Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

Abstract

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM

“…In the FT strategy, different work mode classes in the sequence transit randomly. While in INFT strategy, classes transit according to a transition matrix used in [19], which imitating the mode transition relationship of a real‐world MFR. Based on the transition matrix, radar is more likely to stay in the current work mode than transit to another, causing an infrequent transition in the sequence.…”

“…Thus, a well‐scheduled work mode sequence performing the desired radar task can be described in a syntactic way. The hierarchical model and the syntactic modelling of MFR signals in their study have been widely referred to in various research studies related to MFR work mode [7, 12–21].…”

“…After that, many work mode recognition methods were proposed based on the hierarchical model. These methods usually performed the recognition task after a word sequence was extracted from the input pulse stream [15, 16, 18–20]. These two‐stage recognition methods worked well on early MFRs like Mercury and Pluto radars [11].…”

Towards Self-supervised Learning for Multi-function Radar Behavior State Detection and Recognition

“…We build the hypothetical MFR according to the Mercury radar because lots of the current researches on MFR recognition is based on it [30], [10]. The radar has five functional states namely: search(S), acquisition(A), nonadaptive track(NT), range resolution(RR), and track maintenance(TM).The functional states transition is assumed to be a Markov chain and the resulting radar phases, words sequences follow a hidden Markov model, the radar phases corresponding to each functional state is illustrated in TABLE I.…”

Deep Q-Network with Predictive State Models in Partially Observable Domains

“…e commonly used technique for solving such partially observable problems is to model the dynamics of the environments by using the POMDP approach or the PSR approach firstly [3,12] and then the problem can be solved using the obtained model. Although POMDPs provide general frameworks to solve partially observable problems, it relies heavily on a known and accurate model of the environment [17]. erefore, in real-world applications, it is extremely difficult to build an accurate model [18].…”