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首页> 外文会议>IEEE International Conference on Information and Automation;IEEE International Conference on Automation and Logistics >Adaptive compressed sensing based randomized step frequency radar with a weighted PSO
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Adaptive compressed sensing based randomized step frequency radar with a weighted PSO

机译：基于自适应压缩感知的加权PSO随机步进频率雷达

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摘要

In this paper, a novel randomized step frequency radar that combined the adaptive waveform design and the off-grid point effect simultaneously in the scheme of weighted Particle Swarm Optimization(PSO) is proposed, and the range and velocity joint estimating are recovered by exploiting sparseness of the targets and by invoking compressed sensing (CS) theory. In this new mechanism, each of the dictionary matrix element was first extended by adopting Taylor expansion to an arbitrary precise off-grid point, instead of only the points in a discrete form. Then by adding the new generated information into the dictionary matrix adaptively, an updated time-varying new dictionary matrix is yielded. Finally, in order to overcome the local minima in the traditional CS theory, a weighted PSO dynamic optimal method is adopted, where the convergence speed is increased due to the weighted factor introduced in the PSO. It is not necessary to know exactly the target parameters when using our approach, instead, coarse coding bounds of target parameters are enough for the algorithm, which can be done once and for all off-line, and it is only necessary to specify the initial scopes of the velocity and the range of the target. The proposed weighted PSO based waveform design approach has the potential to achieve much higher estimation accuracy, a faster convergence speed and robustness against unpredictable perturbations for range, a high precision in randomized step frequency radar.
机译：提出了一种在加权粒子群优化（PSO）方案中将自适应波形设计与离网点效应同时结合的新型随机步频雷达，利用稀疏性恢复了距离和速度联合估计通过调用压缩感知（CS）理论确定目标。在这种新机制中，首先通过采用泰勒展开法将每个字典矩阵元素扩展到任意精确的离网点，而不仅仅是离散形式的点。然后，通过将新生成的信息自适应地添加到字典矩阵中，得到更新的时变新字典矩阵。最后，为了克服传统CS理论中的局部极小值，采用了加权PSO动态最优方法，该方法由于引入了PSO中的加权因子而提高了收敛速度。使用我们的方法时，不必精确地了解目标参数，相反，目标参数的粗略编码范围对于算法而言就足够了，可以一次性完成所有离线操作，仅需指定初始速度范围和目标范围。所提出的基于加权PSO的波形设计方法具有实现更高估计精度，更快收敛速度​​和针对范围不可预测扰动的鲁棒性，随机步进频率雷达高精度的潜力。

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