Ultra-Low-Power, High-Accuracy Location for Wearable GNSS Devices: From Host-Based to On-Chip Photo: Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit As location penetrates smaller and smaller devices that lack memory and computation power, GNSS chips must reacquire the standalone capability that they shed when first going to small form factors such as phones. A new chip with a new architecture demonstrates navigation and tracking and avoids burdening its main processor with heavy software. By Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit End users first experienced the amazing capabilities of GPS 12 years ago with early mass-market GPS devices. The focus was on navigation applications with specific tracking devices like personal navigation devices and personal digital assistants (PNDs, PDAs). With the advent of smartphones, GPS became a must-have feature. Other constellations were added to improve performance: GLONASS, QZSS, SBAS, and very recently, BeiDou. In the current phase, the focus is shifting to fitness applications and background location. This is not an insignificant change. Always-on connected applications, high-resolution displays, and other such features do not improve battery life. This article describes new ultra-low-power, high-accuracy location solutions for wearables’ power consumption. Impact of Always-On Connected Applications New applications require frequent GNSS updates with regard to user position. Sometimes the application will be open and other times it will not. The chips need to keep working in the background, buffering information and taking predefined actions. The GNSS chips need to be able to cope with these new requirements in a smart way, so that battery life is not impacted. Saving power is now the name of the game. Furthermore, GNSS is penetrating small devices: the Internet of Things (IoT) and wearables. They do not have the luxury of large resources (memory, computation power) as smartphones do. GNSS chips cannot leverage the resources in those devices; they need to be as standalone as possible. In summary, the new scenario demands chips that: do not load device’s main processor with heavy software; use less power while maintaining accuracy; can be flexibly configured for non-navigation applications. New GNSS Chip Architectures The industry is designing chips to meet these requirements by including the following features: measurement engine (ME) and positioning engine (PE) hosted on the chip; accelerometer and other sensors directly managed by the chip; new flexible configurations, duty cycling intervals, GNSS measurement intervals, batching, and so on. These features require hardware and software architectural changes. The new chips need more RAM than that required for smartphones, as they must now host the ME and PE. Wearables and IoT devices are small, cheap, and power-efficient. They do not have large processors and spare memory to run large software drivers for the GNSS chip. In many cases, the device’s microcontroller unit (MCU) is designed to go into sleep mode if not required, that is, during background applications. Therefore, new GNSS chips with more RAM are much better adapted to this new scenario. New chips must tightly integrate with sensors. The accelerometer provides extremely valuable information for the position update. It can detect motion, steps, motion patterns, gestures, and more. However, as a general rule, the MCU’s involvement in positioning should be minimized to reduce power consumption. For power efficiency, the new GNSS chips must interface directly with the sensors and host the sensor drivers and the sensor software. Finally, new chips must adapt to different human activities as they are integrated into wearable devices. This is the opposite approach from past developments where GNSS development was focused on one use case: car navigation. Now they must adapt to walking, running, cycling, trekking, swimming, and so on. All these activities have their particularities that can determine different modes in which new GNSS chips can work. Electronics must now conform to humans instead of the other way around. New wearable-chip GNSS tracking strategies include dynamic duty cycling and buffering, which contribute to the goal of reducing power consumption without compromising accuracy. Satellite positioning embedded in devices over the last few years first saw on-chip positioning before the era of smartphones, where you had dedicated SoCs that supported the silicon used to compute the GNSS fix. These expensive chips had lots of processing power and lots of memory. Once GNSS started to be integrated into cellphones, these expensive chips did not make sense. GNSS processing could be offloaded from the expensive SoCs, and part of the GNSS processing was moved onto the smartphone application processor directly. Since navigation is a foreground type of application, the host-based model was, and is still, a very good fit. But with advances in wearable devices, on-chip positioning will become the new architecture. This is because the host processor is small with very limited resources on wearables; and because energy must be minimized in wearables, reducing the processor involvement when computing GNSS fixes is critical. Some vendors are taking old stand-alone chips designed for PNDs and repurposing them for wearable devices. This approach is not efficient, as these chips are large, expensive, and use a lot of power. GNSS Accuracy While the new fitness and background applications in wearables have forced changes in GNSS chips’ hardware and software architectures, GNSS accuracy cannot be compromised. Customers are used to the accuracy of GNSS; there’s no going backwards in performance in exchange for lower power consumption. Figure 1. Software architecture for wearables. A series of tests shown here demonstrate how a new wearable, ultra-low-power GNSS chip produces a comparable GNSS track to existing devices using repurposed full-power sportwatch chips, while using only a fraction of the power. Speed Accuracy. Not only does the ultra-low-power solution produce a comparable GNSS track, it actually outperforms existing solutions when it comes to speed and distance, thanks to close integration with sensors and dynamic power saving features (Figures 2 and 3). Figure 2. Ultra-low-power versus full power. Figure 3. Full-power sportwatch, left, and ultra-low power chip, right, in more accuracy testing. GNSS Reacquisition. GNSS-only wearable devices face a design challenge: to provide complete coverage and to avoid outliers. This is seen most clearly when the user runs or walks under an overpass (Figure 4). Familiar to urban joggers everywhere, the underpass allows the user to cross a busy road without needing to check for traffic, but requires the GNSS to reacquire the signals on the tunnel exit. See the GNSS track in Figure 5: when the device reacquires the signals, the position and speed accuracy suffers. Figure 4. Position accuracy on reacquisition, emerging from overpass. Figure 5. GNSS speed accuracy on reacquisition. Using the filtered GNSS and sensors, however (Figure 6), enables smooth tracking of speed and distance through the disturbance. Figure 6. Sensors provide smooth speed estimate. Urban Multipath. The pace analysis in Figure 7 shows a user instructed to run at a constant 8-minute/mile pace, stopping to cross the street where necessary. The red line on each plot shows the true pace profile. The commercial GNSS-only sportwatch on top shows frequent multipath artifacts, missing some of the stops and, worse for a runner, incorrectly showing erroneously high pace. The ultra-low-power chip captures all the stops and shows a constant running pace when not stopped. Figure 7. Urban multipath tests. It is well known in the community that regular sportwatches give unreliable speed and distance estimates in urban environments — where most organized running races are held! There’s nothing worse, as a runner, than to hear the distance beep from your watch going off earlier than expected: how demoralizing! The major benefit of this solution is that the speed estimate is much more reliable in the presence of multipath. At the same time, battery life can be extended because the GNSS is configured to use significantly less power. fSpeed in existing solutions is computed in two different ways: indirectly from two consecutive, time-stamped GNSS position estimates, each derived from range measurements to the satellites, and directly from the Doppler frequency offset measurements to the satellites. Both range and frequency measurements are subject to significant error when the direct path to the satellite is blocked and a reflection is acquired. The effects of multipath mean that the range error may in typical urban environments be hundreds of meters. The frequency error is also a function of the local geometry and is typically constrained by the magnitude of the user’s horizontal speed. In either case, the GNSS device alone, in the presence of signal multipath, generates a velocity vector that fluctuates significantly, especially when there is a change in the satellites used or signal propagation path between the two consecutive positions. A variety of real-life cases generate this sudden fluctuation in velocity vector: Running along a street in an urban canyon and turning a 90-degree corner. Running along a pedestrian lane and taking a short road underpass. Running under tree cover and suddenly arriving at an open area. Running under an elevated highway and turning 90 degrees to a wide-open area. In each case, the chips are using a certain set of satellites, and suddenly other, higher signal-strength satellites become available. A typical situation is for the position to be lagging the true position (while under tree cover, going through an underpass) and needing to catch up with the true position when arriving to the wide-open area. A jump in position is inevitable in that situation. This is not too bad for the GNSS track, but it will mean a noticeable peak in the speed values that is not accurate. Fitness applications save all of the computed speed values and generate a report for each workout. These reports are not accurate, especially the maximum speed values, for the reasons explained above. Figure 8 describes a typical situation where the actual speed of the runner is approximately constant. GNSS fixes are computed regularly; however, the speed computed from subsequent GNSS fixes have sudden peaks that spoil the workout speed reports. Figure 8. Sudden peaks spoil workout speed reports. The new ultra-low-power solutions for wearables solve this problem by deriving speed and accumulated distance from the sensors running in the device. This avoids incorrect speed peaks, while still being responsive to true pace changes by the runner. In running biomechanics, runners increase pace by increasing step cadence and/or increasing step length. Both methods depend on the runner’s training condition, technique, biomechanics, and so on. As a general rule, both step cadence and step length increase as the running speed increases from a jogging speed to a 1,500-meter race speed. A runner may use one mechanism more than the other, depending on the moment or on the slope (uphill or downhill). In the case of male runners, the ratio of step length to height at a jogging speed is ~60 percent.The ratio of step length to height in a 1,500 meter race speed is ~100 percent. For female runners, the respective ratios are ~55 percent and ~90 percent. The ultra-low-power chips take into account both mechanisms to derive the speed values. The sensor algorithms count the number of steps every time interval and translates the number of steps into distance multiplying by the step length. The reaction time of the GNSS chip to speed changes based on a higher cadence is immediate. Speed changes due to longer steps are also measured by the ultra-low-power chips. The step length is constantly calibrated by the GNSS fixes when the estimated GNSS position error is low. The reaction time of the GNSS chip to speed changes based on longer steps has some delay, as it depends on the estimated error of the GNSS fixes. Manufacturer The ultra-low-power, high-accuracy, 40-nanometer single-die BCM4771 chip was designed by Broadcom Corporation. It is now being manufactured in production volumes and is focused on the wearables and IoT markets.It consumes five times less power than conventional GNSS chips (~10 mW) and needs 30 KBytes of memory in the MCU for the software driver. It features tight integration with the accelerometer and innovative GNSS tracking techniques for extremely accurate speed, accumulated distance, and GNSS tracking data. Steve Malkos is an associate director of program management in the GPS Business Unit at Broadcom, responsible for defining GPS sensor hub and indoor positioning features. He has a B.S. in computer science from Purdue University, and currently holds eight patents,10 more pending, in location. Manuel del Castillo is an associate director of marketing for Broadcom in the GNSS group. He has an MS in electronic engineering from the Polytechnic Universityand an MBA from the Instituto de Empresa, both in Madrid, Spain. He holds three patents in location with five more pending. Steve Mole is a manager of software engineering for Broadcom in the GNSS group. He received his bachelor’s degree in physics and astrophysics from the University of Manchester.
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Condor dsa-0151d-12 ac adapter 12v dc 1.5a switching power suppl,technology private limited - offering jammer free device,mobile jammers effect can vary widely based on factors such as proximity to towers,the paralysis radius varies between 2 meters minimum to 30 meters in case of weak base station signals,bs-032b ac/dc adapter 5v 200ma used 1 x 4 x 12.6 mm straight rou,thermo gastech 49-2163 ac adapter 12.6vdc 220/70ma battery charg,mb132-075040 ac adapter 7.5vdc 400ma used molex 2 pin direct plu,jhs-e02ab02-w08a ac adapter 5v 12vdc 2a used 6pin din power supp,71109-r ac adapter 24v dc 350ma power supply tv converter used,the jammer transmits radio signals at specific frequencies to prevent the operation of cellular phones in a non-destructive way.chd scp0500500p ac adapter 5vdc 500ma used -(+)- 0.5 x 2.4 x 9 m,finecom pa-1121 ac adapter 19vdc 6.32a 2.5x5.5mm -(+) 120w power.motorola psm4250a ac adapter 4.4vdc 1.5a used cellphone charger,cgsw-1201200 ac dc adapter12v 2a used -(+) 2x5.5 round barrel,all these project ideas would give good knowledge on how to do the projects in the final year.jobmate ad35-04503 ac adapter 4.5vdc 300ma new 2.5x5.3x9.7mm,41-9-450d ac adapter 12vdc 500ma used -(+) 2x5.5x10mm round barr,incoming calls are blocked as if the mobile phone were off.sagemcom nbs24120200vu ac adapter 12vdc 2a used -(+) 2.5x5.5mm 9.anam ap1211-uv ac adapter 15vdc 800ma power supply,worx c1817a005 powerstation class 2 battery charger 18v used 120,bc-826 ac dc adapter 6v 140ma power supply direct plug in.brother ad-20 ac adapter 6vdc 1.2a used -(+) 2x5.5x9.8mm round b,replacement pa-1700-02 ac adapter 19v 3.42a used.creative ys-1015-e12 12v 1.25a switching power supply ac adapter,liteon pa-1900-03 ac adapter used -(+) 19vdc 4.74a 2.5x5.5mm 90°.biogenik s12a02-050a200-06 ac adapter 5vdc 2a used -(+) 1.5x4x9m,pa-1700-02 replacement ac adapter 18.5v dc 3.5a laptop power sup,replacement sadp-65kb d ac adapter 19v 3.42a used 1.8x5.4x12mm 9,providing a continuously variable rf output power adjustment with digital readout in order to customise its deployment and suit specific requirements,viper pa1801 1 hour battery charger 20.5vdc 1.4a charging base c.three circuits were shown here.nec pc-20-70 ultralite 286v ac dc adaoter 17v 11v power supply. 90 % of all systems available on the market to perform this on your own,oem ads0202-u150150 ac adapter 15vdc 1.5a used -(+) 1.7x4.8mm,and cell phones are even more ubiquitous in europe.2100 to 2200 mhzoutput power,our pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,fisher-price na090x010u ac adapter 9vdc 100ma used 1.5x5.3mm,mini handheld mobile phone and gps signal jammer,toshiba adp-60fb 19vdc 3.42a gateway laptop power supply,recoton ad300 ac adapter universal power supply.livewire simulator package was used for some simulation tasks each passive component was tested and value verified with respect to circuit diagram and available datasheet,ac-5 48-9-850 ac adapter dc 9v 850mapower supply,although we must be aware of the fact that now a days lot of mobile phones which can easily negotiate the jammers effect are available and therefore advanced measures should be taken to jam such type of devices.mw mw1085vg ac adapter 10vdc 850ma new +(-)2x5.5x9mm round ba,casio ad-c59200j ac adapter 5.9v dc 2a charger power supply,igloo osp-a6012 (ig) 40025 ac adapter 12vdc 5a kool mate 36 used,a software solution dedicated to post processing static and kinematic gnss raw data,you can clearly observe the data by displaying the screen,macvision fj-t22-1202000v ac adapter 12vdc 2000ma used 1.5 x 4 x.compaq 2822 series ac adapter 18.5v 2.2a 30w power supply 91-470,konka ktc-08bim5g 5vdc 500ma used travel charger,at every frequency band the user can select the required output power between 3 and 1,delta adp-65jh db ac adapter 19vdc 3.42a used 1.5x5.5mm 90°rou.we use 100% imported italian fabrics,rova dsc-6pfa-12 fus 090060 ac adapter +9vdc 0.6a used power sup.theatres and any other public places.uses a more efficient sound with articulation similar to speech,atlinks 5-2418a ac adapter 9vac 400ma ~(~) 2x5.5mm 90° used 120v,wowson wde-101cdc ac adapter 12vdc 0.8a used -(+)- 2.5 x 5.4 x 9.radioshack 23-240b ac adapter 9.6vdc 60ma used 2-pin connector,motorola 2580955z02 ac adapter 12vdc 200ma used -c+ center +ve -.ad35-03006 ac adapter 3vdc 200ma 22w i t e power supply.a&d tb-233 ac adapter 6v dc 500ma used -(+) 2x5.5mm barrel 120va.sii psa-30u-050 ac adapter 5v 4a slp2000 sii smart label printer. Hp ppp012s-s ac adapter 19v dc 4.74a used 5x7.3x12.6mm straight,here is the diy project showing speed control of the dc motor system using pwm through a pc,li shin lse0202c1990 ac adapter 19vdc 4.74a used -(+) screw wire,ac adapter 12vdc output 3pin power supply used working for lapto.replacement ac adapter 15dc 5a 3x6.5mm fo acbel api4ad20 toshiba,upon activation of the mobile jammer,this project shows the automatic load-shedding process using a microcontroller,pride battery maximizer a24050-2 battery charger 24vdc 5a 3pin x,it consists of an rf transmitter and receiver.bellsouth dv-1250 ac adapter 12vdc 500ma power supply.sunforce 11-1894-0 solar battery charger 12v 1 watt motorcycle,bti ac adapter used 3 x 6.3 x 10.6 mm straight round barrel batt,larger areas or elongated sites will be covered by multiple devices,sino american sa106c-12 12v dc 0.5a -(+)- 2.5x5.5mm switch mode.logitech l-ld4 kwt08a00jn0661 ac adapter 8vdc 500ma used 0.9x3.4,toshiba pa3378e-3ac3 ac adapter15vdc 5a -(+) 3x6.5mm used round,practical peripherals dv-8135a ac adapter 8.5vac 1.35amp 2.3x5mm.kodak k5000 li-ion battery charger4.2vdc 650ma for klic-5000 kli,chd dpx351314 ac adapter 6vdc 300ma used 2.5x5.5x10mm -(+),radio shack 23-243 ac dc adapter 12v 0.6a switching power supply,airspan pwa-024060g ac adapter 6v dc 4a charger,371415-11 ac adapter 13vdc 260ma used -(+) 2x5.5mm 120vac 90° de.this paper describes the simulation model of a three-phase induction motor using matlab simulink,ryobi p113 class 2 battery charger 18v one+ lithium-ion batterie,chi ch-1265 ac adapter 12v 6.5a lcd monitor power supply,we would shield the used means of communication from the jamming range.powerbox ma15-120 ac adapter 12vdc 1.25a -(+) used 2.5x5.5mm.symbol 59915-00-00 ac adapter 15vdc 500ma used -(+)- 2 x 5.4 x 1.a mobile phone jammer or blocker is a device which deliberately transmits signals on the same radio frequencies as mobile phones,fujitsu adp-80nb a ac adapter 19vdc 4.22a used -(+) 2.5x5.5mm c.li shin international enterprise 0322b1224 ac adapter 12vdc 2a u.motorola fmp5358a ac adapter 5v 850ma power supply,computer wise dv-1280-3 ac adapter 12v dc 1000ma class 2 transfo. 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