84 results about "Discrete-time Fourier transform" patented technology

The taps of an equalizer are used as inputs to a discrete Fourier transform (DFT). Certain spectral components are extracted from the DFT and used to estimate the frequency difference. The frequency difference estimate is filtered using a phase locked loop and used to adjust one clock to synchronize the transmit and receive clocks.

The invention provides a phase difference measuring method based on improved windowing discrete Fourier transform. The phase difference measuring method includes the first step of collecting two periodic signals to be measured, the second step of analyzing the collected periodic signals to digital signals, the third step of carrying out windowing processing on the digital signals by constructing a 4-step Blackman-Harris window, and carrying out FFT spectral analysis on the signals subjected to windowing processing to obtain signal frequency spectrums, extracting fundamental wave parameters and respectively calculating initial phase angles of the periodic signals to be measured, and the fourth step of correcting phase positions of useful frequency spectrums according to a discrete spectrum correction method so as to calculate the phase differences of the periodic signals to be measured. The phase difference measuring method can effectively solve the problems that a time domain of a DFT algorithm is cut off, the introduced spectrums are revealed, and spectral analysis has bigger errors caused by the picket fence effect, overcomes the defects of spectrum leakage and lower frequency resolution in an interpolation method, improves the frequency resolution, and finally achieves the high-precision measurement of the phase differences.

The invention provides an adaptive modulation-demodulation method base on fractional order Fourier transform, aiming at reducing influence caused by feedback time delay specific to an MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) adaptive modulation system which has feedback time delay, and enabling the system to have better performance in a fast time-varying channel. The method has the technical scheme that inverse discrete fractional order Fourier transform is used to replace inverse discrete Fourier transform for carrying out subcarrier modulation at a transmitting terminal, discrete fractional order Fourier transform is used to replace discrete Fourier transform for carrying out subcarrier demodulation at a receiving terminal, the optimal fractional order Fourier transform order selection is carried out by channel estimation information fed back to the transmitting terminal by the receiving terminal, the deviation between the current channel state information and the feedback channel state information is reduced and the performance of the system having time delay is enhanced.

The invention discloses a fast calculation method of discrete Fourier transformation (DFT)/inverse discrete Fourier transform (IDFT). The rapid calculation method comprises the following steps of: carrying out zero fill on a bit sequence to be transformed to enable the sequence length after the zero fill to accord with the processing range of a fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT) processor; carrying out FFT/IFFT calculation on the sequence after zero fill by utilizing the FFT/IFFT processor, and resampling time domain/frequency domain of an FFT/IFFT calculation result sequence according to a sampling position determined by the sequence lengths before and after the zero fill; and outputting the resampling result as a DFT/ IDFT result sequence. The invention also discloses a fast calculation device of DFT/IDFT. By adopting the invention, the calculation complexity of DFT/IDFT can be reduced, and the calculation speed can be improved.

The invention discloses a realization method for fast computation of discrete Fourier transform with non-second power points. The realization method is an improvement obtained by comprehensively using a prime factor decomposition algorithm, a mixed-radix FFT (fast Fourier transform) algorithm and a WFTA (Winograd Fourier transform algorithm) decomposition method, and the sequence of non-second power points is decomposed layer by layer through common factor decomposition and prime factor decomposition. The realization method for fast computation of discrete Fourier transform with non-second power points has the characteristics of less computation, high computation efficiency and small overhead for realization.

The invention discloses a multifrequency interpolation iteration frequency estimation method based on all phase spectrum analysis, and an estimator. The method comprises the following steps that: 1) carrying out all phase FFT (Fast Fourier Transform) spectrum analysis processing on an input signal, and searching a peak value spectrum position; 2) calculating the all phase DTFT (Discrete Time Fourier Transform) amplitudes of two-side frequency points of the peak value spectrums, carrying out interpolation iteration, and obtaining a frequency estimation result; 3) judging whether the relative difference of two all phase DTFT spectrum values meets an iteration termination condition or not; and 4) if the relative difference of two all phase DTFT spectrum values does not meet the iteration termination condition, calculating frequency offset, regulating the positions of the peak value spectrums, and repeating the steps 2)-3); and if the relative difference of two all phase DTFT spectrum values meets the iteration termination condition, finishing iteration, and outputting a frequency estimation result. The estimator comprises that a simulated input signal is sampled, a sampled digital signal is segmented, each section of data and filter coefficients are stored into an external RAM (Random Access Memory), a DSP (Digital Signal Processor) carries out all phase FFT, all phase DTFT and interpolation iteration processing on the above input data, frequency is estimated, and finally, a frequency value is displayed in virtue of an output driver and a display module thereof.

The invention discloses a blind spectrum sensing method based on low-rank sparse matrix decomposition. The method includes: receiving wireless signals of a to-be-sensed frequency band, and sampling to obtain observation signals; performing discrete time Fourier transformation on the observation signals to obtain transformed observation signals; performing low-rank sparse decomposition on the transformed observation signals according to the sparse factors of set low-rank factors to obtain low-rank elements and sparse elements; performing reverse discrete time Fourier transformation on the low-rank elements and the sparse elements to obtain transformed low-rank elements and transformed sparse elements, and acquiring final observation signals according to the transformed low-rank elements and the transformed sparse elements; calculating the average energy value of the transformed sparse elements and the final observation signals in a time domain to obtain a sparse energy value and a final observation signal energy value, and judging whether the to-be-sensed frequency band is idle or not according the relation of the sparse energy value and the final observation signal energy value with a preset judging threshold. The method is applicable to spectrum sensing with uncertain noise and high in sensing precision.

The spacialú¡temporal joint detection device and method based on discrete Fourier transform in wireless transmission concerns a equilibrious method and device based on wireless mobile communication field. The detective method of the device is as follows: a. making the spacialú¡tempora incorporation for estimated channel impulse response sequence to get new data sequence h(n); b. making the estimated channel impulse response sequence and received data sequence to carry out spacialú¡temporal incorporation to get new data sequence r^ (n); c: taking the non-normalization discrete Fourier transform for r ^(n), and getting transform coefficient R^(k); d. taking discrete Fourier transform for h (n), and taking the coefficient real part H (k) after transforming; e. handling H (k),R^(k) and estimating channel noise variance by equalizer to get new data sequence S^(k); f. taking non-normalization inverse discrete Fourier transform for S^(k) sequence, getting reconstructive sending data sequence S^ (n).

Systems and methods for digital signal processing using a sliding windowed infinite Fourier transform (“SWIFT”) algorithm are described. A discrete-time Fourier transform (“DTFT”) of an input signal is computed over an infinite-length temporal window that is slid from one sample in the input signal to the next. The DTFT with the temporal window at a given sample point is effectively calculated by phase shifting and decaying the DTFT calculated when the temporal window was positioned at the previous sample point and adding the current sample to the result. The SWIFT algorithms are stable and allow for improved computational efficiency, improved frequency resolution, improved sampling, reduced memory requirements, and reduced spectral leakage.

A spectrometric system has a primary channel with a signal waveform and a reference channel with a signal waveform. A digital representation of the primary signal waveform and a digital representation of the reference signal waveform to provide a digital output representing the primary signal at datum points synchronized with the reference signal are processed by computing the Fourier transform of the primary signal waveform and using a fast reverse non-uniform discrete Fourier Transform technique to compute the reverse non-uniform discrete Fourier transform of the Fourier transform of the primary signal waveform to provide the digital output representing the primary signal at datum points synchronized with the reference signal.

The invention discloses an undersampled signal arrival time difference measuring method. The method includes adopting at least 4 radio receivers to receive signals emitted by one target, and determining the time domain sampling sequence of each radio receiver for signal receive by undersampling; performing discrete time Fourier conversion on the time domain sampling sequence of each radio receiver for signal receive to determine the frequency domain sampling sequence of each radio receiver for signal receive; establishing signal arrival time difference constraints of spectrum aliasing among the frequency domain sampling sequences of all the radio receivers for signal receive; performing alternate two-step iterative data processing through the signal arrival time difference constraints of the spectrum aliasing, determining the minimum fitting error corresponding to a time difference of searching, and measuring the time difference of searching corresponding to the minimum value of the minimum fitting error of the signal arrival time difference. The method has the advantages that the error of signal arrival time difference measuring is small, and the quantity of signal data to be transmitted and processed is small.

The invention relates to a spectrum sensing data processing method based on discrete Fourier transformation. The spectrum sensing data processing method based on the discrete Fourier transformation specifically comprises the steps that band-pass filtering is carried out on received signals; down-conversion is carried out on band-pass signals; low-pass filtering is carried out on demodulated signals; A/D conversion is carried out on the signals; minimum sampling frequency is given according to frequency-domain characteristics of the signals, and fast Fourier transformation is carried out on the signals; lastly, obtained discrete data are screened, and only discrete Fourier transformation coefficients capable of reflecting the real conditions of all information channels are reserved. According to the spectrum sensing data processing method based on the discrete Fourier transformation, the sampling process of the fast Fourier transformation and a method for extracting wireless spectrum characteristics are provided. In addition, the spectrum sensing data processing method based on the discrete Fourier transformation further improves information processing efficiency, and under the premise that the expression of the frequency-domain characteristics of the signals is not influenced, the total number of discrete points is 25% smaller than the total number of original discrete value through the reasonable screening of the discrete data.

The invention proposes a sinusoidal signal frequency estimation method based on DFT (Discrete Fourier Transform) and dichotomy. According to the method, a quantization frequency index corresponding tothe maximum value of an N-point DFT spectral line is used as the estimated value of an integer part; a dichotomy method is adopted to perform iterative search so as to determine the estimated value of a fractional part; and the estimated value of a residual is determined by solving an equation, therefore, the accurate estimated value of a frequency is obtained. With the method adopted, the accurate estimation of the frequency of a single sinusoidal signal can be obtained. The performance of the method is better than that of an existing method. A mean square error estimated by the method is closer to the Cramer-Rao Lower Bound.

The invention provides a multipath delay synchronous decision demodulation method based on short-time discrete Fourier transform. The method comprises the following steps: firstly receiving a carrier signal on a power line by a receiving end, and then filtering out of band noise through a band-pass filter; then performing sampling and quantization on the carrier signal after the band-pass filtering, and performing discretization on the signal; performing short-time discrete Fourier transform on a discrete sequence obtained by the discretization; performing approximating to obtain amplitudes at two carrier frequency; selecting the optimal branch; and performing demodulation decision by using the optimal branch information to accomplish the signal demodulation. The multipath delay synchronous decision demodulation method has the technical effects that the symbol synchronization can be quickly achieved, accurate signal demodulation is also achieved, the system is not affected by the initial phase of the carrier or the carrier frequency estimation error, robustness is high, and the implementation is easy. A simulation result shows that the performance of the method is close to the performance of coherent demodulation. A field operation result shows that the method has a high rate of communication results and is of a practical application value.

A method for measuring wind velocity including using a Fourier Transform (FT) and a correlation between a wave vector number and temporal frequency of a wind to calculate wind velocity. Contour line deviations in a series of images of a far object are evaluated by performing a spatial discrete fourier transform (DFT) on deviations within each image, and subsequently a time discrete fourier transform (DFT) on the Fourier coefficients obtained by the spatial DFT, to get the frequency dependence of each Fourier coefficient.

The invention discloses a design of an implementation device of a signal processing technology. The working process of the implementation device comprises the following steps: firstly, multiplying a signal X and a three-weighted fractional Fourier transform coefficient B0 (alpha) to obtain Z0; performing discrete Fourier transform on the signal X to obtain a signal Z1; performing 1/3-order 4-WFRFT on the signal Z1 after the discrete Fourier transform to obtain a signal Z2, and multiplying the Z2 with a three-weighted fractional Fourier transform coefficient B1 (alpha) to obtain a signal Z3; processing the X by a symbol reversing module P to obtain a signal Z4, performing 2/3-order 4-WFRFT on the Z4 to obtain a signal Z5, and multiplying the Z5 with a three-weighted fractional Fourier transform coefficient B2 (alpha) to obtain a signal Z6; summing the Z0, the Z3 and the Z6 to obtain a signal Y obtained after alpha-order three-weighted fractional Fourier transform of the signal X; completing the whole process of the alpha-order three-weighted fractional Fourier transform of the signal X. The alpha in the process is changed into -alpha, so that a three-weighted fractional inverse Fourier transform process of the signal is carried out.