6results about How to "Achieving Interference Suppression" patented technology

The invention provides a broadband navigation interference suppression method for an unmanned aerial vehicle cluster equivalent array, and the method comprises the steps: receiving an initial navigation signal of each second unmanned aerial vehicle through a first unmanned aerial vehicle, and an initial navigation signal of the first unmanned aerial vehicle, so as to form an initial navigation signal data set, the influence of array element spacing, unmanned aerial vehicle clock error and crystal oscillator offset on array signal processing is analyzed, and a time domain filter is introduced on the basis of spatial domain processing for processing. The method has the beneficial effects that the influence of array element spacing, unmanned aerial vehicle clock error and crystal oscillator offset on array signal processing is analyzed emphatically, the three factors are modeled into a fixed and linearly increasing time delay deviation item, a time domain filter is introduced on the basis of space domain processing, time delay is compensated through space-time adaptive processing, and the accuracy of array signal processing is improved. Therefore, interference suppression of the unmanned aerial vehicle cluster on the navigation signal is realized, and the accuracy of unmanned aerial vehicle cluster positioning is improved.

The application discloses a beamforming method of a holographic MIMO antenna array based on a steering vector, and under the condition that a user end position is unknown, multiple code words are constructed based on the steering vector through the combination of the pitch angle and the azimuth angle of the user end relative to the antenna array, a code word with the maximum gain of a receiving end is selected from the code words according to the feedback of the user end, and the code word is used as the optimal transmitting or receiving coefficient of the antenna array. The user end only needs to feed back the corresponding information in sequence, the requirement for the feedback time delay is slightly low, the error code of the feedback information has a certain fault tolerance, the time complexity is relatively low compared with the exhaustive search method, the performance is good, the outdoor channel changing rapidly can be well responded, the robustness to the channel fluctuation is higher, and the overall power gain is improved. Moreover, under the condition that the user end position is known, the transmitting or receiving coefficient of the antenna array can be directly determined.

The existing time-frequency domain false target jamming suppression method generally requires high jamming parameter estimation accuracy, and the application scene is relatively limited. Therefore, the present application provides a DRFM jamming suppression method based on the spike distribution characteristics after pulse compression. The algorithm does not require prior information of jamming parameters, analyzes and summarizes the spike distribution characteristics after DRFM jamming pulse compression, counts the number of spike intervals according to the spike distribution characteristics, designs a corresponding decision threshold, thereby distinguishes the target and the jamming, and realizes the jamming suppression on this basis. The present application can suppress the false target jamming in a complex electromagnetic environment, and has a low calculation degree, and can improve the anti-jamming capability of the radar.

A scene-prior-guided pig target detection method includes input image and feature extraction, coordinate encoding generation, and explicit foreground prior prediction. The method inputs features fused with spatial priors into a foreground prior prediction head. Foreground prior information is generated through convolutional mapping, normalization, and nonlinear activation. A foreground probability map is generated by activating the foreground probability map using the sigmoid function. The foreground probability map is then used to perform residual foreground modulation on the original detection features to enhance the foreground region. A weakly supervised foreground mask is automatically generated based on the target detection bounding boxes, and an auxiliary prior loss is constructed to supervise and constrain the foreground probability map. Finally, the enhanced detection features are input into the detection head to complete pig target classification and bounding box regression, outputting the final detection result. This invention solves the problems of existing pig target detection methods, such as lack of fixed pigsty scene spatial stability modeling, severe background interference, and insufficient detection stability under dense occlusion conditions.

The application discloses a cognitive FDA-MIMO radar deception jamming suppression target detection method, comprising the following steps: acquiring a nonlinear motion target signal received on a range gate; using a nonlinear UKF filter to predict the state and weight of a sampling point set, and estimating the state of the nonlinear motion target; performing distance gate energy solving according to the nonlinear motion target signal received on the range gate, acquiring an energy gradient sequence, extracting a range gate serial number set, and respectively constructing a nonlinear motion target covariance matrix and an interference covariance matrix; suppressing the interference noise covariance matrix, and acquiring a nonlinear motion target signal power vector on all range gates; detecting the nonlinear motion target signal power vector on all range gates, and acquiring an updated nonlinear motion target signal; and tracking the updated nonlinear motion target signal. The application can improve the detection performance.

The present invention discloses a spatial domain anti-interference method, which relates to the field of signal processing. The method comprises: based on the power inversion anti-interference method, a weighted processing is performed on the first received signal received by the first antenna module and the second received signal received by the second antenna module to obtain a first composite signal; based on the reference sequence correlation peak search, the first correlation peak is determined according to the first composite signal, and the second correlation peak and the third correlation peak are determined according to the first received signal and the second received signal; the reference signal index position is determined according to the first correlation peak, the second correlation peak and the third correlation peak; based on the MMSE method, the second composite signal after interference suppression is determined according to the reference signal index position, the first received signal and the second received signal. Compared with the prior art, the present invention can meet the interference suppression of a large dynamic range interference-to-signal ratio, and can effectively suppress interference signals in both high interference-to-signal ratio and low interference-to-signal ratio conditions.