Global Positioning System (GPS) location accuracy improvement due to Selective Availability removal

Christophe Adrados; Irène Girard; Jean-Paul Gendner; Georges Janeau

doi:10.1016/S1631-0691(02)01414-2

Global Positioning System (GPS) location accuracy improvement due to Selective Availability removal
[Amélioration de lˈexactitude des localisations GPS liée au retrait de la SA.]

Présenté par : Yvon Le Maho

Christophe Adrados ¹ ; Irène Girard ² ; Jean-Paul Gendner ³ ; Georges Janeau ¹

¹ Institut de recherche sur les grands mammifères, Institut national de la recherche agronomique, BP 27, 31326 Castanet-Tolosan cedex, France
² Parc national de la Vanoise, BP 705, 73007 Chambéry cedex, France
³ Centre d’écologie et physiologie énergétiques, Centre national de la recherche scientifique, 23, rue Becquerel, 67087 Strasbourg cedex 2, France

Comptes Rendus. Biologies, Volume 325 (2002) no. 2, pp. 165-170.

Résumés

Anglais
Français

Global Positioning System (GPS) is an important new technology for spatio-temporal behaviour studies of animals. Differential correction improves location accuracy. Previously, it mostly removed partially the influence of Selective Availability (SA). SA was deactivated in May 2000. The aim of this study was to quantify the influence of SA cancellation on location accuracy of various GPS receivers. We tested the accuracy of locations obtained from non-differential and differential GPS animal collars before and after SA removal. We found a significant improvement in accuracy for both types of GPS collars. However, differential GPS still provides more accurate locations.

Le Global Positioning System (GPS) est une nouvelle technique d’étude du comportement spatio-temporel des animaux. Une correction différentielle permet d’améliorer l’exactitude des localisations. Avant la suppression en mai 2000 de la Selective Availability (SA), cˈest surtout lˈinfluence de celle-ci quˈelle supprimait partiellement. Le but de notre étude était de tester lˈexactitude des localisations obtenues avec des colliers GPS différentiels et non-différentiels, avec et sans SA. Nous avons constaté une augmentation significative de lˈexactitude des localisations depuis la suppression de la SA. Toutefois, le mode différentiel reste plus performant que le mode non-différentiel.

Métadonnées

Reçu le : 2001-08-20
Accepté le : 2001-11-21
Publié le : 2002-02-01

PMID

DOI : 10.1016/S1631-0691(02)01414-2

Keywords: differential correction, Global Positioning System (GPS), location accuracy, Selective Availability, Wildlife telemetry
Mots-clés : correction différentielle, Global Positioning System (GPS), exactitude, Selective Availability, faune sauvage, télémétrie

Affiliations des auteurs :

Christophe Adrados ¹ ; Irène Girard ² ; Jean-Paul Gendner ³ ; Georges Janeau ¹

¹ Institut de recherche sur les grands mammifères, Institut national de la recherche agronomique, BP 27, 31326 Castanet-Tolosan cedex, France
² Parc national de la Vanoise, BP 705, 73007 Chambéry cedex, France
³ Centre d’écologie et physiologie énergétiques, Centre national de la recherche scientifique, 23, rue Becquerel, 67087 Strasbourg cedex 2, France

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     author = {Christophe Adrados and Ir\`ene Girard and Jean-Paul Gendner and Georges Janeau},
     title = {Global {Positioning} {System} {(GPS)} location accuracy improvement due to {Selective} {Availability} removal},
     journal = {Comptes Rendus. Biologies},
     pages = {165--170},
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     number = {2},
     year = {2002},
     doi = {10.1016/S1631-0691(02)01414-2},
     language = {en},
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Christophe Adrados; Irène Girard; Jean-Paul Gendner; Georges Janeau. Global Positioning System (GPS) location accuracy improvement due to Selective Availability removal. Comptes Rendus. Biologies, Volume 325 (2002) no. 2, pp. 165-170. doi : 10.1016/S1631-0691(02)01414-2. https://comptes-rendus.academie-sciences.fr/biologies/articles/10.1016/S1631-0691(02)01414-2/

Version originale du texte intégral

Le texte intégral ci-dessous peut contenir quelques erreurs de conversion par rapport à la version officielle de l'article publié.

1 Introduction

Since the first application of Global Positioning System (GPS) technology to locate wild animals [1] there have been recurrent questions about location accuracy. One of the main sources of error was a random degradation intentionally imposed by the United States Department of Defence called Selective Availability (SA). To manage SA influence, and to achieve better accuracy, it was possible to use GPS animal collars in differential mode [1–7]. This method consists of storing measured distances to individual satellites (called pseudoranges) in the GPS collar, and correcting it in post-treatment with data obtained simultaneously by a pinpointed GPS reference base station. Locations are then accurately determined. Both differential and non-differential GPS collars are used in wildlife applications, the latter being cheaper and easier to use, but less accurate. SA was removed at the beginning of May 2000, and theoretically [2,6] it is possible to calculate the effect of this removal. However, we decided to test this impact on location accuracy of our GPS base station (as an example) and of non-differential and differential GPS collars in stationary positions. The goal of this paper is to help future wildlife GPS users to choose the most appropriate GPS mode to suit their scientific objectives.

2 Materials and methods

We studied the influence of SA elimination on location accuracy of three very different GPS receivers in stationary positions: a reference base station, non differential and differential GPS collars. Our goal was not to compare the GPS performance among devices but location accuracy before and after SA removal within each one. In fact it is almost impossible to directly compare the results obtained from the various GPS receivers because: (i) the reference base station was used under optimal conditions with an effective ground plane (a better ground plane improves antenna performances, particularly by increasing antenna gain); (ii) GPS collars are not designed to work without a ground plane (which is usually the body of the fitted animal); (iii) channel number is higher for the reference base station than for the collars (more satellites can be tracked simultaneously); (iv) the fix interval was different among devices, and it has an influence both on location success rate [5] and accuracy [6]; and (v) the GPS receivers were located in different places (with different topographic obstructions) and did not work simultaneously.

2.1 Reference base station

Fixes were obtained from a 12-channel GPS Pathfinder base station (Trimble Navigation Inc., USA, PFCBS software version 2.67) working in non-differential mode, one month before and one month after SA removal, with a fix interval of 5 s. The reference base station was located in south-west France (43°31’N, 01°30’E). Topographic obstruction was below the elevation mask threshold of 10° set in the base station software.

2.2 Non-differential GPS collars

Locations were collected from non-differential 8-channel GPS Simplex collars (TVP Positioning AB., Sweden), more than 4 months before and after SA removal, with a fix interval of 6 min. Collars were processed using Simpset software version 2.0 and data were downloaded via a wire link to a personal computer. The GPS collars were tested in south-east France (45°57’N, 05°90’E). Topographic obstructions were low to the North (7°), East (5°) and West (10°) but higher to the South (35°).

2.3 Differential GPS collars

Pseudoranges were recorded from differential 8-channel GPS_1000 collars (Lotek Engineering Inc., Canada, software version 2.17) more than 4 months before and after SA removal, with a fix interval of 5 min. We chose to test the GPS collars 420 km away from the reference base station; i.e., close to the 500-km limit given by Trimble instructions [2]. The area was located in a mountainous environment in south-east France (45°16’N, 05°58’E), with significant topographic obstructions to the North and West (N 43°, E 12°, S 10°, W 31°), thus potentially increasing multipath effects, as for non-differential collars, compared to reference base station. Data were downloaded via a radio modem command unit to a personal computer using Gpshost 1000 software version 3.08 [8]. The pseudoranges were differentially corrected using N4win software version 1.1895.

We converted all GPS locations from latitude–longitude to the French Lambert III coordinate system using GPS Pathfinder Office software version 2.7 (Trimble Navigation Ltd., Sunnyvale, USA). Locations were pooled into different classes according to fix status: 2- or 3-dimensional location (2D or 3D) and Dilution of Precision (DOP), both of them having an influence on location accuracy. 2D locations are less accurate than 3D locations, and DOP is a measure of the quality of satellite geometry: satellites that are close together will provide a lower accuracy and a higher DOP. We determined location error as the distance in metres between each observed location and an estimated true location. The true location was estimated as the geometric centre of the most accurate locations (3D locations with DOP < 5) obtained at each site. As location errors cannot be < 0, the variance is related to the mean. Error data was then transformed by natural logarithm before statistical analysis, as suggested by [3,4]. We tested the hypothesis that SA removal had an effect on location error within these different classes of GPS location, for both differential and non-differential GPS collars, using analysis of variance (ANOVA). Significance level of all tests was p = 0.05. We report exact p-values when p > 0.001. Statistical tests were conducted using SPSS for Windows (SPSS Science, Chicago, IL, USA).

3 Results

3.1 Reference base station

We analysed two sets of 4320 locations recorded by our reference base station before and after SA removal. All of these were 3D locations with DOP values lower than 5. Mean location error decreased significantly from 23.9 m with SA (SE = 0.2, max = 94.8) to 1.4 m without SA (SE = 0.01, max = 3.1) (F_1,8640 = 4.1 10 ⁴, p < 10^–3). Table 1 shows the influence of SA removal on location error distribution.

Location error (m)	Cumulative % of locations
	SA	no SA
n	4320	4320
0–5	4.1	100
5–10	15.6
10–15	30.0
15–20	46.3
20–30	74.8
30–40	87.7
40–50	93.5
50–100	100
mean	23.9	1.4
SE	0.2	0.01
max	94.8	3.1

3.2 Non-differential GPS collars

We recorded respectively 354 and 339 locations before and after SA removal with non-differential GPS collars. Mean location error decreased significantly from 78.0 m with SA (SE = 4.9, max = 776.9) to 11.9 m without SA (SE = 0.7, max = 160.1) (F_1,692 = 784.4, p < 10^–3). We collected very few locations with DOP ≥ 10 for 2D locations (n = 9 with SA and n = 4 without SA) and none for 3D locations, so we pooled data in 4 sets: DOP < 5 and DOP ≥ 5 for both 2D and 3D locations. For each data set, mean location error was significantly lower after SA removal (2D locations with DOP < 5: F_1,203 = 273.4, p < 10^–3; 2D locations with DOP ≥ 5: F_1,69 = 135.4, p < 10^–3; 3D locations with DOP < 5: F_1,326 = 470.6, p < 10^–3 and 3D locations with DOP ≥ 5: F_1,91 = 159.34, p < 10^–3). Table 2 shows the distribution of location error for each DOP and fix status class, with and without SA.

Location error (m)	Cumulative % of locations
2D		3D
DOP < 5	DOP ≥ 5		DOP < 5	DOP ≥ 5
	SA	no SA	SA	no SA	SA	no SA	SA	no SA
n	96	108	41	29	176	151	41	51
0–5	1.0	25.9		3.4	1.1	38.4		25.5
5–10	4.2	49.1		“	4.5	62.3		58.8
10–15	7.3	76.9		17.2	10.2	87.4	7.3	82.4
15–20	8.3	80.6		27.6	15.3	90.1	12.2	88.2
20–30	21.9	94.4		55.2	35.8	100	22.0	98.0
30–40	33.3	97.2	4.9	86.2	49.4		36.6	“
40–50	45.8	“	“	93.1	60.8		43.9	100
50–100	76.0	99.1	29.3	100	93.2		75.6
100–200	94.8	100	53.7		99.4		100
200+	100		100		100
mean	81.9	13.1	206.4	27.9	48.3	8.8	68.0	10.1
SE	10.7	1.7	22.0	2.5	2.6	0.5	7.1	1.0
max	776.9	160.1	693.5	62.8	230.9	27.1	191.9	43.2

3.3 Differential GPS collars

We analysed 475 and 654 locations recorded with differential GPS collars before and after SA removal. Mean location error decreased significantly from 11.3 m (SE = 3.1, max = 1441.4) to 5.2 m (SE = 0.3, max = 171.8) (F_1,1128 = 24.7, p < 10^–3). Few 2D locations were collected with DOP ≥ 10 (n = 19 with SA and n = 4 without SA) so we made only two DOP classes in this case: DOP < 5 and DOP ≥ 5. We pooled 3D locations in 4 classes: DOP < 5; 5 ≤ DOP < 10; 10 ≤ DOP < 15 and DOP ≥ 15 (Table 3 shows the influence of SA on location error distribution among each class). Mean location error decreased significantly without SA for 2D locations with DOP < 5 (F_1,74 = 6.0, p = 0.032) while 2D locations with DOP ≥ 5 did not show a significant difference before and after SA removal. Mean location error was significantly lower after SA removal for 3D locations with DOP < 5 (F_1,475 = 5.04, p = 0.025) and with 5 ≤ DOP < 10 (F_1,363 = 11.63, p < 10^–3). For 3D locations with higher DOP values, there was no significant difference with or without SA, but maximum location error was lower after SA removal, except for DOP ≥ 15, where the maximum error value was linked to the highest DOP recorded among all data sets (DOP = 268.1). Without this location, maximum error dropped to 43.9 m (instead of 171.8 m) which was lower than the maximum error before SA removal.

Location error (m)	Cumulative % of locations
2D		3D
DOP < 5	DOP ≥ 5		DOP < 5	5 ≤ DOP < 10	10 ≤ DOP < 15	DOP ≥ 15
	SA	no SA	SA	no SA		SA	no SA	SA	no SA	SA	no SA	SA	no SA
n	66	9	30	6		196	280	116	248	25	67	42	44
0–5	33.3	88.9	6.7	50.0		66.3	70.4	51.7	66.5	36.0	52.2	28.6	31.8
5–10	66.7	100	26.7	“		94.4	98.2	86.2	94.0	80.0	82.1	59.5	77.3
10–15	87.9		36.7	“		99.0	100	94.8	99.6	96.0	92.5	69.0	86.4
15–20	90.9		50.0	“		100		98.3	100	“	98.5	78.6	88.6
20–30	98.5		70.0	66.7				100		“	100	85.7	93.2
30–40	100		76.7	“						“		88.1	“
40–50			86.7	83.3						“		90.5	97.7
50–100			90.0	100						100		97.6	“
100–200			93.3									100	100
200+			100
mean	8.5	3.4	77.8	27.2		4.6	4.1	5.8	4.6	7.9	6.2	16.5	12.7
SE	0.85	0.9	47.6	9.6		0.2	0.1	0.4	0.2	1.9	0.6	3.4	3.9
max	30.2	9.8	1441.4	61.9		17.6	12.5	26.3	17.9	52.1	20.3	107.4	171.8

4 Conclusion

Fix accuracy obtained for non-differential GPS locations improved significantly after SA elimination. Differences in location errors recorded by the reference base station and non-differential GPS collars cannot be compared, because:

• the antenna ground plane was more effective for the reference base station than for the collars;
• the number of channels receiving GPS-satellite signals was higher and the fix interval was shorter for the base station;
• the topographic mask was lower for the base station;
• the tests did not take place simultaneously.

The performance of non-differential GPS collars would probably have been better with an antenna ground plane (i.e., the neck of fitted animals) and without topographic obstruction.

The effect of SA removal is logically far more important for non-differential than differential GPS collars because the influence of SA was already limited by differential correction. However, for both GPS modes, SA elimination results in an important decrease in the maximum location error. Differential GPS collars are still more accurate because differential correction can remove other sources of error, such as: satellite clock and ephemeris errors; ionospheric and tropospheric delays [2,6]. The performance of differential GPS collars could have been improved without topographic obstruction, and using a better antenna ground plane and a closer reference base station. Computational possibilities also exist to increase the accuracy of 3D locations with high DOP, by degrading them to 2D locations with fixed height value [3,6,7].

The choice between GPS modes (non-differential versus differential) will be determined by the scale needed in a particular study. If GPS is taken as an alternative to other techniques (such as classical VHF radiotracking) to study annual home ranges or migrations, the non-differential mode is probably accurate enough without the drawbacks induced by differential correction. We must recognize, as other GPS-users have [3–5,7,9], that working with differential GPS is more expensive (cost of a reference base station, maybe several for large migration studies), that data post-treatment requires more time and technical capabilities, and that collars need a greater memory capacity to store pseudorange data. We agree with [7], that differential GPS allows us to study animal-habitat relationships at a very fine spatio-temporal scale, never achieved yet with other techniques, thus opening new approaches to wildlife research. Studies on daily habitat use by individual animals will be possible, even in very fine scale patchy environments.

Acknowledgements

We thank A.R. Rodgers (Ontario Ministry of Natural Resources) and F. Spitz (Institut National de la Recherche Agronomique) for their helpful comments on an earlier version of this paper.

Version abrégée

Dès les premières utilisations du Global Positioning System (GPS) pour la localisation d’animaux, la connaissance de lˈexactitude des localisations a été un point essentiel. La Selective Availability (SA), mise en place par le département de la défense des États-Unis et qui dégradait volontairement les performances du système, était la principale source d’erreur jusqu’à sa suppression au mois de mai 2000. Une correction différentielle des données recueillies, par rapport à celles fournies par une station GPS de référence fixe dont les coordonnées sont connues avec exactitude, permettait dˈen limiter grandement les effets. Cette correction implique cependant plus de temps et une plus grande complexité de mise en œuvre. L’acquisition d’une station GPS de référence entraîne également une augmentation non négligeable du coût du système. Le but de cette étude est de quantifier l’effet de l’élimination de la SA sur l’exactitude des localisations obtenues par GPS différentiel et non-différentiel, afin d’apporter des éléments de réponse aux utilisateurs potentiels de ces techniques, concernant le choix du mode de calcul des localisations le plus adapté à leurs objectifs. Pour cela, trois types de matériels ont été testés : une station de référence 12 canaux fonctionnant en mode non-différentiel et des colliers GPS 8 canaux différentiels et non-différentiels. La station de référence était utilisée dans des conditions optimales (faible masque topographique) contrairement aux colliers dont les antennes GPS étaient privées de plan de sol (normalement constitué par le cou de l’animal), ce qui diminue leurs performances. De même, la station de référence utilisée pour la correction des données issues des colliers GPS différentiels était éloignée d’environ 420 km, ce qui est proche de la limite maximale d’utilisation de 500 km spécifiée par le fabricant, réduisant ainsi l’exactitude des localisations obtenues après correction différentielle. Les résultats sont donnés en fonction de deux paramètres indicateurs de l’exactitude des localisations : le statut des localisations en 2 ou 3 dimensions (selon le nombre de satellites utilisés pour le calcul d’une localisation), et la dilution of precision (DOP) qui est un indice de la qualité de la configuration des satellites pris en compte pour le calcul d’une localisation. Pour les trois types de matériels testés, nous avons constaté une augmentation logique de lˈexactitude de localisation, accompagnée d’une diminution des erreurs maximales observées. Pour la station de référence, l’erreur moyenne passe de 23,9 m à 1,4 m après suppression de la SA. Elle décroît de 78,0 m à 11,9 m pour les colliers non-différentiels, et de 11,3 m à 5,2 m pour les colliers différentiels. En mode non-différentiel, la diminution de l’erreur est significative pour toutes les classes de localisations. En mode différentiel, seules les classes de localisations les plus précises (2D et DOP < 5 ; 3D et DOP < 10) ont bénéficié d’un gain significatif d’exactitude. Les localisations obtenues en mode différentiel restent toujours plus précises, car la correction différentielle permet aussi de corriger partiellement d’autres sources d’erreurs telles que celles provenant de l’horloge et de l’éphéméride des satellites, et des vitesses de propagation des signaux lors de la traversée de l’atmosphère. Toutefois, la précision obtenue par GPS non-différentiel (donc avec moins de contraintes techniques et financières) peut maintenant s’avérer suffisante pour certaines applications (étude du domaine vital, migrations…). L’utilisation du GPS différentiel permet dorénavant d’aborder des études à une échelle encore plus fine (relation animal-plante, trajectométrie…) ouvrant ainsi de nouvelles perspectives de recherche.

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A. P. B. Martins; M. R. Heupel; A. Oakley-Cogan; A. Chin; C. A. Simpfendorfer Towed-float GPS telemetry: a tool to assess movement patterns and habitat use of juvenile stingrays, Marine and Freshwater Research, Volume 71 (2020) no. 1, p. 89 | DOI:10.1071/mf19048
Wenbo Yan; Zhigao Zeng; Huisheng Gong; Yan Duan; Leigang Zhao; Aliu Peng; Julia Molnar Locomotor activity patterns of takin (Budorcas taxicolor) in a temperate mountain region, PLOS ONE, Volume 15 (2020) no. 7, p. e0235464 | DOI:10.1371/journal.pone.0235464
Christos Bouras; Apostolos Gkamas; Vasileios Kokkinos; Nikolaos Papachristos Time Difference of Arrival Localization Study for SAR Systems over LoRaWAN, Procedia Computer Science, Volume 175 (2020), p. 292 | DOI:10.1016/j.procs.2020.07.043
Robert F. Keefe; Ann M. Wempe; Ryer M. Becker; Eloise G. Zimbelman; Emily S. Nagler; Sophie L. Gilbert; Christopher C. Caudill Positioning Methods and the Use of Location and Activity Data in Forests, Forests, Volume 10 (2019) no. 5, p. 458 | DOI:10.3390/f10050458
Marie-Line Maublanc; Lucie Daubord; Éric Bideau; Jean-François Gerard Experimental evidence of socio-spatial intolerance between female roe deer, Ethology Ecology Evolution, Volume 30 (2018) no. 5, p. 461 | DOI:10.1080/03949370.2017.1423116
Jodie Martin; Gwenaël Vourc’h; Nadège Bonnot; Bruno Cargnelutti; Yannick Chaval; Bruno Lourtet; Michel Goulard; Thierry Hoch; Olivier Plantard; A. J. Mark Hewison; Nicolas Morellet Temporal shifts in landscape connectivity for an ecosystem engineer, the roe deer, across a multiple-use landscape, Landscape Ecology, Volume 33 (2018) no. 6, p. 937 | DOI:10.1007/s10980-018-0641-0
Jodie Martin; Vincent Tolon; Nicolas Morellet; Hugues Santin-Janin; Alain Licoppe; Claude Fischer; Jérôme Bombois; Patrick Patthey; Elias Pesenti; Delphine Chenesseau; Sonia Saïd Common drivers of seasonal movements on the migration – residency behavior continuum in a large herbivore, Scientific Reports, Volume 8 (2018) no. 1 | DOI:10.1038/s41598-018-25777-y
Olivia A. Hurley Athletic Performance, Sport Cyberpsychology (2018), p. 45 | DOI:10.4324/9781315222769-3
Haruki Nakajima Assessing whether Japanese National Forest Inventory Plots Were Re-measured:, Journal of the Japanese Forest Society, Volume 99 (2017) no. 4, p. 156 | DOI:10.4005/jjfs.99.156
Wen‐Bo Yan; Zhi‐Gao Zeng; Hui‐Sheng Gong; Xiang‐Bo He; Xin‐Yu Liu; Yi‐Sheng Ma; Yan‐Ling Song Seasonal variation and sexual difference of home ranges by takins, The Journal of Wildlife Management, Volume 81 (2017) no. 5, p. 938 | DOI:10.1002/jwmg.21247
Haruki Nakajima Plot location errors of National Forest Inventory: related factors and adverse effects on continuity of plot data, Journal of Forest Research, Volume 21 (2016) no. 6, p. 300 | DOI:10.1007/s10310-016-0538-1
Wen-Bo Yan; Zhi-Gao Zeng; Hui-Sheng Gong; Xiang-Bo He; Xin-Yu Liu; Kai-Chuang Si; Yan-Ling Song Habitat use and selection by takin in the Qinling Mountains, China, Wildlife Research, Volume 43 (2016) no. 8, p. 671 | DOI:10.1071/wr16011
Woosik Lee; Woojin Chung, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) (2015), p. 460 | DOI:10.1109/urai.2015.7358906
Eunil Park; Heetae Kim; Jay Y. Ohm Understanding driver adoption of car navigation systems using the extended technology acceptance model, Behaviour Information Technology, Volume 34 (2015) no. 7, p. 741 | DOI:10.1080/0144929x.2014.963672
Emmanuelle Richard; Sonia Saïd; Jean-Luc Hamann; Jean-Michel Gaillard Daily, seasonal, and annual variations in individual home-range overlap of two sympatric species of deer, Canadian Journal of Zoology, Volume 92 (2014) no. 10, p. 853 | DOI:10.1139/cjz-2014-0045
YanBin ZHANG; WeiJun LU; DengYun LEI; YongCan HUANG; DunShan YU Effective PPS Signal Generation with Predictive Synchronous Loop for GPS, IEICE Transactions on Communications, Volume E97.B (2014) no. 8, p. 1742 | DOI:10.1587/transcom.e97.b.1742
Carla L. Dellaserra; Yong Gao; Lynda Ransdell Use of Integrated Technology in Team Sports, Journal of Strength and Conditioning Research, Volume 28 (2014) no. 2, p. 556 | DOI:10.1519/jsc.0b013e3182a952fb
Bart Milne; XiaoQi Chen; Chris Hann; Richard Parker, 2013 10th IEEE International Conference on Control and Automation (ICCA) (2013), p. 1609 | DOI:10.1109/icca.2013.6564913
Eunil Park; Ki Joon Kim; Angel P. del Pobil An Examination of Psychological Factors Affecting Drivers’ Perceptions and Attitudes Toward Car Navigation Systems, IT Convergence and Security 2012, Volume 215 (2013), p. 555 | DOI:10.1007/978-94-007-5860-5_66
Sophie Monsarrat; Simon Benhamou; François Sarrazin; Carmen Bessa-Gomes; Willem Bouten; Olivier Duriez; Dustin Rubenstein How Predictability of Feeding Patches Affects Home Range and Foraging Habitat Selection in Avian Social Scavengers?, PLoS ONE, Volume 8 (2013) no. 1, p. e53077 | DOI:10.1371/journal.pone.0053077
Dean M. Anderson; Rick E. Estell; Andres F. Cibils Spatiotemporal Cattle Data—A Plea for Protocol Standardization, Positioning, Volume 04 (2013) no. 01, p. 115 | DOI:10.4236/pos.2013.41012
Bingxuan ZHAO; Shigeru SHIMAMOTO Non-coherent Power Decomposition-Based Energy Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks, IEICE Transactions on Communications, Volume E95-B (2012) no. 1, p. 234 | DOI:10.1587/transcom.e95.b.234
T.A. Gipson; T. Sahlu; M. Villaquiran; S.P. Hart; J. Joseph; R.C. Merkel; A.L. Goetsch Use of global positioning system collars to monitor spatial-temporal movements of co-grazing goats and sheep and their common guardian dog, Journal of Applied Animal Research, Volume 40 (2012) no. 4, p. 354 | DOI:10.1080/09712119.2012.692475
Zakia Akasbi; Jens Oldeland; Jürgen Dengler; Manfred Finckh Analysis of GPS trajectories to assess goat grazing pattern and intensity in Southern Morocco, The Rangeland Journal, Volume 34 (2012) no. 4, p. 415 | DOI:10.1071/rj12036
Yong-Kui Li; Xue-Wei Bai, 2011 International Conference on Electrical and Control Engineering (2011), p. 3003 | DOI:10.1109/iceceng.2011.6057968
Emmanuelle Richard; Sonia Said; Jean-Luc Hamann; Jean-Michel Gaillard; Sharon Gursky-Doyen Toward an Identification of Resources Influencing Habitat Use in a Multi-Specific Context, PLoS ONE, Volume 6 (2011) no. 12, p. e29048 | DOI:10.1371/journal.pone.0029048
Luca P Ardigò Low-Cost Match Analysis of Italian Sixth and Seventh Division Soccer Refereeing, Journal of Strength and Conditioning Research, Volume 24 (2010) no. 9, p. 2532 | DOI:10.1519/jsc.0b013e3181b2c82a
Allen Y. Chang; Chang-Sung Yu; Sheng-Chi Lin; Yin-Yih Chang; Pei-Chi Ho, 2009 Fifth International Joint Conference on INC, IMS and IDC (2009), p. 1899 | DOI:10.1109/ncm.2009.306
Brian W. Morse; Nathan P. Nibbelink; David A. Osborn; Karl V. Miller Home range and habitat selection of an insular fallow deer (Dama dama L.) population on Little St. Simons Island, Georgia, USA, European Journal of Wildlife Research, Volume 55 (2009) no. 4, p. 325 | DOI:10.1007/s10344-008-0245-0
Dominique Pépin; Nicolas Morellet; Michel Goulard Seasonal and daily walking activity patterns of free-ranging adult red deer (Cervus elaphus) at the individual level, European Journal of Wildlife Research, Volume 55 (2009) no. 5, p. 479 | DOI:10.1007/s10344-009-0267-2
Gilles Bourgoin; Mathieu Garel; Dominique Dubray; Daniel Maillard; Jean-Michel Gaillard What determines global positioning system fix success when monitoring free-ranging mouflon?, European Journal of Wildlife Research, Volume 55 (2009) no. 6, p. 603 | DOI:10.1007/s10344-009-0284-1
Luca Borghesio Effects of fire on the vegetation of a lowland heathland in North-western Italy, Plant Ecology, Volume 201 (2009) no. 2, p. 723 | DOI:10.1007/s11258-008-9459-1
Nicolas Morellet; Hélène Verheyden; Jean‐Marc Angibault; Bruno Cargnelutti; Bruno Lourtet; Mark A.J. Hewison The Effect of Capture on Ranging Behaviour and Activity of the European Roe Deer Capreolus capreolus, Wildlife Biology, Volume 15 (2009) no. 3, p. 278 | DOI:10.2981/08-084
Mikko Lehtinen; Ari Happonen; Jouni Ikonen, 2008 16th International Conference on Software, Telecommunications and Computer Networks (2008), p. 334 | DOI:10.1109/softcom.2008.4669506
E. Richard; N. Morellet; B. Cargnelutti; J.M. Angibault; C. Vanpé; A.J.M. Hewison Ranging behaviour and excursions of female roe deer during the rut, Behavioural Processes, Volume 79 (2008) no. 1, p. 28 | DOI:10.1016/j.beproc.2008.04.008
Dominique Pépin; Christophe Adrados; Georges Janeau; Jean Joachim; Carole Mann Individual variation in migratory and exploratory movements and habitat use by adult red deer (Cervus elaphus L.) in a mountainous temperate forest, Ecological Research, Volume 23 (2008) no. 6, p. 1005 | DOI:10.1007/s11284-008-0468-2
Christophe Adrados; Christophe Baltzinger; Georges Janeau; Dominique Pépin Red deer Cervus elaphus resting place characteristics obtained from differential GPS data in a forest habitat, European Journal of Wildlife Research, Volume 54 (2008) no. 3, p. 487 | DOI:10.1007/s10344-008-0174-y
Luca Borghesio Effects of fire on the vegetation of a lowland heathland in North-western Italy, Herbaceous Plant Ecology (2008), p. 359 | DOI:10.1007/978-90-481-2798-6_30
ANDREW D. TOWNSHEND; CHARLES J. WORRINGHAM; IAN B. STEWART Assessment of Speed and Position during Human Locomotion Using Nondifferential GPS, Medicine Science in Sports Exercise, Volume 40 (2008) no. 1, p. 124 | DOI:10.1249/mss.0b013e3181590bc2
C. BROOKS; C. BONYONGO; S. HARRIS Effects of Global Positioning System Collar Weight on Zebra Behavior and Location Error, The Journal of Wildlife Management, Volume 72 (2008) no. 2, p. 527 | DOI:10.2193/2007-061
Gail Schofield; Charles M. Bishop; Grant MacLean; Peter Brown; Martyn Baker; Kostas A. Katselidis; Panayotis Dimopoulos; John D. Pantis; Graeme C. Hays Novel GPS tracking of sea turtles as a tool for conservation management, Journal of Experimental Marine Biology and Ecology, Volume 347 (2007) no. 1-2, p. 58 | DOI:10.1016/j.jembe.2007.03.009
Kim G. Poole; Robert Serrouya; Kari Stuart-Smith Moose Calving Strategies in Interior Montane Ecosystems, Journal of Mammalogy, Volume 88 (2007) no. 1, p. 139 | DOI:10.1644/06-mamm-a-127r1.1
Donat‐Peter Häder; Michael Lebert; Martin Schuster; Lineu del Ciampo; E. Walter Helbling; Richard McKenzie ELDONET—A Decade of Monitoring Solar Radiation on Five Continents, Photochemistry and Photobiology, Volume 83 (2007) no. 6, p. 1348 | DOI:10.1111/j.1751-1097.2007.00168.x
BRUNO CARGNELUTTI; AURÉLIE COULON; A.J. MARK HEWISON; MICHEL GOULARD; JEAN‐MARC ANGIBAULT; NICOLAS MORELLET Testing Global Positioning System Performance for Wildlife Monitoring Using Mobile Collars and Known Reference Points, The Journal of Wildlife Management, Volume 71 (2007) no. 4, p. 1380 | DOI:10.2193/2006-257
G.L. Andersen How to detect desert trees using corona images: Discovering historical ecological data, Journal of Arid Environments, Volume 65 (2006) no. 3, p. 491 | DOI:10.1016/j.jaridenv.2005.07.010
TABITHA A. GRAVES; JOHN S. WALLER Understanding the Causes of Missed Global Positioning System Telemetry Fixes, Journal of Wildlife Management, Volume 70 (2006) no. 3, p. 844 | DOI:10.2193/0022-541x(2006)70[844:utcomg]2.0.co;2
Kyla D. Huebner; Michelle M. Porter; Shawn C. Marshall Validation of an Electronic Device for Measuring Driving Exposure, Traffic Injury Prevention, Volume 7 (2006) no. 1, p. 76 | DOI:10.1080/15389580500413067
Christopher L. Jerde; Darcy R. Visscher GPS MEASUREMENT ERROR INFLUENCES ON MOVEMENT MODEL PARAMETERIZATION, Ecological Applications, Volume 15 (2005) no. 3, p. 806 | DOI:10.1890/04-0895
Eugene D. Ungar; Zalmen Henkin; Mario Gutman; Amit Dolev; Avraham Genizi; David Ganskopp Inference of Animal Activity From GPS Collar Data on Free-Ranging Cattle, Rangeland Ecology Management, Volume 58 (2005) no. 3, p. 256 | DOI:10.2111/1551-5028(2005)58[256:ioaafg]2.0.co;2
T.H. Witte; A.M. Wilson Accuracy of non-differential GPS for the determination of speed over ground, Journal of Biomechanics, Volume 37 (2004) no. 12, p. 1891 | DOI:10.1016/j.jbiomech.2004.02.031
Travis O. Brenden; Brain R. Murphy; Eric M. Hallerman Estimating Total Error in Locating Radiotelemetry Transmitters by Homing in a Riverine Environment, Journal of Freshwater Ecology, Volume 19 (2004) no. 2, p. 295 | DOI:10.1080/02705060.2004.9664544
Dominique Pépin; Christophe Adrados; Carole Mann; Georges Janeau ASSESSING REAL DAILY DISTANCE TRAVELED BY UNGULATES USING DIFFERENTIAL GPS LOCATIONS, Journal of Mammalogy, Volume 85 (2004) no. 4, p. 774 | DOI:10.1644/ber-022

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