A blind channel equalizer device for a radiofrequency receiver suitable for modulating the constant envelope signal of the transmission includes: an adjustable linear digital filter, defined at a point in time by the coefficients) thereof, able to filter an input signal in order to produce an output signal; an estimator able to estimate a power of the input signal; an adapter able to adapt the filter by calculating the coefficients of the filter at a point in time by subtracting, from the filter coefficients at a preceding point in time, the gradient of a cost function assigned with a correction coefficient. The cost function includes a first distance criterion between the square of the output signal and the power, wherein the correction coefficient is a product including a constant convergence coefficient and a scaling coefficient inversely proportional to the square of the power. Also disclosed is a related Radiofrequency receiver.

One example includes an equalizer system. The system includes a filter system configured to receive digital sample blocks associated with an input signal and to provide equalized digital sample blocks associated with the respective digital sample blocks based on adaptive tap weights. Each of the digital sample blocks includes samples and each of the equalized digital sample blocks includes equalized samples. The system also includes a sample set selector to select a subset of equalized samples from each of the equalized digital sample blocks at the output of the filter and an error estimator configured to implement an error estimation algorithm on the subset of the equalized samples to determine a residual error associated with the equalized samples. The system further includes a tap weight generator configured to generate the adaptive tap weights in response to the residual error and to provide the adaptive tap weights to the filter.

Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

Systems and methods for classifying a modulation scheme of a wireless signal are described. In some embodiments, a system receives a wireless signal modulated based on a modulation scheme having a constant modulus. The system can generate a resampled signal from the wireless signal based on features extracted from the wireless signal and perform blind equalization on the resampled signal based on a constant modulus criterion to generate an equalized signal. Then, the system can cause a modulation classifier to classify the received wireless signal to a modulation scheme from a plurality of predetermined modulation schemes based on the equalized signal. By preconditioning the wireless signal to reduce feature variability imparted by a propagation channel onto the wireless signal, the system can increase the classification accuracy of the modulation classifier.

One example includes an equalizer system. The system includes a filter system configured to receive digital sample blocks associated with an input signal and to provide equalized digital sample blocks associated with the respective digital sample blocks based on adaptive tap weights. Each of the digital sample blocks includes samples and each of the equalized digital sample blocks includes equalized samples. The system also includes a sample set selector to select a subset of equalized samples from each of the equalized digital sample blocks at the output of the filter and an error estimator configured to implement an error estimation algorithm on the subset of the equalized samples to determine a residual error associated with the equalized samples. The system further includes a tap weight generator configured to generate the adaptive tap weights in response to the residual error and to provide the adaptive tap weights to the filter.

A method for decreasing multi-path interference, for a vehicle radio receiver including at least two radio reception antennas that each receive a plurality of radio signals composed of time-shifted radio signals resulting from a multi-path effect. The plurality of radio signals combined to deliver a combined radio signal y.sub.s to be played, with: y.sub.n=W.sub.n.sup.T[G.sub.1,n.sup.S, X.sub.1,n+G.sub.2,n.sup.S, X.sub.2,n] at time n, where x.sub.1 and x.sub.2 are vectors the components of which correspond to the plurality of signals received by the first antenna and by the second antenna, respectively, G.sub.1,n.sup.S and G.sub.2,n.sup.S are scalars the components of which are the complex weights of a spatial filter and w.sub.n.sup.T is the transpose matrix of a vector the components of which are the complex weights of a temporal filter. The method includes implementation of an iterative adaptation algorithm to determine the complex weights of the spatial filter and the complex weights of the temporal filter.

One example includes an equalizer system. The system includes a filter system configured to receive digital sample blocks associated with an input signal and to provide equalized digital sample blocks associated with the respective digital sample blocks based on adaptive tap weights. Each of the digital sample blocks includes samples and each of the equalized digital sample blocks includes equalized samples. The system also includes a sample set selector to select a subset of equalized samples from each of the equalized digital sample blocks at the output of the filter and an error estimator configured to implement an error estimation algorithm on the subset of the equalized samples to determine a residual error associated with the equalized samples. The system further includes a tap weight generator configured to generate the adaptive tap weights in response to the residual error and to provide the adaptive tap weights to the filter.

A method for decreasing multi-path interference, for a vehicle radio receiver including at least two radio reception antennas that each receive a plurality of radio signals composed of time-shifted radio signals resulting from a multi-path effect. The plurality of radio signals combined to deliver a combined radio signal y.sub.s to be played, with: y.sub.n=W.sub.n.sup.T[G.sub.1,n.sup.S, X.sub.1,n+G.sub.2,n.sup.S, X.sub.2,n ] at time n, where x.sub.1 and x.sub.2 are vectors the components of which correspond to the plurality of signals received by the first antenna and by the second antenna, respectively, G.sub.1,n.sup.S and G.sub.2,n.sup.S are scalars the components of which are the complex weights of a spatial filter and w.sub.n.sup.T is the transpose matrix of a vector the components of which are the complex weights of a temporal filter. The method includes implementation of an iterative adaptation algorithm to determine the complex weights of the spatial filter and the complex weights of the temporal filter.

A jones matrix is obtained using coefficients of an equalizer; a parameter of the jones matrix is obtained; a coefficient of an X axis polarization state or a Y axis polarization state in the coefficients is adjusted using the parameter of the jones matrix when the coefficients have singularity characteristics, or energy corresponding to each coefficient of X or Y axis polarization state under each order of a filter in the equalizer is determined using two coefficients of an X or Y axis polarization state in the equalizer coefficients; and a central position of a coefficient tap of X or Y axis polarization state of the equalizer is adjusted using the energy corresponding to each coefficient of X or Y axis polarization state under each order of the filter when the coefficient tap of the X axis or Y axis polarization state of the equalizer deviates from the central position.