We consider a dual-function radar communication (DFRC) system employing frequency hopping (FH) radar waveforms. Each communication symbol is represented by a phase-modulated pulse sequence that multiplies the radar hops in fast-time and transmitted from a multiple-input multiple-output radar platform. In this respect, the DFRC system considered follows the code-shift keying (CSK) signal embedding strategy. The phase modulations include phase-shift keying (PSK), derivative PSK, and continuous phase modulation. The CSK pulse sequences and the FH code are jointly designed to reduce range sidelobe levels (RSLs) and combat range sidelobes modulation. It is shown that the joint optimization using the genetic algorithm yields desired CSK sequences and FH code matrix with less computational complexity compared to the exhaustive search algorithm. The power spectral density and detection performance of various DFRC systems are analyzed, underscoring the tradeoff between RSLs and spectral sidelobe levels. The proposed DFRC system with optimum phase sequences and hopping code achieves high data communication rates by permitting frequencies to be reused in the radar hops.

In the area of near-field millimeter-wave imaging, the generalized sparse array synthesis (SAS) method is in great demand. The traditional methods usually employ the greedy algorithms, which may have the convergence problem. This paper proposes a convex optimization model for the multiple-input multiple-output (MIMO) array design based on the compressive sensing (CS) approach. We generate a block shaped reference pattern, to be used as an optimizing target. The pattern occupies the entire imaging area of interest in order to involve the effect of each pixel into the optimization model. In MIMO scenarios, we can fix the transmit subarray and synthesize the receive subarray, and vice versa, or doing the synthesis sequentially. The problems associated with focusing, sidelobes suppression, and grating lobes suppression of the synthesized array are examined in detail. The simulation results reveal that the proposed method can efficiently synthesize a sparse array within a significantly shorter time in comparison to state-of-the-art techniques. As a result, it can be applied to generate arrays with large apertures. Additionally, both numerical and experimental results demonstrate that the synthesized sparse array offers superior image quality when compared to both state-of-the-art and commonly used sparse arrays with an equivalent number of antenna elements.

Radar-based assisted living has received significant amount of research interest in recent years. However, most of the existing works are focused only on free space detection. When it comes to the through-the-wall human motion detection, the wall media and indoor static non-human targets would cause clutters and significantly corrupt the motion information of human targets behind wall. In this work, we first propose to use a low center-frequency ultra-wideband (UWB) radar system to probe the behind wall scene. Then, a Robust Principal Component Analysis (RPCA) based subspace decomposition technique is employed not only to remove the stationary clutters in raw range slow-time map but also to mitigate the multipath effects in the time-frequency map. Onsite experiments of human motions detection behind a single layer of reinforced concrete wall are carried out to investigate the performance of the technique. Lastly, a two-dimensional (2D) PCA based method and a convolutional neural network (CNN) based method are employed for the motion classification. The results based on the enhanced features obtained by the proposed method show a higher accuracy than those based on the traditional features through mean value subtraction.

In this article, a hierarchical stochastic optimization algorithm for profiling of multiple high-contrast buried objects in large investigation domains (IDs) is presented. As this problem is highly non-linear and ill-posed, a combination of different profiling modalities is required to tackle the challenges. First, an initialization step using qualitative diffraction tomography (DT) method is performed to not only limit the ID to scatterers’ locations but also obtain an approximation of their dielectric permittivity range. Then, an algorithm which combines the iterative multi-scaling approach (IMSA) with the reconstruction capabilities of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented. IMSA is a multistep strategy, which starts with coarse meshes in lower frequencies, then, step by step, tightens the ID to the newly found domain of scatterers and uses finer meshes for partitioning them. This procedure enhances the resolution without increasing the number of unknowns. In each step, the inversion process is executed using the global optimization technique of CMA-ES. The proposed technique uses the full advantages of global optimization technique and at the same time, by executing it on a multi-scaling scheme, and using the initializing step, reduces the number of unknowns, the degree of freedom in the search space, and the required measured data. The Numerical assessments for various scenarios are performed that clearly shows an acceptable dielectric profile retrieval, even for inhomogeneous dielectric distributions or noisy measurements. Moreover, a comparison between CMA-ES and other global optimization algorithms is performed, which reveals the outperformance of CMA-ES in these scenarios.

In this work, we present a statistical comparative study of the applications of EP, GA, micro-GA and PSO to two classical antenna problems, namely the gain optimization of Yagi-Uda array and the side-lobe-level reduction of a non-uniform spaced (aperiodic) linear array. The convergences of these algorithms for both best and average fitness trajectories are compared. In addition the performances of GAs, micro-GA with both single and uniform cross-over, and PSO with absorbing, invisible and reflective boundaries are discussed and included in the comparison.