In this article, a novel method for passive indoor localization using LED luminaires is proposed where explicit user participation is not required. This approach measures changes in the impulse response between sources and receivers and estimates a location based on optical channel sounding data. An exponential integrating-sphere model is used to represent object impulse response (OIR) from each luminaire source-receiver pair, which is obtained by subtracting impulse response (IR) of the room background (i.e., without an object) from IR when the object is present inside the room. This model is represented as a function of 3D position of the object and depends on both source and receiver parameters and the physical geometry of the room. An analytical expression of the Cramér–Rao lower bound (CRLB) on the proposed passive indoor localization method is derived. The position is also estimated by using a maximum likelihood (ML) estimator which gives the position estimate by maximizing the log-likelihood function of the received noisy OIR waveforms. The results show that the signal-to-noise ratio (SNR) and number of source-receiver pairs used in the estimation, play a crucial role in performance. Typical localization root-mean squared error is less than 10 cm over a broad range of light intensities and object locations.

中文翻译：

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使用可见光进行无源室内定位的性能界限

在本文中，提出了一种无需用户明确参与的使用 LED 灯具进行被动室内定位的新方法。这种方法测量源和接收器之间脉冲响应的变化，并根据光信道探测数据估计位置。指数积分球模型用于表示来自每个灯具源 - 接收器对的对象脉冲响应 (OIR)，这是通过从 IR 中减去房间背景（即没有对象）的脉冲响应 (IR) 获得的存在于房间内。该模型表示为对象 3D 位置的函数，并取决于源和接收器参数以及房间的物理几何形状。推导出了所提出的被动室内定位方法的 Cramér-Rao 下界 (CRLB) 的解析表达式。位置还通过使用最大似然 (ML) 估计器进行估计，该估计器通过最大化接收到的噪声 OIR 波形的对数似然函数来给出位置估计。结果表明，估计中使用的信噪比 (SNR) 和源接收器对的数量对性能起着至关重要的作用。在广泛的光强度和物体位置范围内，典型的定位均方根误差小于 10 厘米。结果表明，估计中使用的信噪比 (SNR) 和源接收器对的数量对性能起着至关重要的作用。在广泛的光强度和物体位置范围内，典型的定位均方根误差小于 10 厘米。结果表明，估计中使用的信噪比 (SNR) 和源接收器对的数量对性能起着至关重要的作用。在广泛的光强度和物体位置范围内，典型的定位均方根误差小于 10 厘米。