Study On Robust Methods For Clutter Suppression And Parameter Estimation

As a military sensor, airborne early warning radar plays an important role on the modern battlefield. Airborne radar usually looks down to detect low-altitude targets, thus ground clutter will be received. Since ground clutter returns can appear quite strong and are widely spread in Doppler and angle, moving targets may be obscured by clutter, which results in a degradation in slow target detection performance. Clutter suppression techniques for airborne radar include ultralow sidelobe antenna, displaced phased center antenna and space time adaptive processing(STAP). STAP is a two-dimensional adaptive filtering technique, which takes advantage of the spatial and temporal degrees of freedom provided by the elements of the array and the coherent pulses. STAP can effectively suppress clutter and improve the target detection performance.The basic theory of STAP is relatively complete. However, in practical engineering applications, STAP is still faced with many problems. STAP for low sample support applications, STAP in non-homogeneous environments, robust STAP, knowledge-aided STAP and STAP in complex electromagnetic scenarios are hot and urgent problems in the field of STAP research. This paper is focused on studying the above five aspects, and the main contents are organized as follows.In chapter 2, the diagonal loading level estimation problem is investigated. Diagonal loading method can be exploited to improve the performance of STAP in the face of limited training data. However, the diagonal loading level may be not easily determined in reality. To solve this problem, an adaptive parameter estimation method based on the received radar data is proposed. We firstly transform the diagonal loading problem into the Tikhonov regularization problem. Then, generalized cross validation(GCV) is introduced to construct the optimization problem. Finally, secant method is utilized to solve the optimization problem and calculate the loading parameter. The performance of the method is demonstrated using simulated data. The results show that the method can effectively estimate the loading parameter and makes diagonal loading method more practical in the realistic environments.In chapter 3, outlier detection problem is studied. For airborne ground moving target indication radar system, the density of targets in the main beam is high. The presence of target-like signals corrupts the covariance estimation severely, resulting in performance degradation of the conventional non-homogeneity detector. To address the problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed. The deleterious effect of outliers on the covariance calculation is eliminated by an adaptively reweighted scheme to the training data set. Simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the robustness of the traditional adaptive power residue detector.In chapter4, array amplitude and phase error estimation problem is studied. In airborne radar system, the parameter estimation and geo-location accuracy of moving targets will be affected by array error. Here, two array error estimation methods based on subspace orthogonality and Frobenious norm fitting are proposed to deal with this problem. The subspace orthogonality method uses the orthogonality between complement subspace of clutterr and a left singular vector corresponding to the maximal singular value to estimate the array error; while Frobenious norm fitting method estimate the array error by fitting the reconstructed data to the received data. Numerical simulation results validate that, compared to the existing methods, the proposed two methods can achieve fine parameter estimation accuracy and provide robustness, when the number of snapshots and pulses is limited or the clutter-to-noise power ratio(CNR) is low.In chapter 5, velocity and crab angle estimation problem is researched. The parameters of platform velocity and crab angle are essential for the knowledge-aided space time adaptive processing technique. However, in some situation, they are either inaccurate or deficient. Here, a method based on curve fitting is proposed to deal with this problem. Firstly, the subaperture smoothing capon technique is used to estimate the power spectrum density of the received data. Then, the power peaks due to clutter scatterers is extracted by the threshold detection approach. Finally the two-dimensional frequency of peaks and the known geometry parameter are substituted into the least trimmed squares estimator. Performance analysis using the simulated and measured data shows that the proposed method increases accuracy and robustness of the traditional curve fitting method.In chapter 6, coherent repeater jammers(CRJ) counter problem is investigated. CRJ may induce many false targets in the radar receiver, resulting in degradation of the true target detection performance. To deal with this problem, an adaptive transmit technique for anti-CRJ is proposed. Firstly a burst of high repetition frequency pulse is transmitted to detect and estimate the parameter of CRJ. Then, based on the estimated parameter, the array transmit pattern is optimized to place nulls in the direction of jammer, therefore the probability of interception radar transmitted signal can be reduced. Simulation results show that this method can effectively detect and estimate the parameter of CRJ. Besides, this method ameliorates the processing burden in the receiver compared to other signal processing methods...