Indoor positioning is one of the core technologies behind the idea of Internet of Things (IoT). Some of the use cases are asset tracking and management, factory automation systems, virtual and augmented reality applications, social media relevance and precision marketing in shopping malls. Distances between wireless devices can be determined through various ranging techniques that were introduced in the big picture of localization. Among the candidates, phase based ranging is a low-cost and accurate method that can be implemented on cheap hardware and deployed in real scenarios with relative ease (even in the absence of synchronization among nodes). In this article, the phase slope method will be explained in detail. Background The bane of almost all indoor localization techniques is

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## Maximum Likelihood Estimation of Clock Offset

When I started my PhD, one of the first papers I read was On Maximum Likelihood Estimation of Clock Offset by Daniel Jeske [1] from University of California, Riverside. It eventually set the direction of my future research and ultimately my PhD dissertation. I found this paper quite interesting as it talked about the estimation of clock phase offset. Later I went on to explore what was missing here (the clock frequency offset) and more. Keep in mind that carrier phase estimation is a different problem that has already been discussed in the past here, here and here. Most of the solutions involve a Phase Locked Loop (PLL) from a software defined radio perspective. In this article, I summarize the

Continue reading## The Big Picture of Localization

Digital Signal Processing (DSP) enables us to find the range of a device transmitting a wireless signal with a particular structure under some conditions. To understand how this process works, we need to look at the big picture of a localization process. Localization implies locating the unknown position of a source which can be computed in a straightforward manner if its ranges from some reference nodes can be found. Various techniques are employed for this purpose, some of which are Received Signal Strength Indicator (RSSI), time of arrival, time difference of arrival and angle of arrival. Phase of arrival is a special case of time of arrival scheme which is extremely accurate due to the high frequency carrier resembling a

Continue reading## There and Back Again: Time of Flight Ranging between Two Wireless Nodes

With the growth in the Internet of Things (IoT) products, the number of applications requiring an estimate of range between two wireless nodes in indoor channels is growing very quickly as well. Therefore, localization is becoming a red hot market today and will remain so in the coming years. See the big picture of localization for general solutions to this problem. One question that is perplexing is that many companies now a days are offering cm level accurate solutions using RF signals. The conventional wireless nodes usually implement synchronization techniques which can provide around $\mu s$ level accuracy and if they try to find the range through timestamps, the estimate would be off by $$1 \mu s \times 3 \times

Continue reading## Location Estimation through Differential Phase Difference of Arrival

In an article on carrier phase based ranging, we saw how phase observations were employed to find the range between two wireless devices. Today we explore how phase can also be used for the purpose of location estimation. Background To determine the position of a wireless device, its range needs to be computed from a set of anchor nodes. When these anchors and the device itself are synchronized with each other, the signal propagation time of an electromagnetic wave arriving at these anchors after its emission from a Tx can be employed to calculate the corresponding distances. This is the well known time of arrival problem. With an unsynchronized wireless device, time difference of arrival utilizes the difference in signals

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