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At first glance, geolocation technology seems to work well. Our phones and cars get us to where we need to be with little effort and rather reliably. For consumers, it’s a free service, thanks to service providers like Google and Apple.


Peel back a layer and things are more complex. For one, even smartphone and in-vehicle navigation systems are not perfect. Ever try to start a route from inside a parking garage? While walking on a city sidewalk, has your map app located you on the wrong block, or perhaps inside a building? How’s your battery life when you’re running navigation?


These challenges multiply when faced with the demands of IoT applications. IoT use cases in smart city and smart factory deployments strain the current class of consumer solutions in areas including ubiquitous accuracy, power consumption and cost. Despite advances in satellite (GNSS) and terrestrial (4G, 5G, BLE and WiFi) geolocation technology, existing technology and solutions are often not up to the task.


At Nestwave, we’ve been innovating in an effort to move the needle. In Part 1 of this blog, we’ll be covering advances in a process that occurs deep inside a geolocation chipset: the signal processing used to determine the time of arrival of a wireless positioning signal.


The operation of satellite and many terrestrial geolocation systems begins with the transmission of wireless positioning signals between nodes of known location and an object of unknown location.

A time of arrival (TOA) is determined and converted into a distance from the transmitter, via velocity of the wave and clock synchronization. Multilateration is then used to calculate the position of an object based on the distance to the nodes of known location.


In a perfect world, a positioning signal would be transmitted at time t0 as a pure impulse or having a front rising edge of infinite vertical slope, propagate through a lossless, impairment-free medium and be received with no distortion. Establishing an accurate TOA would be rather straightforward.


In reality, signals are transmitted with finite slopes and are further bandwidth limited upon reception. They attenuate over distance, and critically, are distorted by atmospheric impairment, signal blockage, non-line-of-sight propagation and superimposed signal reflections due to multipath.


Take a quick glance at Figure 1. The red and blue traces show the received positioning signal transmitted from two antennas at a single location. When did each arrive? Maybe at the signal peak? Which peak? You’d be wrong. The green reference signal establishes “zero” on this Figure, showing the actual TOA (or in this case, the equivalent distance). It’s quite easy to see that ranging errors on the order of dozens of meters is possible in the presence of impairments such as multipath.

Figure 1: Real-world reception of positioning signals

Nestwave has developed a new approach to determining TOA using unique methods of signal filtering and estimation to improve accuracy in the presence of signal impairments and multipath.


Let’s start by looking at signal filtering. In conventional systems, received signals are often filtered using a symmetrical matched filter (MF). The impulse response of such filters is typically a sinc function. MF solutions can optimize data symbol detection in noisy/multipath environments where information is contained (and useful) in both direct and multipath signal components. However, for TOA estimation, we want to eliminate the energy contained in multipath components – not use it.


Nestwave has developed a near-causal filtering approach which enables better identification of the direct positioning signal path. In Figure 2 below, the Nestwave filter (blue) exhibits a taller, steeper rising filter edge as compared to a conventional sinc (yellow). When presented a signal with large multipath energy, the Nestwave filter results in a “cleaner” and more distinct signal rising edge.

Figure 2: Nestwave near-causal filter vs typical matched filter


Once we have a filtered signal, we need to estimate the TOA. Conventional techniques are either inadequate in dense multipath and NLOS environments, or for the cases of maximum-likelihood (ML) and MUSIC algorithms, unreliable and impractical due to the intense processing requirements. Nestwave has developed several advanced, robust estimation solutions which result in near-ML optimality but with reduced complexity in dense multipath.


A key to our approach is to simplify how we treat the multipath components as determined by actual (or expected) channel conditions, e.g. a power-delay profile. We sort multipath components into two classes: (1) nuisance paths that require joint estimation and (2) paths that can be treated as colored noise. By reducing the number of paths (= direct path + nuisance paths) for estimation, this approach exponentially reduces the complexity of ML computation with little loss of accuracy.


Let’s see how this all comes together in practice. Nestwave ran field testing in downtown San Francisco, an environment well known for substantial multipath. Indoor and outdoor locations were sampled according to the paths shown in Figure 3 below. In total, 7,000 sample points were collected across three hundred (300) 4G cells.


Figure 3: Indoor and outdoor test locations in San Francisco


We applied three different filtering methods to the received signals: (1) a conventional sinc-filter design (2) a Dolph-Chebyshev filter, known for high performance in the presence of multipath and (3) Nestwave’s near-causal filter design and estimation.


As can be seen in the cumulative distribution function (CDF) shown in Figure 4, the Nestwave solution outperformed the other two solutions, often substantially. At the 80% mark, the Nestwave ranging error is one-half of Dolph-Chebyshev and less than one-seventh the error of a conventional sinc filter solution.


Figure 4: Cumulative distribution function of ranging error comparing Nestwave with conventional solutions

Putting it all together, better filtering and estimation techniques can be used to improve geolocation accuracy and reduce power consumption even when faced with real-world impairments such as dense multipath and NLOS conditions.


As IoT deployments grow and applications expand, these benefits will be critical for chipset and product manufacturers looking to provide the best performance and value to their customers.


Nestwave is proud to announce the publication of two papers in the 2020 Journal of the IEEE / Institute of Navigation (ION) Position, Location and Navigation Symposium (PLANS). Both technical papers describe novel advances in geolocation algorithms providing substantially increased performance and robustness for our geolocation solutions.

The first paper, an improved method for multilateration, reformulates the classic time difference of arrival problem into a globally valid, time-of-transmission problem, further taking into account clock bias and the effects of motion (Doppler). The second paper describes techniques to substantially improve location accuracy in the presence of severe multipath using unique, near-causal filters and reduced complexity Maximum Likelihood (ML) estimation.

Congratulations to the R&D team at Nestwave! And stay tuned, there’s more to come.

Links to the papers (fee required):

https://www.ion.org/publications/abstract.cfm?articleID=17376

https://www.ion.org/publications/abstract.cfm?articleID=17342

Updated: Mar 4

In the rapidly growing world of IoT, tracking the location of ‘things’ including equipment, assets, parcels and pets is a valuable capability. In fact, one might even consider geolocation to be one of the killer apps for IoT. But there is a use of geolocation that may be equally important yet not quite as obvious to the casual observer – the automated locating of stationary objects.


This seems counter intuitive at first – if something is not moving, why do I need to know and track its location?


Let’s explain using an example. Perhaps your city is rolling out a smart parking meter program – converting coin-based systems to convenient digital solutions. These meters often accept multiple forms of payment, allow customers to pay or top off a meter with a smartphone app, and help cities analyze parking trends by incorporating sensors for vehicle detection. Some even embed pollution sensors enabling a large-scale web of environmental monitoring.


Knowing the location of each parking meter is important. Location enables users to quickly find their cars using an app, allows cities to pinpoint short term and long-term capacity needs across city geography and is critical for service crews to quickly find meters when repairs or upgrades are needed.


Under the hood, this means that each datapoint sent by the meter needs to have an associated location - a geotag. Deeper still, this means that each communication module ID needs to be paired with the meter’s location during installation.


How is this done? A technician may take a location reading on a smartphone or GPS device and record it in a spreadsheet or enter it on an electronic form. Perhaps they simply record the communication module ID and the corresponding meter pole ID on a sheet of paper. Later that module ID is correlated with a physical location based on a pre-existing database, spreadsheet or paper document.



Sounds like this could quickly get complicated, right? It gets worse.


In each scenario, there is a person involved in the process and one or more manual operations. As we know, people make mistakes. Numbers are transposed. Tracking sheets are lost. Excel spreadsheets are overwritten. Over time, this problem grows. What happens when the smart module is replaced? Will the repair crew be as well trained or meticulous as the installation crew? Will the updates propagate all the way through the system?


The solution is to employ a simple, automated means to accurately assess geolocation during install and at any time in the future, directly on the device. Having geolocation “ground truth” simplifies deployment and virtually eliminates the risk of faulty object geotags. Money is saved by automating manual processes. Equally, since reliability and accuracy of the services are improved, the ultimate value of the solution is increased.


We used smart parking meters as an example, but this can apply to many other IoT use cases: streetlights, traffic lights and vacancy sensors in parking garages. It works equally well for fixed enterprise and industrial assets including buildings, HVAC units, tanks, pipelines, storage containers, industrial machinery and surveillance cameras.


To extract the most value from this automation, it’s critical to use the right geolocation technology. An embedded GNSS chip or module designed for a smartphone is unsuited for IoT as the performance requirements and capability constraints are substantially different.


For one, IoT solutions are often battery powered with some devices needing to last 5-10 years or longer on a simple coin cell. Ultra-low power operation is the key here. Solutions must be extremely small, very inexpensive and connectivity is often intentionally intermittent to further save power.


Back to our example – single space smart parking meters incorporate rechargeable batteries. Despite being opportunistically recharged via small integrated solar cells, power consumption remains a critical problem. A Sacramento, CA audit of smart parking meters revealed that 17% of meters suffered low battery problems which led, in part, to consumer frustration with the new technology.


Thankfully, a new breed of geolocation solutions is designed specifically with these constraints in mind. Here at Nestwave, we have GNSS solutions that can reduce acquisition time by a factor of 10. By reducing the amount of time receiving circuitry is powered, we can gain a substantial reduction in power consumption per location fix. Additionally, our architecture can offload the most processing intensive operations to the cloud, further saving power. Beyond GNSS, Nestwave has solutions enabling geolocation via 4G/5G signal mutlilateration (via time difference of arrival or TDOA) and WiFi signal sniffing. Hybrid approaches like these enable extremely low-cost and low-power solutions as the existing wireless communication hardware is re-used for geolocation purposes.


These new technologies are key enablers to the emergence of purpose-built geotagging solutions needed for billions of IoT devices in the coming years. And if we do a good job, we might just end the use of clipboards and spreadsheets to track stationary objects.

© Nestwave SAS 2020