How accurate is an indoor positioning system?

In complex indoor environments such as large shopping malls, hospitals, and airports, people often have difficulty quickly finding their destination due to the complex spatial layout. Indoor positioning systems are a key technology for addressing this problem, and their accuracy directly impacts the user experience. However, the accuracy of indoor positioning systems varies significantly due to multiple factors, including environmental interference, device performance, and algorithm design.

Differences in Technical Principles Lead to Diverging Accuracy

Different positioning technologies are based on different physical principles, resulting in significant differences in their theoretical accuracy and practical application performance. For example, ultra-wideband (UWB) technology achieves centimeter-level positioning accuracy by transmitting extremely narrow nanosecond pulses combined with time difference of arrival (TDOA) or angle of arrival (AOA) algorithms. It is commonly used in high-precision scenarios such as industrial robot navigation and prison personnel management. Wi-Fi positioning, on the other hand, relies on matching a signal strength fingerprint library, with a theoretical accuracy of only 1-5 meters and is susceptible to environmental changes. It is primarily used in coarse positioning scenarios such as shopping guides in shopping malls and finding cars in parking lots. Bluetooth beacon positioning estimates distance by measuring signal strength attenuation (RSSI), with an accuracy of approximately 1-3 meters. However, dense beacons (one every 10-20 meters) are required to offset signal fluctuations. Inertial measurement units (IMUs) use accelerometers and gyroscopes to estimate position, achieving short-term accuracy of up to 0.1 meters. However, errors accumulate over time and require regular calibration in conjunction with other technologies. Differences in technical principles directly determine the theoretical upper limit of positioning systems, and users should choose the appropriate technology based on their scenario requirements.

 

Environmental interference is an invisible killer of accuracy

Indoor environments, multipath, non-line-of-sight (NLOS), and dynamic obstacles are three major environmental factors that affect positioning accuracy. Multipath refers to the multiple propagation paths formed by signals reflecting off walls and furniture, resulting in deviations in the time or angle of arrival measured by the receiver. For example, UWB signals in dense metal environments may produce "ghosting" due to reflections, causing the positioning point to deviate from its true location. Non-line-of-sight (NLOS) transmission occurs when the signal is blocked by walls or people, interrupting the direct path. The system mistakenly processes the reflected signal as a direct signal, leading to ranging errors.

Dynamic obstacles (such as moving people and carts) can alter the signal propagation environment in real time. In shopping malls, densely populated areas can cause Wi-Fi signal strength fluctuations exceeding 20dB, increasing fingerprint positioning errors from 1.5 meters to over 3 meters. Furthermore, temperature and humidity fluctuations can affect signal propagation speed, further exacerbating errors. The unpredictability of environmental interference requires positioning systems to possess dynamic calibration capabilities.

 

Device Performance Determines the Lower Limit of Accuracy

The hardware quality of positioning equipment directly impacts signal acquisition and processing accuracy. Taking UWB modules as an example, high-end chips (such as the Decawave DW3000) can achieve a time measurement accuracy of 2ps, corresponding to a ranging error of less than 0.3 meters; low-end chips, however, can have an error exceeding 1 meter. Antenna design is also critical. Directional antennas can reduce multipath interference but are more expensive to deploy. Omnidirectional antennas are less expensive but offer poor signal coverage uniformity.

Sensor fusion capabilities are also a key indicator. High-end positioning terminals (such as professional AR glasses) integrate multiple sensors, including UWB, IMUs, and barometers. Using a Kalman filter algorithm to fuse data, they can reduce vertical positioning error from 1 meter to 0.2 meters. However, single-sensor devices (such as mobile phones that rely solely on Wi-Fi) rarely operate reliably in complex environments. This difference in device performance creates a stark contrast between the positioning experience in high-end scenarios (such as operating room navigation) and consumer scenarios (such as shopping guides).

 

Algorithm Optimization is the Key to Overcoming Accuracy Bottlenecks

Positioning algorithms, which use mathematical models to compensate for environmental interference and device errors, are a key means of improving accuracy. Fingerprint positioning algorithms pre-collect a large number of location-signal strength pairs (such as Wi-Fi RSSI values) to construct a "signal map." During actual positioning, real-time data is matched against this map to achieve positioning. Google Maps version 6.0 uses this technology for indoor positioning, keeping positioning error within 2 meters in shopping malls, but requires regular updates to the fingerprint library to account for environmental changes.

Machine learning algorithms (such as deep neural networks) can automatically extract signal features and adapt to dynamic environments. For example, the NOKOV motion capture system reduces positioning error in industrial scenarios from 5 cm to 2 cm by training models to identify multipath signals. Furthermore, hybrid positioning algorithms (such as UWB + IMU + map constraints) can further enhance robustness by fusing multi-source data. In firefighting and rescue scenarios, such algorithms can maintain positioning continuity through inertial navigation when smoke obscures signals.

 

The accuracy of indoor positioning systems is a comprehensive reflection of technical principles, environmental adaptability, device performance, and algorithm optimization. Users must balance scenario requirements (e.g., medical scenarios require sub-meter accuracy, while retail scenarios are more accepting of meter-level accuracy), cost budget, and environmental complexity when selecting a positioning system. Achieving a balance between accuracy and cost-effectiveness requires technical adaptation.