Research On Target Parameter Estimation With Frequency Diverse Array MIMO Radar Based On Subspace Algorithms

The frequency diverse array(FDA)with different carrier frequency can acquire the beampattern,which is angle-dependent and range-dependent.Therefore,the FDA radar is superior in suppressing range-dependent interference,identifying targets,structuring secure communication.By converging the multiple-input multiple-output(MIMO)technique with the FDA in recent years,the neotype radar system,named FDA-MIMO radar,has the rangedependent beampattern of FDA radar and large degree of freedom of MIMO radar,so that FDA-MIMO radar produces greater receiver gain and flexibility.Since that direction of departure(DOD)and range are coupling inherently for FDA radar,the primary issue of parameter estimation with FDA-MIMO radar is how to decouple DOD and range.Furthermore,considering multi-dimensional parameter estimation in multi-objective case,matching parameter is crucial.Thus,this paper exploited the parameter estimation with the monostatic and bistatic FDA-MIMO radar.First,the structure and beampattern of FDA are introduced.Specifically,we analyze the antenna structure of PA and FDA,and derive the expression of beampattern of them.Then,the range-dependent beampattern of FDA is demonstrated by analyzing the simulations.Besides,the signal models for the monostatic and bistatic FDA-MIMO radar are introduced comprehensively.In addition,two basic algorithms based on the subspace theory are introduced.The estimation performance of two subspace-based algorithms are verified by the several simulation results.An improved parameter estimation algorithm based on subspace is proposed to solve the issues of the monostatic FDA-MIMO radar which contains the low accuracy,large complexity and mismatch.In the proposed algorithm,the extended received data is constructed to satisfy the property of Centro-Hermitian.Additionally,the complex-valued data is transformed into the real-valued data by the unitary transformation.Then based on the real-valued rotation invariance of signal subspace,which is obtained by eigenvalue decomposition,the phase matrices about angle and range are acquired.As for multiple targets,a novel phase matrix is created to achieve the same fuzzy matrix,and then the correct parameter estimation of angle and range is obtained.The numerous simulation results are implemented to verify that the high-precision of parameter estimation is achieved,and the computational complexity of the proposed algorithm is reduced.Especially,the superiority is evident in the small number of snapshots.Finally,a joint angle and range estimation algorithm via real-valued subspace decomposition is proposed to solve the multi-objective mismatch problem,which occurred in conventional algorithm with bistatic FDA-MIMO radar.In the proposed algorithm,we design a scheme of dividing transmitting array into multiple non-overlapping subarrays,and different subarrays adopt different frequency increments.In addition,the received data of transmitting subarray are extended to make them possess the property of Centro-Hermitian.Then,take the unitary transformation to the extended data,and obtain the real-valued signal subspace.Based on the various frequency increment,a method is proposed to decouple DOD and range.Considering the multi-dimensional parameter mismatch case,a novel pairing method based on subarray data is first proposed to acquire the parameter estimation.Compared with the existed algorithm,the proposed algorithm possesses lower complexity and higher estimation accuracy,and obtain the correct parameter estimation in multiobjective case.With the extensive simulation results,the proposed algorithm is verified to possess the lower complexity and higher accuracy,and obtain the position of multiple targets.