An Improved SDIF Radar Pulse Signal Main Sorting Algorithm

Xiao, Wei Hua; Wu, Hong Chao; Yang, Cheng Zhi; Wang, Mei Ling

doi:10.4028/www.scientific.net/amr.710.637

Cited by 7 publications

(5 citation statements)

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“…Once the quantity of TOA differences distributed in the PRI bin corresponding to real PRIs is reduced to below the threshold, there is a failure in extracting the candidate PRIs. To deal with this problem, the improved SDIF methods [22] and the improved PRI transform methods [23,24] adopt overlapped PRI bins to histogram. The centers of overlapped PRI bins are the same as the original PRI bins, while the widths are required to be greater than the PRI jitter value in general.…”

Section: Introductionmentioning

confidence: 99%

A novel method for deinterleaving radar signals: First‐order difference curve based on sorted TOA difference sequence

Xie

Zhao

et al. 2022

IET Signal Processing

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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.

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Section: Introductionmentioning

confidence: 99%

A novel method for deinterleaving radar signals: First‐order difference curve based on sorted TOA difference sequence

Xie

Zhao

et al. 2022

IET Signal Processing

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“…It has been noted that clustering techniques [10][11][12][13][14], as defined separately from TOA-based histogram techniques [15][16][17][18][19], have deficiencies that can be overcome by combining them with TOA-based techniques, such as two RADARs located in the same direction that have similar parameters and cannot be deinterleaved using clustering (as defined separately). A subtle conceptual aspect of reference [10] is that the concept of model space emerges persistently throughout that analysis, although not emphasized conceptually with respect to inverse analysis theory.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for deinterleaving of RADAR scans for their analysis by ELINT systems

Warren,

Allanigue,

Outlaw

et al. 2024

Pattern Recognition and Tracking XXXV

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A case study analysis is presented demonstrating the procedure of deinterleaving, and its characteristics in terms of parameter sensitivity, which is for PRI-modulation recognition associated with ELINT analysis. This study examines data obtained from a RADAR collection site, consisting of various PRI-modulated characteristics, such as those occurring typically for ELINT-analysis environments. A specific aspect of this analysis is that it considers deinterleaving of RADAR pulses, which represent a precursor data format collected in a tactical maritime environment, to construct RADAR scans. Ultimately, the goal of this analysis is to construct these RADAR scans to eventually construct and identify RADAR signals, which represent a post-processed data format with respect to scans, in order to determine the geolocation of maritime targets. We present initial results of an evolving data-driven methodology based on the types of data being processed.

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“…Based on the autocorrelation function [5], the PRI transform adds a phase rotation factor to solve the sub‐harmonic problem, but the calculation cost increases significantly. Since the beginning of the 21st century, many improved versions of SDIF [6–9] and PRI transform [10, 11] have been proposed. Some of them are implemented on a digital signal processor (DSP) [12] or field‐programmable gate array (FPGA) [13, 14].…”

Section: Introductionmentioning

confidence: 99%

“…Some of them are implemented on a digital signal processor (DSP) [12] or field‐programmable gate array (FPGA) [13, 14]. The improved SDIF can deinterleave and search the jittered signals, but only the pulse trains in small jitter [6, 7]. The improved PRI transform can deinterleave large jittered signals [10] but has problems such as poor accuracy of PRI value estimation, a large amount of calculation, and inadequacy of staggered signals.…”

Section: Introductionmentioning

confidence: 99%