Airborne Radar Clutter Suppression Method Based On Historical Data

The problem of airborne radar clutter suppression has been the focus of scholars around the world.The clutter suppression capability of a radar system is a major factor in measuring the performance of radar moving target detection.In the complex battlefield environment,the airborne phased-array radar is influenced by both the passive interference and the active interference.In such circumstances,the weak target signal is buried with the high power interference that enters from the sidelobe,which degrades the performance of the moving target detection.In the sequel,it is very practical to study the problem of airborne phased-array radar clutter suppression.In this thesis,the clutter suppression is carried out from two aspects.Firstly,a beampattern synthesis with low sidelobe level is proposed to suppress the clutter.Moreover,the clutter covariance matrix is estimated by the historical data and the space-time adaptive processing algorithm is utilized to suppress the clutter.The main contributions are summarized as follows:1.To solve the problem of the effect of the antenna amplitude phase error on the sidelobe level of the beampattern,a beampattern synthesis with low sidelobe level is proposed using the differential evolution algorithm.An antenna model with array error is established.Specifically,a differential evolution algorithm is used to optimize the cost function of beampattern of the target which is calculated by minimizing the mainlobe width and the maximum sidelobe level.Thus,the amplitude and phase errors in the antenna array are compensated with the optimal weights obtained by differential evolution algorithm,leading to the sidelobe level and the mainlobe width without the amplitude and phase error.With the proposed method,the sensitivity to the amplitude and phase errors in traditional algorithms is reduced.2.To solve the problem of strong ground clutter in airborne radar moving target indication,the model of the airborne phased-array radar is studied.Firstly,the different distribution of the clutter power spectrum varying with different yaw angles is analyzed theoretically.Focusing the problem of large computation complexity and lack of sufficient independent and identically distributed samples,the concept of dimension-reduced STAP algorithms and related classical algorithms are carried out.By changing the parameters of the yaw angle and the speed of the carrier and comparing different distribution tracks of the clutter power spectrum,the effectiveness of the proposed clutter model is verified.Furthermore,the improvement factor curves of several algorithms are compared to prove the performance of the reduced dimension STAP algorithm.3.To solve the problem of the lack of sufficient independent and identically distributed samples,a method to suppress the clutter is proposed with historical data.Firstly,a cognitive based two dimension two pulse clutter cancellation(TDPC)is designed and used as a pre-filter cascading reduction algorithm to perform clutter adaptive processing.The TDPC prefilter can filter a large number of clutter along the clutter trajectory,and reduce the clutter freedom,and improve the performance of the reduced dimension STAP algorithm.The number of samples to be estimated in the current clutter covariance matrix is compensated by historical data,and a model of the clutter based on historical data is established.Finally,the TDPC pre-filter cascading the reduced dimension STAP algorithm is applied to process clutter.Because the actual number of homogeneous samples is not enough,the historical data of the radar can increase the number of samples needed to estimate the covariance matrix of the clutter,and improve the performance of the original algorithm.Eventually,the effectiveness of clutter suppression based on historical data is verified by comparing the improvement factor curves.