This study proposes a low-cost indoor positioning system based on a single Light Detection and Ranging (LiDAR) sensor and several fixed reflective reference points. Distances are obtained by trigonometric measurement, and positions are computed by trilateration. In static tests the average error was 7.4 mm. When the target moves at walking speed, small survey errors of the reference points cause the average error to increase to 21.8 mm. Finally, the proposed Reference Point Update Method (RPUM) that continuously corrects reference point coordinates using a moving average of recent residuals reduces the dynamic error from 208.71 mm to 20.34 mm, which is about 90% improvement. The method used in this paper requires no additional hardware and runs in real time.

In this study, an improved particle swarm optimization (PSO) algorithm is designed to construct a weighting model for line-of-sight (LOS) and non-line-of-sight (NLOS) channels in an ultra-wideband (UWB) indoor positioning system. In the proposed algorithm, the particle position represents candidate weight vectors, and the fitness function is defined by the 3D positioning error over multiple test points. An optimized weight modeling framework is proposed for a multi-anchor, three-dimensional UWB indoor positioning system under LOS and NLOS channels. First, the three-dimensional positioning problem is formulated as a multilateration model, and the tag coordinates are estimated via a linearized matrix equation solved by the least-squares method, which explicitly links anchor geometry and ranging errors to the positioning accuracy. To evaluate the proposed method, extensive ranging and positioning experiments are conducted in a realistic indoor environment using up to eight anchors with different LOS/NLOS configurations, including dynamic scenarios with varying numbers of NLOS anchors. The results show that, compared with the conventional unweighted multi-anchor scheme, the PSO-based weighting model can reduce the average 3D positioning error by more than 30% in typical LOS-dominant settings and significantly suppress error bursts in severe NLOS conditions. These findings demonstrate that the combination of mathematical modeling, least-squares estimation, and swarm intelligence optimization provides an effective tool for designing intelligent engineering positioning systems in complex indoor environments, which aligns with the development of smart factories and industrial Internet-of-Things (IIoT) applications.