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Many techniques propose to combine RF signal data with readings from other sources beyond inertial sensors. SurroundSense [149] utilizes fingerprints of a location based on RF (GSM, Wi‐Fi) signals as well as ambient sound, light, color, and the layout‐induced user movement (detected by an accelerometer). Cameras, microphones, and accelerometers on a Wi‐Fi‐enabled Nokia N95 phone were used to sense the fingerprint information. The sensed values are recorded, pre‐processed, and transmitted to a remote SurroundSense server. The goal of pre‐processing on the phone is to reduce the data volume that needs to be transmitted. Once the sensor values arrive at the server, they are separated by the type of sensor data (sound, color, light, Wi‐Fi, accelerometer) and distributed to different fingerprinting modules. These modules perform a set of appropriate operations, including color clustering, light extraction, and feature selection. The individual fingerprints from each module are logically inserted into a common data structure, called the ambience fingerprint, which is forwarded to a fingerprint matching module for localization. Support vector machines (SVMs), color clustering, and other simple methods were used for location classification.

The Acoustic Location Processing System (ALPS) [156] combines BLE transmitters with ultrasound signals to improve localization accuracy and also help users configure indoor localization systems with minimal effort. ALPS consists of time‐synchronized beacons that transmit ultrasonic chirps that are inaudible to humans, but are still detectable by most modern smartphones. The phone uses the TDoA of chirps to measure distances. ALPS uses BLE on each node to send relevant timing information, allowing for the entire ultrasonic bandwidth to be used exclusively for ranging. The platform requires a user to place three or more beacons in an environment and then walk through a calibration sequence with a mobile device where they touch key points in the environment (e.g. the floor and the corners of the room). This process automatically computes the room geometry as well as the precise beacon locations without needing auxiliary measurements. Once configured, the system can track a user’s location referenced to a map. Other techniques such as SmartLOCUS [157] and Cricket [158] also use a combination of RF and ultrasound technologies, where the TDoA between RF and ultrasound signals (generated by wall‐ and ceiling‐mounted beacons) is used to measure distance and localize mobile subjects.

Radianse [159] and Versus [160] use a combination of RF and IR signals to perform location positioning. Their tags emit IR and RF signals containing a unique identifier for each person or asset being tracked. The use of RF allows coarse‐grain positioning (e.g. floor level granularity), while the IR signals provide additional resolution (e.g. room granularity). The EIRIS local positioning system [161] uses an IRFID triple technology that combines IR, RF (UHF), and LF (RF low‐frequency transponder) signals. It combines the advantages of each technology, that is, the room location granularity of IR, the wide range of RF, and the tailored range sensitivity of LF.

The CUPID2.0 indoor positioning system [162] combines ToF‐based localization with signal strength information to improve indoor localization with Wi‐Fi RF signals. The proposed architecture consists of a location server and multiple Wi‐Fi APs, each of which talks to the mobile device. ToA‐based trilateration methods are used to determine the device location. In particular, the time of flight of the direct path (TFDP), as calculated from the data‐ACK exchange between the AP and the device, is used for distance estimation. TFDP is then combined with measurements of signal strength, particularly the EDP [82], to improve accuracy and also ensure scalability. The system was implemented, deployed, and analyzed at six cities across two different continents for more than 14 months with 40 different mobile devices and more than 2.5 million location fixes, and was shown to achieve a mean localization error of 1.8 m.