, the temporal structure of MFR pulse group is expressed hierarchically, which is intensively modelled based on regular grammar. Besides, a corresponding hierarchical automaton is established to sequentially extract and recognise the MFR pulse group structure in pulse train. The hierarchical automaton used for pulse group recognition has a two‐layer structure. The bottom layer of the hierarchical automaton realises the sequential input of pulses and recognition of pulse subgroups, and the sequential input of pulse subgroups and recognition of pulse groups are realised at the top layer. The simulation results demonstrate that the proposed method performs satisfyingly in recognising the pulse group structure and is robust to not only pulse noises but also to the emitter number and pulse group scale.
Many modern radars use variable pulse repetition intervals (PRI) to improve anti-reconnaissance and anti-jamming performance. Their PRI features are probably software-defined, but the PRI values at different time instants are variable. Previous statistical pattern analyzing methods are unable to extract such undetermined PRI values and features, which greatly increases the difficulty of Electronic Support Measures (ESM) against such radars. In this communication, we first establish a model to describe the temporal patterns of software-defined radar pulse trains, then introduce the recurrent neural network (RNN) to mine high-order relationships between successive pulses, and finally exploit the temporal features to predict the time of arrival of upcoming pulses. In the simulation part, we compare different time series prediction models to verify the RNN’s adaptability for pulse sequences of variable parameter radars. Moreover, behaviors of different RNN units in this task are compared, and the results show that the proposed method can learn complex PRI features in pulse trains even in the presence of significant data noises and agile PRIs.
An important task in the Electronic Support Measures (ESM) field is analyzing and recognizing radar signals. Feature extraction is one of the primary key elements of radar emitter recognition algorithms. Current research mainly finds statistical features such as the mean and variance of parameters from pluses as the input features of the classifier. However, data noise in intercepted pulse signals greatly interferes with the accuracy of the extracted statistical features and seriously affects the recognition rate of radar emitters. In this paper, we proposed a method of radar emitter recognition. We first clustered parameter sets to establish a set of frequent items and their corresponding clustering centers. Next, we concatenated the clustering centers of each frequent item into a feature vector associated with the data volume dimensions. Then, we built a decision tree classification model based on the feature vector, and finally we used the learned model for the recognition of unknown radar pulse trains. The simulation results show that the proposed method has better robustness when applied to a variety of data volumes and data noise scenarios compared with long short-term memory (LSTM) and support vector machine (SVM) methods.
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