Range Estimation Phase

The techniques to measure distance and/or angle information comes under the range estimation phase and are the output of the measurement layer as defined by the location stack [55]. Range based localization schemes rely on the availability of range estimation. The precision of such estimation, however, is the focus to the transmission medium and surrounding environment. The commonly considered ranging techniques are:

2.3.1.1 Angle of Arrival (AoA)

The AoA is a method to measure the angle at which an incoming signal arrives at the receiver (anchor node), hence its measures the angle between two nodes.

There are a couple of ways that sensors measure AoA. One category is phase interferometry, where an angle is estimated by phase differences in the signal received by two or more individual sensors (microphones for acoustic signals or antennas for RF signals) [57, 58]. Another category is based on the varying signal strength, where AoA estimation uses the RSS ratio between two (or more) directional antennas located on the sensor [59, 60]. Two directional antennas pointed in different directions, such that their main beams overlap, can be used to estimate the AoA from the ratio of their individual RSS values [59].

2.3.1.2 Complexity and Error Concerns using AoA

• The accuracy of AoA measurements is limited by the directivity of the an- tenna, by shadowing and by multipath reflections. A multipath component may appear as a signal arriving from an entirely different direction and can lead to very large errors in AoA measurements [14, 15, 59].

• The AoA is not a favourable localization approach for low cost IEEE Zigbee transceivers as use of directional antenna arrays increases the system cost and complexity. Furthermore, angle estimation improves at the cost of additional antennas.

2.3.1.3 Time Difference of Arrival (TDoA)

Conventionally, one-way ToF range measurement requires highly synchronized clock among the subject and anchors. To overcome this synchronization prob- lem, TDoA was proposed. TDoA technique can be implemented in two different possible modes, uplink and downlink [55]. In the uplink mode, subject nodes broadcast a signal, which arrives at multiple measuring anchor nodes. This differ- ence in the arrival of time can be treated as a hyperbola, which has two receiving anchors at its focii. Three anchor nodes are required in 2-D positioning. The target node is located at the intersection of two hyperbolas. An alternative mode downlink, where the anchors broadcast the signal simultaneously while the sub- ject node receives it with different delays. In both cases, the anchor clocks should be accurately synchronized which are often wired to guarantee synchronization. The synchronization of the subject node and anchors in this case is however not

mandatory. In TDoA location estimation is the intersection of all hyperbolas (hy- perboloids in 3-D). Unlike ToF where localization is the intersection of all circles (spheres in 3-D). This method is also known as hyperbolic localization method. Some famous TDoA based systems are Cricket (RF and ultrasound) [49], Active Bat (RF and ultrasound) [48].

Complexity and Error Concerns using TDoA

• It requires highly synchronized clocks at each of the anchor nodes as preci- sion of the location engine is directly proportional to the clock accuracy. • Similar to AoA and ToA, TDoA is also affected by strong multipath com-

ponents, which results in inaccurate range estimation (i.e. intersection of hyperbolas).

2.3.1.4 Time of Flight (ToF)

In ToF ranging, measurements based on propagation time are used to estimate the distance between neighbouring devices. ToF is classified as either one-way propagation time or two-way propagation time measurement based on the num- ber of packet transmission for range estimation. One-way ToF is less attractive in WSNs due to size and cost of precise clocks for synchronization between trans- mitter and receiver. In one-way, the node A transmits the time-stamped signal at t1 and is received at node B at t2, the distance between the nodes is given by

the equation d = c × ToF2 − ToF1. As compared to one-way ToF, where two

highly synchronized clocks are needed, in two-way ToF the same clock is used to calculate the round-trip time [16, 17]. Consequently synchronization between different clocks is not necessary. ToF is further discussed in chapter 3.

2.3.1.5 Received Signal Strength (RSS)

The Received Signal Strength Indicator (RSSI) of a radio channel provides a feasible way of estimating distance between sensor nodes. It is preferred to use this distance measurement technique because the sensor nodes do not require any additional hardware but only a radio transceiver. Both medium characteristics

and node hardware consistency influences RSS measurement results. Existing distance-estimation based techniques for localization rely on a log-normal radio propagation model [61] to estimate inter-sensor distances from RSS measure- ments. The path loss exponent (η) is a key parameter in the log-normal model which characterizes the transmission media and accurate knowledge of this fac- tor is required in order to obtain an accurate estimate from RSS measurements. Hence to reduce the ranging error for localization, a calibration method (aka fingerprinting) to map the channel information (i.e. η, shadowing variance, fre- quency selective fading) has been considered to model an appropriate path loss model [11, 12, 61].

Most of the previous work is limited to 2-D, and in addition to that optimal anchor placement is not considered in order to calibrate the channel parameters [12, 61–63]. However, in practical systems, these calibrated channel parame- ters may become impractical due to the nuisance in the channel such as, the background noise and some other environmental factors, such as temperature, humidity, weather conditions and obstacles to the transmission. In addition, the hardware device characteristics include the wireless communication part (the node transmitting power, receiver sensitivity) and antenna (antenna directivity and antenna gains) [64]. It is therefore, in addition to the prior knowledge of channel parameters, knowledge of a confined area (indoor environment) can be utilized to enhance the RSS based localization. RSS is further experimentally analysed in chapter 3, and a proposed scheme for indoor localization is discussed in chapter 8.

Due to the complexity and error concerns posed by AoA and TDoA, ToF and RSS are mainly focused (chapter 3). Round-trip ToF overcomes the ma- jor problem of synchronization, faced by the one-way ToF, hence it reduces the complexity and system cost as compared to TDoA and AoA. Whereas, received signal strength (RSS) is one of the standard parameters available on most of the wireless devices.