As wireless networks and sensing environments grow increasingly complex, it is challenging for a single measurement technique to consistently provide accurate, reliable, and robust positioning across all scenarios. This reality has led to the growing importance of fusion-based positioning methods, which integrate complementary data from multiple sources, such as Bluetooth beacons, inertial sensors, floor plans, and visual inputs, to overcome the limitations of individual techniques. This chapter introduces how fusion systems can compensate for weaknesses like signal occlusion, multipath propagation, or hardware constraints, ultimately delivering more consistent and high-precision positioning.