In practical applications, multiple types of array errors exist simultaneously. In view of the orientation dependence of array errors in this case, an improved MUSIC (multiple signal classification) algorithm based on MST (manifold separation technique) was proposed, which can effectively solve DOA (direction of arrival) estimation problem under the influence of multiple array errors. The sampling matrix, which contains all the non-ideal characteristics of the array was obtained by using MST, so as to achieve accurate direction finding. By using two-dimensional Fourier transform, the 2D spatial spectrum was solved. Compared with existing MUSIC algorithm, the computation amount of spectrum peak searching was reduced. Theoretical analysis and simulation verify the effectiveness of the algorithm, which can provide reference for the solution of practical problems.

Aiming at the situation that the existing sorting algorithms were more or less dependent on prior information or difficult to adapt to multi-function radar, a multi-station fusion sorting algorithm based on set pair potential of partial coefficients was presented. The partial connection number parameter was introduced from the set pair analysis in the mathematical field to establish a clustering model. On this basis, the decision level fusion of clustering results was carried out by using the arrival time difference parameter in the multi-station cooperation mode. The actual measurement data and simulation results show that the algorithm can adapt to multi-functional radar systems such as search, acquisition and tracking, and realize the accurate clustering and fusion of radar pulse signals without any prior information. The sorting success rate is more than 97% in case of the jamming pulse ratio is lower than 60%.

The proliferation of “low, slow and small” UAVs (unmanned aerial vehicles) poses a serious threat to flight safety in airspace. Accurate analysis of the characteristics of UAV echo signals is of great significance for the detection of non-cooperative UAVs. Based on the time-domain integral echo model of rotor UAV target and the principle of cepstrum algorithm, the frequency-domain expression and cepstrum expression of echo signal were derived, the corresponding relationship between echo signal parameters and frequency-domain and cepstrum characteristics was analyzed, and a parameter estimation method for UAV echo signal was proposed, and the effectiveness of this method was verified by simulation and measured data. The results show that, it can estimate the bandwidth and rotation frequency of UAV echo signal more accurately and provide an important reference for target detection and recognition of UAV.