Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology.

翻译：室内和户外环境的精确和准确本地化是一个具有挑战性的问题,目前对若干实际应用构成重大限制。超广带(UWB)本地化技术是解决这个问题的低成本宝贵解决办法,然而,非视觉(NLOS)条件和具体无线电环境的复杂性很容易在测距方面造成积极的偏差,导致定位估计非常不准确和不令人满意。鉴于此,我们利用深神经网络优化技术的最新进展及其在超低功率微控制器上的应用,以引入有效的范围差错缓解解决方案,为NLOS或LOS条件中的校正提供几兆瓦的电源。我们的广泛实验认可了我们低成本和节能方法的优势和改进。