Study On DOA Estimation Method With High-precision And Fast Feature Based On SMV Model Via Tail Optimization

Electronic reconnaissance is an important branch of electronic warfare.Direction of Arrival(DOA)estimation is one of the main contents of electronic reconnaissance.The electronic reconnaissance system based on sparse array has the potential of high-precision direction finding,which can reduce the system complexity and avoid the wide-band spatial grating lobe problem caused by uniform array.Due to the low signal-to-noise ratio of electronic reconnaissance array when receiving remote target signals,the estimation accuracy of traditional electronic reconnaissance interferometer algorithm is reduced or even completely invalid.The radar signal received by electronic reconnaissance is usually narrow pulse,and the number of sampling points is small after digital sampling,which is called short snapshot.The electronic reconnaissance system of vehicle or shipborne platform is prone to multipath phenomenon when the received signal frequency band is low due to the low installation height and the influence of platform scattering,which is called coherent source.The traditional decoherence processing algorithm will pay the cost of spatial resolution and accuracy caused by aperture loss.In summary,how to achieve multi-source DOA estimation accurately and quickly at the same time under the condition of low signal-to-noise ratio,short snapshot and coherent source is a challenge for broadband electronic reconnaissance system.Sparse recovery DOA estimation is one of the effective ways to cope with the above challenges.Therefore,the high-precision and fast DOA estimation algorithm based on sparse array using tail optimization is studied.1.The receiving model of array narrowband signal and the principle of traditional subspace DOA direction finding algorithm are summarized.The shortcomings of traditional DOA direction finding algorithm are pointed out through simulation.Combining single measurement vector(SMV)with sparse recovery DOA estimation,the traditional sparse recovery DOA estimation based on l1 norm optimization(l1-CVX)is proposed.Based on the rank 1 model vector obtained by a small number of snapshots through array element denoising as a single measurement vector,the observation quality of short snapshots under low SNR can be improved.Using the spatial distribution of multi-sources usually has spatial sparse characteristics,without loss of spatial aperture and no matter whether there is a coherent source,DOA estimation of multi-sources can be achieved with high probability through sparse recovery.2.Since the l1-CVX objective function is to minimize the global 1-norm of the sparse recovery vector x,the lower the probability of the l1-CVX constrained optimization problem(essentially an underdetermined problem)to obtain the exact sparsest solution by sparse recovery is when the arrival direction of the two sources in the main lobe(not less general)is closer.This paper uses only the l1 norm of the x tail(i.e.the small set of x)as the objective function,and combines the SMV observation model with the tail optimization to improve the spatial resolution of sparse recovery.The higher the accuracy requirement of DOA estimation,the stronger the correlation between the columns of the observation matrix A,and the more difficult it is to meet the Restricted Isometry Property(RIP),that is,the lower the probability of obtaining the unique sparse solution vector by sparse recovery.In order to take into account the accuracy of DOA estimation and sparse recovery solution,a tail optimization DOA estimation algorithm based on alternating grid offset(abbreviated as l1-Tail + AGO-CVX)is proposed by alternately reducing the offset through grids.3.Since the source code of CVX(Convex)toolkit is not open,and various constraint optimization problems are considered,we propose a fast DOA estimation based on Hadamard product for tail optimization(abbreviated as l1-Tail-Hadamard)to improve the real-time performance of tail optimization.The Hadamard product is used to replace the CVX toolkit to optimize the constraint of x tail.Through the Hadamard parameterization product,the time complexity of the algorithm is reduced,and the code publicity engineering is easier to realize.Secondly,combined with the basic idea of alternating grid offset,the Hadamard + AGO tail optimization DOA fast estimation algorithm(abbreviated as l1-tail +AGO-Hadamard)is proposed.Compared with the existing algorithms,the proposed algorithm based on Hadamard product for DOA estimation runs significantly shorter,which lays a good foundation for engineering implementation.4.Since the source code of CVX(Convex)toolkit is not open,it is difficult to optimize different constraint problems for constraint optimization.Therefore,we need to explore other ways to improve the real-time performance of tail optimization.We propose a tailoptimized fast DOA estimation based on Hadamard product(abbreviated as l1-TailHadamard).The Hadamard product is used to replace the CVX toolkit to optimize the constraint of x tail.Through the Hadamard parameterization product,the time complexity of the algorithm is reduced,and the code publicity engineering is easier to realize.Secondly,combined with the basic idea of alternating grid offset,the Hadamard + AGO tail optimized fast DOA estimation algorithm(abbreviated as l1-Tail + AGO-Hadamard)is proposed.Then,the effectiveness and performance simulation of the algorithm are carried out.Compared with the existing sparse recovery DOA estimation algorithm,the proposed Hadamard product based tail optimized fast DOA estimation algorithm runs much shorter than the existing algorithm,which lays a good algorithm foundation for realization.5.Design and experimental verification of tail-optimized fast DOA estimation scheme.Firstly,the development environment,module function and implementation scheme of sparse electronic reconnaissance array direction finding system are summarized.Secondly,the direction finding block diagram and module flow chart are introduced.Then,the experimental steps of the system direction finding are given.Finally,the effectiveness of the algorithm is verified by the measured data processing results.