Method for wireless communication high-precision signal identification and baud rate parameter estimation
A technology of parameter estimation and signal recognition, which is applied in modulation type recognition, digital transmission system, modulated carrier system, etc. It can solve the problems of unsatisfactory recognition rate, influence of eigenvalue accuracy, and high computational complexity, so as to avoid over-learning and partial Convergence, ensuring the safety of citizens' property, and resisting the effect of Gaussian white noise
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Embodiment 1
[0077] Such as figure 1 As shown, the method for high-precision signal identification and baud rate parameter estimation for wireless communication according to the present invention has specific steps as follows:
[0078] S1: Perform high-order cumulant processing on the signal to be measured, and extract the characteristic parameters of the signal to be measured;
[0079] High-order cumulants have good anti-noise performance and are widely used in signal processing. In general, the transmission signal and Gaussian noise in the channel are independent of each other, and the cumulative amount of Gaussian noise higher than the second order is zero. Therefore, converting the received signal into a high-order cumulant for processing can eliminate Gaussian noise (8-9).
[0080] Assuming that we are in a continuous and synchronized environment, and the time and carrier synchronization and waveform recovery have been completed, then a complex baseband sequence of the signal to be measured...
Embodiment 2
[0135] Such as Figure 5 As shown, this embodiment is different from Embodiment 1 only in that after the step of extracting characteristic parameters of the signal, the following steps are further included:
[0136] S2’: Perform clustering optimization processing on the characteristic parameters:
[0137] S21’: Set the iteration criterion ε=10 -5 , Initialize the feature parameter classification matrix V (0) ;
[0138] S22’: Calculate feature parameters and update membership matrix U (k) :
[0139] S23’: Calculate the characteristic parameter clustering center matrix V (k+1) :
[0140] S24’: Compare V with the norm of the characteristic parameter matrix (k+1) With V (k) , If ||V (k+1) -V (k) ||≤ε;
[0141] Then the iteration stops, and the characteristic parameters after clustering optimization are obtained. Otherwise, let K=K+1, and go to step S22'.
[0142] Through the processing of this step, the required iteration time can be minimized under the condition of setting the recognition ...
Embodiment 3
[0144] Such as Image 6 As shown, this embodiment is different from Embodiment 1 only in that: in step S4, the spectral resolution is increased, specifically as follows:
[0145] S41': Sampling the signal to obtain the digital sequence X(n);
[0146] S42': According to the set spectral resolution, divide the digital sequence X(n) into corresponding subband signals x N (n);
[0147] S43': For the subband signal x N (n) Obtained by Hilbert transform Hilbert Calculate the envelope square of the subband signal
[0148] S44’: For the envelope square e x (n) Perform Fourier transform and search for its peak in the frequency domain, and set the frequency corresponding to the peak as f d1 ;
[0149] S45’: In [f d1 -f s / N,f d1 +f s / N] to the envelope square e x (n) Perform chirp Z transformation CZT transformation, then search for its peak value, and set the frequency corresponding to the peak value as f d2 ;
[0150] S46’: Search for the second peak value in the result of chirp Z transform C...
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