Non-contact vital signs monitoring with frequency modulated continuous wave (FMCW) MIMO radar has attracted increasing attention. However, in practical applications, existing methods cannot accurately and robustly measure vital signs due to the influence of indoor multipath propagation. Therefore, this paper proposes a joint design method of waveform parameter optimization and transmit-receive weighting based on FMCW-MIMO radar to suppress multipath interference. This method uses quasi-orthogonal FMCW with low range sidelobe level, which can separate undesired multipath from the desired signal and suppress it by adaptive transmit-receive weighting. Theoretical and simulation results show that the method has good performance in indoor multipath environments with mean errors of 0.608 (bpm) for respiration rate and 3.164 (bpm) for heart rate.

To achieve full coherence, it is indispensable for distributed coherent aperture radar (DCAR) to estimate coherence parameters (CPs) using orthogonal waveforms in MIMO mode. However, incomplete orthogonality between waveforms and initial time and phase synchronization errors between the subapertures introduce estimation errors, which degrade full coherence performance. To tackle these issues, we propose an orthogonal waveform design framework based on ambiguity function (AF) shaping to improve CPs’ estimation accuracy. We combine the alternating direction method of multipliers (ADMM) and the majorization-minimization (MM) algorithm to transform the nonconvex quartic optimization problem into a quadratic optimization problem. Numerical simulation results show that the optimized waveforms significantly enhance the estimation accuracy of CPs.

This letter first outlines a flexible conformal array geometric model on variable airborne wings, which can undergo complete shape change according to different combat situations, whereas the deformation may result in array beampattern distorted. In order to realize fast beampattern synthesis for a flexible conformal array, we propose a real-time beampattern optimization algorithm, where we split the protoproblem into two sub-problems based on the alternating direction method of multipliers (ADMM). In the first sub-problem, we transform the quadratic inequality constrained problem into a linear equality constrained problem, and then get the iterative update expression for the variables. Unlike the traditional interior point method, the simulation results demonstrate that the proposed algorithm can efficiently solve large-scale convex optimization problems and greatly reduce the computational complexity.