Frequency shift and DFT-based real sinusoidal signal phase difference estimation method

Abstract

The invention provides a frequency shift and DFT-based real sinusoidal signal phase difference estimation method, and belongs to the technical field of signal processing. The method uses known signalfrequency estimation information to perform frequency shift on an original signal; when the frequency shift component meets certain conditions, an original non-coherent sampling signal is close to coherent sampling after frequency shift, and most of the components of the positive frequency or the negative frequency part of the frequency shift signal approach to be zero, and then a two-point DFT value of the frequency-shifting signal is used for coupling and solving to achieve inter-spectrum interference elimination; and finally, the phase difference of two paths of signals is obtained throughfrequency shift signal positive frequency or negative frequency spectrum peak phase subtraction. By adoption of the method, the influence of non-coherent sampling on phase difference estimation precision can be effectively inhibited; compared with other existing methods, the method can effectively resist the influence of signal frequency estimation errors, and the dependence of an existing methodon signal frequency estimation precision is reduced, so that relatively high estimation performance can be kept under the condition that certain frequency estimation errors exist.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to the parameter estimation technology of noisy real sinusoidal signals. Background technique [0002] Parameter estimation of noisy real sinusoidal signals is a basic problem in signal processing, and it is widely used in many fields such as radar communication, instrumentation, biomedicine, power system, vibration testing, and smart wearable devices. With the extensive and in-depth application of signal processing technology, the accuracy, real-time performance, and robustness of parameter estimation are constantly facing new challenges, and higher requirements are put forward for various parameter estimation methods. Phase difference is an important parameter to be estimated in application scenarios such as ranging, positioning, tracking, and direction finding. Generally speaking, phase difference estimation methods can be simply divided into two categories: time domai...

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Problems solved by technology

Literature (Y.Tu and H.Zhang, "Method for CMF signal processing based on the recursiveDTFT algorithm with negative frequency contribution", IEEETrans.Instrum.Meas.57, 2647-2654(2008).) proposed a method that takes negative frequency DTFT algorithm, which effectively improves the accuracy of phase difference estimation under incoherent sampling conditions by considering the influence of negative frequency spectrum on phase difference estimation, but this method also needs to obtain the frequency information of the signal before phase difference estimation. When the frequency estimation error is large, the phase difference estimation accuracy drops significantly

[0005] To sum up, whether it is a time-domain method or a frequency-domain method, the phase difference estimation accuracy is largely affected by the incoherent sampling of the signal, and incoherent sampling is ubiquitous and unavoidable in practical applications.

Among the existing estimation methods that can overcome incoherent sampling, data continuation cross-correlation and data continuation Hilbert transform can reduce the estimation error, but even if the signal frequency is known, data continuation cannot fully achieve coherent sampling

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