Figure 2.2: The wireless geolocation system.
The Figure 2.2 illustrates the functional block diagram of a wireless ge-olocation system [11]. The main elements of the system are a number of location sensing devices that measure metrics related to the relative position of a mobile terminal (MT) with respect to a known reference point (RP), a positioning algo-rithm that processes metrics reported by location sensing elements to estimate
the location coordinates of MT, and a display system that illustrates the location of the MT to users.
2.2.1 Sensing Techniques
The signal metrics measured by sensor are determined by location-sensing techniques. These datum will affect the accuracy of location estimation by posi-tioning algorithms. In the following, there are several sensing techniques in com-mon use.
1. Angle of Arrival ( AOA ) [12]:
Angle of arrival is the common metric used in direction-based systems.
This approach requires the installation of complex antenna array at mobile terminal (MT). According to the arrival signal received by the two receiver stations can determine the angle between the MT and each of receiver sta-tions. After getting these computed AOAs, we can find several lines pro-jected from the receiver stations to where the signal originated at the angles as measured. The estimated position is the intersection point.
2. Time of Arrival (TOA) [6]:
In the standard of IEEE 802.11b, the signal is coded by a known pseudo-noise (PN) and transmitted by a transmitter. Then a receiver cross-correlates received signal with locally generated PN sequence using a sliding correlator.
The distance between the transmitter and receiver is determined from the arrival time of the first correlation peak. However, due to the complexity of multipath indoor radio propagation channels, the resolution of TOA estima-tion is roughly determined by the base width of the PN correlaestima-tion funcestima-tion,
or equivalently the signal bandwidth.
3. Received Signal Strength (RSS) [10]:
This technique is based on the received signal energy of antenna of devices.
The four units of measurement to represent RF signal strength are mW, dBm, RSSI (Receive Signal Strength Indicator), and percentage. The RSS metric can be used to determine the distance between a transmitter and a receiver with the radio propagation model because the signal decay with increment of distance square. Then we use triangulation approach to locate the receiver.
The other approach using RSSs to perform the location-determination is lo-cation fingerprinting algorithm. It is based on that the received signals at different locations have different characteristic of the signal. The characteris-tic signal means that RSSs from different fixed transmitters, like access point (AP), is different. This is known as the location fingerprinting.
2.2.2 Positioning Algorithm
The measurement accuracy of location metrics in indoor areas depends on location sensing technologies and radio propagation conditions. Due to the im-perfect sensing techniques and the multipath radio propagation problem, the mea-surements of location metrics always contains varying errors. To achieve high positional accuracy when the measurements of location metrics are unreliable, the errors have to be mitigated in the positioning process. Therefore, we discuss two types of positioning technique [11], the traditional techniques and pattern recog-nition techniques.
1. Traditional Techniques
In the indoor environment, it is difficult to measure AOA and RSS ac-curately, so most of the independent indoor positioning systems mainly use TOA based techniques. With reliable TOA-based distance measurements, simple geometrical triangulation method can be used to find the location of the MT. Due to the estimation errors of distances at receivers caused by inaccurate TOA measurement, the geometrical triangulation technique can only provide a region of uncertainty, instead of a single fixed position, for estimated location of the MT. The Figure 2.3 illustrates the geometrical triangulation technique and the gray region is the estimate location of MT.
Figure 2.3: The geometrical triangulation technique.
2. Pattern Recognition Techniques
The small coverage of the system makes it possible to conduct extensive pre-measurement in the areas of interest. As a result, the pre-measurement based location pattern recognition (also called location fingerprinting)
tech-nique is practicable for indoor applications. The basic operation of pattern recognition positioning algorithms is simple. Due to the unique radio sig-nal propagation characteristics in each indoors environment, each spot in a building would have a unique signature in terms of RSS, TOA, and/or AOA, observed from different sensors in the building. This unique signature also calls as ”location fingerprint”. A pattern recognition system determines the unique pattern features (i.e., the location fingerprint) of the area of inter-est in a training process, and then this knowledge is used for the rules of recognition. The challenge for such algorithms is to distinguish locations with similar signatures. To build the signature database, a terminal is used to gather signals through all location sensing elements in the interest area which is divided into nonoverlapping zones. Then we have to analyzes the gathered signal patterns and compiles a unique signature for each zone. The location estimation is determined to be the one associated with the minimum Euclidean distance [9]. Therefore, the measurement of Euclidean distance is computed for all the measured location fingerprint and all entries in the sig-nature database. Usually we choose the physical location of the entry associ-ated with the minimum Euclidean distance. Since general WLAN PC cards can more easily get RSS information than AOA and TOA, it is considerable to use the location fingerprinting approach based on RSS.