Radar signal deinterleaving is used to separate interleaved pulse streams in the electronic support measure (ESM) systems. The histogram methods based on the difference in time of arrival (TOA) are more mature in engineering applications than other methods because of their more effortless implementation. In the increasingly complex electronic battlefield, pulse repetition interval (PRI) jitter, and pulse missing are inevitable in the interleaved pulse stream. The TOA differences of pulse trains with PRI jitter distributes in multiple adjacent PRI bins, which results in the significant reduction of the corresponding histogram value. Furthermore, the histogram values of real PRIs vary with pulse missing, which indicates that the histogram lacks robustness to pulse missing. First‐order difference curve based on sorted TOA difference sequence, denoted as FDC‐DTOA, is proposed in this paper to overcome the shortcomings mentioned above. In this method, multi‐order TOA differences are calculated and then sorted in ascending order. Then, FDC‐DTOA is obtained after a first‐order backward difference operation. Finally, an adaptive threshold is applied to FDC‐DTOA to extract candidate PRIs. The simulation results show that the proposed method has remarkable robustness to pulse missing and excellent performance in deinterleaving the pulse stream with PRI jitter.

In pulse deinterleaving, the search procedure may fail because of missing pulses and pulse repetition interval (PRI) jitter. This study proposes a search method based on the directed acyclic graph (DAG). In this graph, pulses are vertices and the extracted pulse pairs are arcs. The greater the length of the “path”, the higher is the confidence level of the corresponding pulse sequence. Therefore, this problem is transformed into a problem associated with the extraction of the longest “path” from DAG. Since pulse segments have more confidence than individual pulses, the proposed method may have important implications and broad application prospects.

Dwell and switch (D&S) pulse repetition intervals (PRIs) are vital in modern radar systems. However, the existing deinterleaving methods that do not rely on prior information are generally ineffective at handling D&S signals. Considering the characteristics of D&S PRIs, spectrum analysis and maximal clique are employed in this study to deinterleave D&S signals in a prior unknown scenario. Sequence search technology is introduced for D&S subsequences to ensure complete D&S subsequences within the search results. Furthermore, D&S subsequence identification technology based on spectrum analysis is developed. Following vector processing of the search results, D&S subsequences are distinguished from fixed sequences based on the number of spectral peaks within the principal interval. Additionally, a subsequence consolidation technique based on the maximal clique is introduced, where the sequence search result is regarded as the vertex, and the existence of an edge between corresponding vertices is determined by evaluating the complementarity and mutual exclusion of the combination. Through the construction of an undirected graph, subsequences from the same D&S sequence are consolidated using the maximal clique extraction. The simulation results demonstrate that the detection threshold can effectively identify subsequences, whereas the complementarity and mutual exclusion constraints efficiently detect associations between subsequences, mitigating false association pairs from false alarms of fixed sequences. For a pulse missing rate of 30% or less, the analysis accuracy of D&S parameters reaches 98.23%, highlighting the outstanding performance and promising application prospects of the proposed method in deinterleaving D&S sequences and decoding their modulation patterns.

For prior-knowledge-informed scenarios, this paper proposes a radar signal deinterleaving method based on hidden Markov chains and residual fence networks (RFNs) with enhanced applicability for different pulse repetition interval (PRI) types and improved performance under PRI jitter. The proposed approach accommodates the five main PRI modulation types by modeling interleaved pulse streams as hidden Markov models (HMMs). Pulse deinterleaving is transformed into a state-sequence prediction problem using HMMs and further into a path optimization problem within RFNs. This method utilizes global information for reliable sequence separation. Experimental results indicate that the method effectively deinterleaves fixed, staggered, dwell-and-switch, sliding, and wobulated sequences under unintentional jitter, significantly improving performance under intentional jitter. The practical value and applicability of the method are demonstrated, providing a new perspective for deinterleaving. The code will be open at: https://github.com/xm980426/HMC-RFN.git .

Existing deinterleaving methods are limited to fixed and staggered pulse sequences, and cannot handle dwell & switch (D&S), sliding, and wobulated pulse sequences. This study proposes a new approach that models actual sequences as Markov chains using the pulse index as the time parameter and the pulse index of the ideal sequence as the state. The transformation turns radar pulse deinterleaving into a Markov chain-based prediction problem. Since the constructed Markov chain encompasses all the PRI modulation information and is applicable to different objects, the method has great potential in practicality and applicability, providing a fresh perspective and direction to deinterleaving.