Cost-optimization through the minimization of hardware and processing costs with minimal loss in performance is an interesting design paradigm in evolving and emerging Multiple-Input-Multiple-Output (MIMO) radar syste...
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Cost-optimization through the minimization of hardware and processing costs with minimal loss in performance is an interesting design paradigm in evolving and emerging Multiple-Input-Multiple-Output (MIMO) radar systems. This optimization is a challenging task due to the increasing Radio Frequency (RF) hardware complexity as well as the signal design algorithm complexity in applications requiring high angular resolution. Towards addressing these, the paper proposes a low-complexity signal design framework, which incorporates a generalized time multiplex scheme for reducing the RF hardware complexity with a subsequent discrete phase modulation. The scheme further aims at achieving simultaneous transmit beamforming and maximum virtual MIMO aperture to enable better target detection and discrimination performance. Furthermore, the paper proposes a low-complexity signal design scheme for beampattern matching in the aforementioned setting. The conducted performance evaluation indicates that the listed design objectives are met.
In order to resolve the issues with overlapping elements in location-related applications, an element adjustment method is proposed. This adjustment method can be implemented using a non-iterative algorithm which can ...
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In order to resolve the issues with overlapping elements in location-related applications, an element adjustment method is proposed. This adjustment method can be implemented using a non-iterative algorithm which can significantly improve the processing efficiency of overlap removal. The method first sorts out all the elements according to the distance from the preset Starting Point. Repulsive offset adjustment is used for circular elements. It can be validated through theoretical derivation. Finally, the influencing factors of algorithm parameters are analyzed. Suggestions are given to further optimization of the algorithm. Experimental results show that it can effectively remove overlapping. The relative positional relationship among elements can be preserved to the greatest extent. User's verifications and expert's evaluation show that it can also achieve high recognition rate between the geographic region and its representation element.
This paper involves solving the generalized sensor calibration problem AR = RB with rotations A, B, R is an element of SO(3), where A, B are known and R is to be figured out. We introduce a methodology called the quat...
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This paper involves solving the generalized sensor calibration problem AR = RB with rotations A, B, R is an element of SO(3), where A, B are known and R is to be figured out. We introduce a methodology called the quaternion decomposition from rotation (QDR) to obtain a robust solution to this problem. The method is applicable to all those cases where A, B are noise-free, noisy, or even not rigid. The non-iterative framework of the eigen-decomposition of 4 x 4 matrices is derived to give very computationally efficient analytical quaternion result. By numerical examples and experimental robotic results, the effectiveness of the proposed method has been verified. The proposed solution is evaluated to own at least the same accuracy and robustness of currently hest algorithm, using real-world experiments for the camera/magnetometer sensor calibration on a quadrotor, while it takes much faster computation speed than all existing representatives to the best of our knowledge.
Noise robust compressive sensing algorithm is considered. This algorithm allows an efficient signal reconstruction in the presence of different types of noise due to the possibility to change minimization norm. For in...
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ISBN:
(纸本)9781479989997
Noise robust compressive sensing algorithm is considered. This algorithm allows an efficient signal reconstruction in the presence of different types of noise due to the possibility to change minimization norm. For instance, the commonly used l(1) and l(2) norms, provide good results in case of Laplace and Gaussian noise. However, when the signal is corrupted by Cauchy or Cubic Gaussian noise, these norms fail to provide accurate reconstruction. Therefore, in order to achieve accurate reconstruction, the application of l(3) minimization norm is analyzed. The efficiency of algorithm will be demonstrated on examples.
Phase-Only Holograms (POHs) are widely embraced for their advantages, including high quality reconstruction and high diffraction efficiency. However, when generating POHs, the utilization of solely random or quadratic...
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Phase-Only Holograms (POHs) are widely embraced for their advantages, including high quality reconstruction and high diffraction efficiency. However, when generating POHs, the utilization of solely random or quadratic phase often leads to speckle noise and ringing artifacts in reconstructed image. To tackle this issue, this paper introduces a non-iterative Optimized Hybrid Phase-only Holograms (OHPOHs) algorithm designed for Fresnel Lensless Holographic Projection system. The proposed algorithm consists of three steps. Firstly, selected quadratic phase and random phase are combined with an appropriate weight coefficient to create a hybrid phase. Next, the hybrid phase is iteratively optimized. Lastly, a complex amplitude is formed by combining the optimized hybrid phase and arbitrary target image, resulting in the generation of POHs through a single-step calculation. Additionally, this paper explores the process of selecting an appropriate weight coefficient for the phase blending procedure. Numerical experiments demonstrate that the non-iterative OHPOHs achieves superior reconstruction of high-quality images by effectively suppressing speckle noise and ringing artifacts. Moreover, this improvement is achieved while maintaining computation efficiency comparable to that of the Optimized Random Phase (ORAP) algorithm and the Hybrid Phase-Only Hologram (HPOHs) algorithm.
As for vision-based pose estimation, which is also known as the PnP problem, non-iterative algorithms are more efficient. Precise extraction of 2D projections of feature points is important. If the projections of the ...
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As for vision-based pose estimation, which is also known as the PnP problem, non-iterative algorithms are more efficient. Precise extraction of 2D projections of feature points is important. If the projections of the feature points are not accurately extracted, the pose estimation accuracy is reduced. Under the condition of natural light, a camera captures the images of feature points, and the existence of high-light regions in the image affects the extraction accuracy of 2D projections of feature points, which reduces the number of effective feature points and leads to poor pose estimation accuracy. In the redundant cases (n > 4), redundant feature points are introduced as additional information, increasing the number of effective feature points to reduce the impact of high-light regions and improve the pose estimation accuracy. For the non-redundant cases (n = 4), it was difficult to ensure pose estimation accuracy. To solve this problem, a non-iterative pose estimation method based on the optimum polarization angle via four corner points of a parallelogram was proposed in this study. First, a model for solving optimum polarization angle was established. Thereafter, on the premise of the optimum polarization angle, the images were captured. Finally, the projections of the four corner points of a parallelogram were extracted, and the object pose was solved non-iteratively according to the four corner points. The corner point extraction experimental results show that the slope difference between the two parallel sides of each parallelogram under the condition of optimum polarization angle is less than that under the condition of natural light, thereby proving the improvement of the imaging quality. Measurement accuracy verification experiments prove that our pose estimation algorithm and the optimum polarization angle is the best combination to improve the non-iterative pose estimation accuracy in non-redundant cases. In the measurement range of - 60-+60 degrees, th
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