基于优化Golomb尺的超分辨多点测距
Superresolution Multipoint Ranging with Optimized Sampling via Orthogonally Designed Golomb Rulers
摘要：
我们认为，有效地和准确地执行源和多个接收器之间的测距测量的问题，所需的基于距离的无线定位系统。为此，提出了一种新的多点测距算法，该算法通过将超分辨率技术应用于测距问题来获得，为了说明ToA和PDoA的具体情况，统一在相同的数学框架下。该算法采用正交非均匀采样方案通过Golomb规则优化，以有效的方式处理多点测距。由于该方法需要设计的相互正交套哥伦布统治者具有同等的属性-一个问题，在目前的文献中没有找到解决方案-一个新的遗传算法来完成这项任务，这也被发现优于最好的已知的替代品时，用于生成一个单一的统治者。最后，对整体优化的多点测距解决方案进行CRLB分析，并与仿真结果进行比较，验证了所提出的技术。
英文摘要：
We consider the problem of performing ranging measurements between a source and multiple receivers efficiently and accurately, as required by distance-based wireless localization systems. To this end, a new multipoint ranging algorithm is proposed, which is obtained by adapting superresolution techniques to the ranging problem, using for the sake of illustration the specific cases of ToA and PDoA, unified under the same mathematical framework. The algorithm handles multipoint ranging in an efficient manner by employing an orthogonalized non-uniform sampling scheme optimised via Golomb rulers. Since the approach requires the design of mutually orthogonal sets of Golomb rulers with equivalent properties -- a problem that founds no solution in current literature -- a new genetic algorithm to accomplish this task is presented, which is also found to outperform the best known alternative when used to generate a single ruler. Finally, a CRLB analysis of the overall optimised multipoint ranging solution is performed, which together with a comparison against simulation results validates the proposed techniques.
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