Scene analysis indoor positioning enhancements

Abstract

The emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one.

Appendix: Experiment description

Details regarding the experimental testbed are provided below.

1.1 A.1 Indoor environment description

The test environment where the experiment was performed corresponds to the hallway of an office building on the campus of the University of Mannheim. The floor plan of the operation area is nearly 15 × 36 m².

1.2 A.2 Hardware and software specifications

As described in [7] and [20], the test environment is equipped with five Linksys/Cisco WRT54GS and four Lancom L-54g APs. All APs support 802.11b and 802.11g. One Lancom and all Linksys APs are located on the same floor with the testing area whereas three Lancom APs are located in other places inside the building.

As a client, a Lucent Orinoco Silver PCMCIA network card supporting 802.11b was used. This card was plugged into an IBM Thinkpad R51 running Linux kernel 2.6.13 and Wireless Tools 28pre. To collect signal strength samples, the implemented framework contained two parts: a library cooperating with the network card driver to perform scans and capture internal driver information and an easy-to-use application that stores this information in a file together with additional data such as the physical position and a timestamp. Further, the application configures the library to select a scan frequency and scan technique for the signal strength measurements. For these experiments, active scanning was used. Active scanning is defined in the 802.11 standard1, and it is a technique to find a suitable gateway to the Internet by measuring the SS of APs within communication range. Therefore, the available data we had and used for testing our systems were multiple scans with the following information:

• t: timestamp in milliseconds

• id: MAC address of the scanning device

• pos: the physical coordinate of the scanning device

• degree: orientation of the user carrying the scanning device in degrees

• MAC _i : MAC address of a responding peer i (e.g., an AP or a device in ad hoc mode) with the corresponding values for signal strength in decibels per milliwatt, the channel frequency, and its mode (access point = 3, device in ad hoc mode = 1).

For keeping consistency throughout the paper, we did not discriminate between the cases of whether the responding peer is an access point or a device in ad hoc mode, i.e., mode 1 or mode 3. Thus, we refer to each responding peers as an access point, leading to an operational area covered in total by 17 APs (the total number of unique MAC addresses).

To obtain the orientation of the user, the Silicon Laboratories C8051F350 Digital Compass Reference Design Board was used. This device provides a USB-to-Serial bridge to access the data and is powered by the USB electricity supply. The compass in the middle of the operation area was calibrated. In a closer area around the calibration point, a variation of 1° was measured. However, variations up to 23° were rarely detected at a few points of the testing area. These measurement errors occurred always close to electromagnetic objects such as high-voltage power lines and electronic devices.

1.3 A.3 Data collection

A grid of reference points was applied to the operation area including 166 points with a spacing of 1 m. During the offline phase, the signal strength was measured at these reference points for different orientations. At each reference point and for each orientation, 110 signal strength measurements were collected. This led to 146,080 measurements for the offline phase. Almost 10 h was spent to collect all the data. For the online phase, 60 coordinates and orientations were randomly selected, and at each one of them, 110 signal strength measurements were sampled, leading to 6,600 measurements in total.