Single channel interference and signal blind separation method based on mutation particle swarm particle filtering

Abstract

The invention provides a single channel communication signal and interference blind separation method based on mutation particle swarm particle filtering (MPSOPF-SCBSS), and belongs to the communication signal processing and communication anti-interference field. In view of the single channel communication signal and interference blind separation problem in the condition of large interference-to-signal ratio, small number of particles and low signal-to-noise ratio, the method adopts Bayesian filtering estimation for communication signal and interference separation. According to the method, a single channel disturbed communication signal state space model is firstly built, the single channel interference and signal blind separation problem is converted into joint estimation of communication symbols and unknown system parameters, and joint estimation is realized through mutation particle swarm optimization particle filtering to achieve the blind separation purpose; through a velocity-displacement update function and a Metropolis-Hastings mutation rule in the particle swarm optimization, the needed particle number in the sequential estimation process is reduced and diversity of the particle set can be ensured. In addition, the provided mutation particle swarm optimization rule reduces demands on the signal-to-noise ratio of communication signals in the single channel disturbed condition.

Description

technical field [0001] The invention provides a method for blind separation of interference signals in the case of single-channel reception, in particular to a single-channel communication signal and interference blind separation method (MPSOPF-SCBSS) based on mutated particle swarm resampling particle filter, which belongs to communication signal processing and Communication anti-jamming field. technical background [0002] Blind separation of communication signal and interference is one of the means to improve the reliability of communication system. Different from the principle of spectrum expansion and interference suppression and anti-jamming in spread spectrum communication, interference separation adopts blind signal processing method to decompose, estimate and separate components of the received disturbed signal, which can maximize the Separating communication signals and interference to the greatest extent, increasing the anti-interference tolerance of the system, ...

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Problems solved by technology

[0004] At present, there are three main solutions to the problem of blind separation of single-channel communication signals: one is to convert a single channel into multiple channels through oversampling, and then use the multi-channel blind separation method for processing. The disadvantage of this method is that it is sensitive to noise. And when the signal has frequency deviation and phase deviation, the separation effect is not good

The second is to use the cyclostationary characteristics of communication signals to construct filters in the cyclic spectral domain for separation. The difficulty of this method is that not all signal combinations have differences in the cyclic spectral domain

The third is to transform the blind separation of signals into the joint estimation problem of communication symbols and unknown parameters in the state space model according to the principle of Bayesian filtering, and separate communication signals and interference through sequential estimation. The advantage of this method is that it is based on the analysis of signals The expression is based on a strict theoretical basis, and the parameters to be solved (such as information symbols, signal or interference frequency offset, phase offset, symbol bit timing, channel transmission parameters, etc.) can be used as the parameters in Bayesian estimation The target to be estimated has a relatively flexible processing method; the current research on this aspect is mainly limited to the blind separation of two communication signals with basically the same power, and there is no research on blind separation in the presence of malicious interference (artificial signal ratio), and the existing The algorithm requires the use of a large number of particles to approximate the probability density distribution, and the separation effect is not ideal when the signal-to-noise ratio of the communication signal is low (current research generally believes that for two BPSK signals with equal power, in the case of 4 times oversampling Next, if the bit error rate is required to be less than 10 -4 , the SNR of the BPSK signal must be greater than 15dB)

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