LOS/NLOS and Urban Degradation Detection: Identifying Bad Signals

Introduction

In open-sky environments, virtually all GNSS signals arrive at the receiver via the direct line-of-sight (LOS) path from the satellite. In urban, industrial, and constrained environments, signals are frequently blocked or arrive via reflected paths - a condition called Non-Line-of-Sight (NLOS) reception. NLOS signals are the most damaging form of GNSS signal degradation because they introduce large, systematic errors that standard receiver quality indicators often cannot detect. Identifying and mitigating NLOS signals is one of the central challenges of urban GNSS positioning.

Line-of-Sight vs Non-Line-of-Sight

A line-of-sight signal is one where the direct geometric path from the satellite to the antenna is unobstructed. The signal arrives with a travel time proportional to the true geometric distance, plus the standard atmospheric delays that can be modelled and corrected. The pseudorange error is typically less than 1 metre under good tracking conditions.

A non-line-of-sight signal occurs when the direct path is blocked - by a building, hill, bridge, or other structure - and the signal reaches the antenna only via a reflected path off a nearby surface. The reflected path is longer than the direct geometric distance by the detour the signal takes to reach the antenna. This extra path length, divided by the speed of light, adds directly to the measured pseudorange as a positive bias ranging from a few metres to tens of metres.

Key Concept: Unlike multipath - where both the direct and reflected signals arrive simultaneously - pure NLOS reception involves only the reflected signal, with the direct path completely blocked. The receiver has no information that the measurement is biased: the signal may appear strong and well-tracked by all standard quality metrics.

Urban Effects on GNSS

Building blockage: Tall buildings obstruct large sectors of the sky, reducing the number of satellites in view and degrading geometry (raising DOP).
NLOS reception: Signals from blocked satellites reach the receiver via building facade reflections. Glass, metal, and smooth concrete surfaces are the most effective reflectors.
Multipath: For satellites that have a partially unobstructed direct path, reflections from nearby surfaces arrive simultaneously with the direct signal, distorting the code and carrier-phase measurements.
Diffraction: At building edges, signals bend slightly around the obstruction. The diffracted signal arrives with a slightly longer path than the LOS, introducing a small positive range bias.

Research in Hong Kong urban canyon tests found that NLOS bias contributed over 50% of the total horizontal position error in the worst-affected areas, far exceeding contributions from atmospheric delays or receiver noise.

Note: Multi-constellation receivers increase the number of satellites visible in urban environments but do not inherently solve the NLOS problem. Adding satellites from Galileo or BeiDou that arrive via NLOS paths can actually worsen the solution if those measurements are incorporated without quality checking.

Detection Methods

C/Nâ‚€ analysis: LOS signals generally show higher and more stable C/Nâ‚€ values than NLOS signals. However, a strong building reflection can have high C/Nâ‚€ while being heavily biased - this indicator alone is not reliable.
Pseudorange residual monitoring: In a position solution with sufficient redundancy, NLOS satellites tend to produce large post-fit residuals. Residual-based exclusion (similar to RAIM) can remove the most egregiously biased measurements.
3D city model shadow matching: By computing which satellites are geometrically visible from a candidate position using a 3D building model, software can predict which signals should be LOS and which should be blocked. Satellites that are predicted blocked but being tracked are flagged as NLOS.
Machine learning classifiers: Trained on features including C/Nâ‚€ patterns, pseudorange rate, satellite elevation, and receiver position context, ML classifiers have demonstrated 80–90% NLOS detection accuracy in controlled urban environments.
IMU-aided consistency checking: When GNSS is integrated with an IMU, large sudden position jumps inconsistent with the inertial trajectory can be attributed to NLOS measurement incorporation and used to flag suspect signals.

Mitigation Strategies

Once NLOS signals are identified, the options are exclusion (removing them from the solution) or down-weighting (reducing their influence proportionally to their estimated bias). In environments with few available LOS satellites, exclusion may leave insufficient measurements for a position solution, making down-weighting the only feasible option.

At the system architecture level, mitigation of urban degradation requires sensor fusion. Tightly coupled GNSS/IMU integration maintains positioning continuity during satellite outages and provides a reference trajectory against which GNSS measurements can be validated. HD map integration and lane-level matching further constrain the position solution using non-GNSS information, reducing the impact of NLOS errors on the final navigation output.

Vital Points

NLOS signals are received only via reflected paths because the direct path is blocked - they introduce large positive pseudorange biases invisible to standard quality indicators.
Urban environments combine blockage, NLOS, multipath, and diffraction simultaneously, producing complex and time-varying error patterns.
Detection methods include C/Nâ‚€ analysis, residual monitoring, 3D shadow matching, and machine learning - no single method is reliable in all conditions.
Mitigation in urban environments requires sensor fusion (IMU, HD maps) in addition to signal-level quality monitoring.