Loran-C passive radar TOA estimating method based on total variation and compressed sensing

Abstract

The invention belongs to the technical field of radar target passive positioning and particularly relates to a Loran-C passive radar TOA estimating method based on a total variation and compressed sensing. The Loran-C passive radar TOA estimating method based on the total variation and compressed sensing comprises the following steps that time domain self-adaptive filtering is carried out on Loran-C direct wave signals, Loran-C sky wave signals and Loran-C target echo signals to obtain signals after time domain self-adaptive filtering is carried out, wherein the Loran-C direct wave signals, the Loran-C sky wave signals and the Loran-C target echo signals are received by a passive radar; total variation filtering is carried out on the signals obtained after time domain self-adaptive filtering, and signals after total variation filtering is carried out are obtained; discrete Fourier transformation is carried out on the signals obtained after total variation filtering, and frequency domain signals after total variation filtering is carried out are obtained; according to the frequency domain signals obtained after total variation filtering, a compressed sensing method is adopted for reconstructing Loran-C target echo time domain sparse signals; according to the Loran-C target echo time domain sparse signals, the TOA of Loran-C target echoes is obtained.

Description

technical field [0001] The invention belongs to the technical field of radar target passive positioning, in particular to a Loran C passive radar TOA estimation method based on total variation and compressed sensing. Background technique [0002] Passive radar locates and tracks the target because it does not emit electromagnetic waves. It is undetectable and has stronger concealment and survivability than conventional radar. It has always attracted attention. At present, passive radar uses non-cooperative radiation sources such as radio stations or TV stations, mobile phone communication base stations, and wireless network signals as the irradiation source for target detection, and extracts the target echo by receiving the direct wave from the radiation source and the echo scattered by the target. Direction of Arrival (DOA, Direction of Arrival), Time of Arrival (TOA, Time of Arrival), Doppler frequency shift and other information to achieve positioning and tracking of targ...

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Problems solved by technology

The matched filtering method is widely used in radar systems, but this method only has a good estimation effect on the arrival time of large time-width-bandwidth product signals, such as linear frequency modulation signals, step frequency signals and phase encoding signals, while Roland C The time-width-bandwidth product of the signal is small, so the matched filter method cannot obtain better arrival time estimation effect

The generalized cross-correlation method is an improvement to the matched filter method. This method reduces the sidelobe of the matched-filtered waveform by means of spectral windowing. However, the main lobe of the Roland C signal is very wide after pulse compression, so the time-of-arrival is estimated by the generalized cross-correlation method. is still poor

The MUSIC super-resolution time delay estimation method is to use the frequency domain samples to estimate the time of arrival by referring to the direction of arrival estimation in the airspace. Since the MUSIC algorithm is a subspace algorithm, the number of information sources must be estimated in advance to complete the signal subspace and noise. The correct division of the subspace, but practice shows that the number of Roland C sources estimated by the MDL (mi minimum description length) or BIC (Bayesian information criterion) criterion is not accurate, and the use of the MUSIC algorithm will cause a large number of false target arrivals time

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