Design of a low sidelobe 4D planar array including mutual coupling

Abstract-An efficient approach is presented for the design of a low sidelobe four-dimensional (4D) planar antenna array, taking into account mutual coupling and platform effect. The approach is based on the combination of the active element patterns and the differential evolution (DE) algorithm. Different from linear and circular arrays, the mutual coupling compensation in a planar array is more complicated since it requires numerous data of the active element patterns in different azimuth planes. In order to solve this problem, a useful interface program is developed to get these data from commercial software HFSS automatically. Also different from conventional low sidelobe arrays with tapered amplitude excitations, the low sidelobe in the 4D array is realized using time-modulation technique under uniform static amplitude and phase conditions. The DE algorithm is used to optimize the time sequences which are equivalent to the complex excitations in conventional arrays. Both computed results and simulated results in HFSS show that a −30 dB sidelobe pattern can be synthesized in a 76-element planar array with an octagonal ground plane and a radome, thus verifying the proposed approach

Direction Finding Using Multiple Sum and Difference Patterns in 4D Antenna Arrays

Traditional monopulse systems used for direction finding usually face the contradiction between high angle precision and wide angle-searching field, and a compromise has to be made. In this paper, the time modulation technique in four-dimensional (4D) antenna array is introduced into the conventional phase-comparison monopulse to form a novel direction-finding system, in which both high angle resolution and wide field-of-view are realized. The full 4D array is divided into two subarrays and the differential evolution (DE) algorithm is used to optimize the time sequence of each subarray to generate multibeams at the center frequency and low sidebands. Then the multibeams of the two subarrays are phase-compared with each other and multiple pairs of sum-difference beams are formed at different sidebands and point to different spatial angles. The proposed direction-finding system covers a large field-of-view of up to ±60° and simultaneously maintains the advantages of monopulse systems, such as high angle precision and low computation complexity. Theoretical analysis and experimental results validate the effectiveness of the proposed system

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Analyses of the performance of adaptive subspace detector on fluctuating target detection in system-dependent clutter background

The adaptive subspace detector in system-dependent clutter background (SDC-ASD) is proved to be able to improve the detection performance for deterministic target detection in the authors' previous study. However, the exact performance of the SDC-ASD for fluctuating target detection is still unknown. In this study, with the aid of matrix decomposition theory, the analytical performance of the SDC-ASD for detecting fluctuating target is considered and assessed. At the design stage, the rigorous mathematical derivation processes for the exact theoretical detection performance of the SDC-ASD for fluctuating target detection is derived. The theoretical results, which have explicitly expressions for both the false alarm probability and the detection probability, not only provide an effective mathematical method to analyse the fluctuating target detection performance, but also can further simplify the deterministic target detection assessments. At the analysis stage, Monte Carlo simulations are resorted to validate the analytical results. Numerical experiments are conducted to assess the effectiveness of the SDC-ASD for fluctuating target detection. Results show that the SDC-ASD, by dealing separately with the clutter and the noise, performs the best in comparison with its published counterparts, i.e. the generalised likelihood ratio detectors, the adaptive matched filters, the adaptive subspace detectors and the low-rank detectors based on sample covariance matrix

Adaptive polarimetric detection method for target in partially homogeneous background

In this paper,the problem of enhancing the detection performance of detector for target in partially homogeneous background is addressed. ased on a general measurement model, a new constant false alarm rate (CFAR) adaptive matched detector (AMD) is proposed through a two-step design procedure. The detection performance of the AMD is theoretically analyzed. Then, the correctness of the analytical results and the effectiveness of the AMD are validated through numerical experiments and IPIX radar data. To further improve the detection performance of AMD, optimal polarimetric waveform design is approached. The waveform is designed by optimally selecting the transmitted polarization that maximizes a non-central parameter of the detection probability. Numerical experiments are provided to validate the performance improvement by comparing the optimal AMD with the optimal adaptive subspace detector(ASD) and the fixed AMDs. Comparison results show that a gain of 1–5 dB is obtained by the optimal AMD