Parameter Estimation of Frequency-Hopping (FH-SS) Signals Using Modified Autocorrelation Techniques

Variable-Speed Frequency-Hopping Signal Sorting: Spectrogram Is Sufficient

“…The duration clustering based on the DBSCAN algorithm is performed on the connected domains, and the density parameter of sample distribution within the neighborhood is set to (30,1), which represents clustering with at least one point in a circular neighborhood with a radius of 30. The clustering result is shown in Figure 6, where labels [1,2,4,5] are category 1, label 3 is category 2, labels [6,8,10,11,14,15,17,19,20,22,24] are category 3, and labels [7,9,12,13,16,18,21,23] are category 4. Due to the duration of category 1 being equal to the observation time, labels [1,2,4,5] belong to fixed-frequency interference signal.…”

“…The clustering result is shown in Figure 6, where labels [1,2,4,5] are category 1, label 3 is category 2, labels [6,8,10,11,14,15,17,19,20,22,24] are category 3, and labels [7,9,12,13,16,18,21,23] are category 4. Due to the duration of category 1 being equal to the observation time, labels [1,2,4,5] belong to fixed-frequency interference signal. The duration of categories [2,3,4] is much shorter than the observation time, so categories [2,3,4] belong to FH signals, which include fixed-speed FH signals and variable-speed FH signals.…”

“…Due to the duration of category 1 being equal to the observation time, labels [1,2,4,5] belong to fixed-frequency interference signal. The duration of categories [2,3,4] is much shorter than the observation time, so categories [2,3,4] belong to FH signals, which include fixed-speed FH signals and variable-speed FH signals. The random duration of variable-speed FH signals makes it difficult to sort FH signals according to duration, and we adopt the proposed time alignment algorithm to solve this problem.…”

“…The sorting of FH signals is a fundamental problem in the reconnaissance of FH signals [1]; extensive studies have been devoted to this area and many methods have been proposed to precisely sort FH signals from multiple networks. The multiple-hop autocorrelation processing is considered a traditional means of estimating the parameters and sorting the networks of FH signals in the presence of broadband thermal noise, which is dependent on prior information [2]. In [3], a shortwave signal detection algorithm based on spectral energy statistics was proposed, utilizing the differences in the energy spectrum characteristics of shortwave signals.…”

Towards Real Time Interception of Frequency Hopping Signals

“…This paper proposes a method to intercept a FH signal using SDR by estimating the used channel. Some studies on FH channel detection exist but only deal with few hops per seconds [6][7] or are based on complex methods such as particle filtering [8], autocorrelation techniques [9] or timefrequency image processing [10]. Moreover, these methods do not cope with very large instantaneous bandwidths and can not deal with fast FH signals in real time.…”