Research On Radar Signal Cooperative Distributed Reconnaissance Theory

Radar reconnaissance is the basis and premise of radar countermeasures.The amount and reliability of the information it obtains directly affects the formulation of operational decisions and the effectiveness of operations.With the development of military technology,radar countermeasures tend to systemize,cooperative reconnaissance has gradually become the focus of attention in the military field at home and abroad.This thesis focuses on the signal detection and time-frequency parameter estimation,space parameter estimation,distributed data fusion algorithms,and multiple reconnaissance station collaborative location and tracking technologies in the field of cooperative reconnaissance.The specific contents are as follows:1.The methods of radar signal detection and time-frequency parameter estimation are studied.Analysis of conventional detection methods,detection methods based on FrFT,fractional autocorrelation methods.Aiming at the limitation of FrFT and fractional autocorrelation algorithms due to the "fence effect" and search step size,which leads to insufficient accuracy when estimating signal parameters,a signal parameter estimation algorithm based on FrFT and fractional autocorrelation interpolation is proposed.The "fence effect" brought by the search interval was improved,and the accuracy of parameter estimation was significantly improved.2.The space parameter estimation algorithm of radar signal is studied.Starting from the basic theory of compressive sensing,the relationship between compressive sensing theory and DOA and Doppler frequency estimation is explained.Two important sparse reconstruction methods are analyzed.Finally,a new 2D-DOA and Doppler frequency joint estimation method is proposed.This method uses the L-shaped array and segmentation observation model to construct a new signal receiving model.To obtain the joint estimation of DOA and Doppler frequencies,the sparse signal is reconstructed by two steps.At the same time,it solves the 2D-DOA pairing problem in the L-shaped array.This method does not require a large sampling data,and the amount of calculation is small.3.The distributed reconnaissance data fusion algorithm is studied,which are weighted fusion algorithm based on signal-to-noise ratio,weighted fusion algorithm based on covariance,and weighted fusion algorithm based on support matrix.Combined with the parameter estimation algorithm proposed in the previous article,the data fusion is performed through simulation experiments.The results verify that the three fusion algorithms can significantly improve the parameter estimation accuracy,and improve the accuracy and reliability of the system.4.The cooperative location and tracking algorithms of multiple reconnaissance stations were studied.First,the basic principles of passive time difference location and passive direction location are described.Then Two classic TDOA location algorithms are introduced.And the influence of various factors on the positioning performance is analyzed.Then introduced the non-linear filtering algorithm—extended Kalman filter and unscented Kalman filter.Combined with the angle information and time difference information respectively to track the target.Finally,the distributed data fusion algorithm is researched,and the local filtering results are used for data fusion to obtain better tracking results.