A Novel Method for Deinterleaving of Radar Signals Using Blind Source Separation

2013 ◽  
Vol 411-414 ◽  
pp. 1053-1057
Author(s):  
Wei Lu ◽  
Ming Ma
Keyword(s):  
Blind Source Separation ◽  
Computer Simulations ◽  
Pulse Train ◽  
Source Separation ◽  
Important Task ◽  
Pulse Repetition ◽  
Radar Signal ◽  
Novel Method ◽  
Radar Signals ◽  
Repetition Interval

In the modern wars under the condition of informationalization, radar signals are generally characterized by repetitive patterns in time, so it is one important task of Radar Warning Receiver (RWR) to estimate the Pulse Repetition Interval (PRI) of all radar signals by intercepting and identifying the mixed radar signals. Because each transmitted radar signal are arbitrary in general. From the view of statistic, the transmitted radar pulse train from different radars is mutual independent. So the constituted model of RWR and radar transmitters accord with blind source separation which can separate mixed signals into pure signals. A deinterleaving method using blind source separation for estimating the PRI of each radar signals is proposed. The implementation architecture of the proposed method is given. Finally, computer simulations show the proposed method can gain good performance for the estimation of PRI of radar signals.

scholarly journals A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

2017 ◽  
Vol 7 (4) ◽  
pp. 1906 ◽  
Author(s):  
Abouzid Houda ◽  
Chakkor Otman
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real Life ◽  
Signal Separation ◽  
Mixing Process ◽  
The Real ◽  
Input Signals ◽  
Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the  nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation.  But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation

2020 ◽  
Vol 131 (2) ◽  
pp. 425-436 ◽  
Author(s):  
Teppei Matsubara ◽  
Naruhito Hironaga ◽  
Taira Uehara ◽  
Hiroshi Chatani ◽  
Shozo Tobimatsu ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Novel Method ◽  
Fractional Type

A Novel Method to Estimate the Unknown Mixing-Matrix for Blind Source Separation

2012 ◽  
Vol 605-607 ◽  
pp. 2206-2210
Author(s):  
Ning Chen ◽  
Hong Yi Zhang
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Membership Function ◽  
Optimization Algorithm ◽  
Source Separation ◽  
Reliable Data ◽  
Mixing Matrix ◽  
Novel Method ◽  
Effectiveness And Efficiency ◽  
The Impact

The blind source separation (BSS) using a two-stage sparse representation approach is discussed in this paper. We presented the algorithm based on linear membership function to estimate the unknown mixing matrix precisely, and then, the optimization algorithm based on integral to get the max value of the function is proposed. Another contribution described in this paper is the discussion of the impact of noise on the estimating the mixing matrix. Given the impact of noise, we set weights to put more emphasis on the more reliable data. Several experiments involving speech signals show the effectiveness and efficiency of this method.

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