SINR improvement based on joint design of transmit covariance matrix and receive filter design for colocated MIMO radar

Abstract

This paper examines we aim to improve the performance of target detection in the presence of signal‐dependent interference in multiple‐input multiple‐output radar through the joint design of the receiver spatial filter and the covariance matrix of waveforms. For this purpose, the signal‐to‐interference‐plus‐noise ratio is considered as the design criterion. The proposed method is a sequential algorithm that calculates the receiver filter coefficients through a closed form in each step of the algorithm. The correlation of transmit waveforms is also calculated utilizing the coordinate descent method. The covariance matrix is designed in two forms. In the first form, it is assumed that the optimal matrix is a Toeplitz matrix which leads to a simpler design. In the second form, the covariance matrix is calculated by combining orthogonal waveforms, which yields better performance for interfere reduction. Simulation results show that the proposed method has more appropriate performance in comparison with other methods.

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss

“…Second, the joint optimization of the radar transmitter and receiver is considered to solve the waveform design problem [ 14 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. We will focus on this waveform design approach.…”

“…However, in [ 14 , 19 , 20 , 23 , 24 , 29 ], the waveform optimization problem established adopts energy constraint, which cannot give full play to the amplifier’s maximum performance and reduce the output SINR. In [ 1 , 14 , 15 , 17 , 20 , 21 , 23 , 25 , 27 , 33 , 34 , 36 ], etc., pulse compression and ambiguity characteristics of the waveform are not taken into account, and the solution of [ 14 , 19 , 23 ] cannot guarantee global optimization. Therefore, the performance of the system can be improved through the design of the joint transmit waveform and receive filter based on semidefinite programming.…”

“…In [ 15 ], author Ahmed proposed a method that uses the cosine function with a step size of from 0 to form a semi-positive definite Toeplitz matrix and covariance matrix generated by an auto-regressive process. Author Imani in [ 27 ] was inspired by [ 15 ], and author Imani proposed two algorithms based on joint design to design a full-rank waveform covariance matrix. It overcomes the shortcoming of [ 15 ] that the rank of the covariance matrix is only 2, and shows better anti-interference performance.…”

“…It overcomes the shortcoming of [ 15 ] that the rank of the covariance matrix is only 2, and shows better anti-interference performance. However, since the covariance matrix generated by eigenvalue decomposition in first method is constrained by the fixed real set , it cannot produce the optimal solution in [ 27 ].…”

“…Moreover, the designed waveform usually imposes some constraints on the waveform in actual engineering applications, for example, constant envelope constraint [ 3 , 8 , 9 , 10 , 11 , 12 , 16 , 18 , 21 , 27 , 30 , 32 , 34 , 35 , 36 , 38 , 50 ], PAR constraint [ 8 , 9 , 19 , 35 , 47 ], similarity constraint [ 11 , 16 , 29 , 43 , 50 , 51 ] or sidelobe constraint [ 15 , 21 , 22 , 30 , 32 , 33 , 38 , 47 , 50 ]. The constant envelope constraint is more stringent than the PAR constraint because most radar transmitters use nonlinear power amplifiers, which usually work in a saturated state, and the amplitude of the waveform is required to be constant.…”