Table 4.

Development studies with data fusion in NLOS environments.

Performance	Robustness in NLOS conditions	Computational load and suitability for real-world environments	Sensor fusion and modality complementarity	NLOS ıdentification and mitigation approach
Achieved 1.46 m error using deep residual fusion61	Adaptive zoning of NLOS regions improves robustness	Medium complexity with deep learning and clustering	Frequency, power, and time-delay fingerprinting	Iterative clustering and classification for NLOS region separation
53.66% improvement when integrating GNSS with IMU/camera; 22.14% with UWB62	Factor graph optimization improves stability in GNSS obstructions	High due to factor graph optimization	IMU, camera, GNSS, and UWB	Sensor factor residual models and linkage for NLOS mitigation
Location accuracy increased by 71.98% compared to UWB alone in indoor environments63	Robust in challenging indoor NLOS environments using factor graphs	Medium with factor graph fusion and DWKNN	UWB, IMU, and geomagnetic sensors	Dynamic weighting and factor graph fusion to address UWB faults
Average localization error 1.2 m in urban and 1.5 m in forested environments64	Robust to sensor noise and environmental challenges in GPS-obscured areas	Medium with loop closure and multi-sensor fusion	IMU, LiDAR, and visual sensors	Loop closure and sensor fusion mitigate NLOS effects
Sub-decimeter accuracy (0.086 m RMSE) with NLOS correction and FGO65	Factor graph optimization improves robustness in sensor geometry	High due to factor graph and gray prediction model	UWB and LiDAR SLAM	Grey prediction model for NLOS correction and real-time exclusion
Location accuracy increases with the number of reflection paths66	Spatial multipath fusion provides good robustness in complex environments	Medium with signal noise subspace decomposition	Multi-array antennas and spatial multipath data	Spatial multipath fusion for NLOS mitigation
Superior accuracy during obstructed LOS using active/passive fusion67	Improved robustness by fusing active and passive radio measurements	Medium with particle-based sum product algorithm	Active and passive radio measurements	Probabilistic data association for NLOS and OLOS scenarios
Location accuracy increased by 69.16% compared to UWB alone68	Optimized particle filter with sparrow search algorithm improves robustness	High efficiency with reduced particle count	UWB and LiDAR	ISSA optimization reduces particle corruption and NLOS errors
Robust localization in fog using LiDAR and RADAR fusion69	Fusion reduces sensor degradation in low visibility	Medium with clustering and correspondence matching	LiDAR and RADAR	DBSCAN line segmentation and sensor
Improved detection of partially obscured objects through fusion70	Radar and LiDAR mitigate each other’s limitations	Medium with sensor fusion	LiDAR and Radar	Utilizing complementary frequency spectrum
NFOV target estimation combining optical and acoustic sensors71	Recursive Bayesian estimation with probability construction	Medium with Bayesian fusion	Optical and acoustic sensors	Probabilistic fusion for NLOS targets
Landmark-assisted fusion Positioning accuracy improved by 24.4%72	Kalman filter with cumulative error analysis for Lidar noise	Medium with Kalman filtering and landmark assistance	UWB and 2D LiDAR	Signal-aided fusion with noise covariance adaptation
2 × accuracy improvement with adaptive noise covariance73	Variance and power envelope-based NLOS identification	Low complexity with adaptive covariance adjustment	UWB and IMU	Adaptive noise covariance and NLOS identification