assessing three new gps combined l1/l2c acquisition methods
TRANSCRIPT
[23] Crausaz, A., Gasquet, E., and Sanchis, E.
Power MosFet: ESA driving licence.
In Proceedings of the 4th European Space Power
Conference, ESA SP-369, Sept. 1995, 227—233.
Assessing Three New GPS Combined L1/L2CAcquisition Methods
This paper presents three new global positioning system
acquisition methods that make use of both L1 C/A and L2C
signals in a combined way. The methods perform joint estimation
of the Doppler frequencies and code delays on L1 and L2
without increasing the coherent integration time. Each method
is assessed in terms of theoretical probabilities of false alarm
and detection as well as through testing with real intermediate
frequency data. The first method is a noncoherent summation of
L1 and L2 correlator outputs. The second method implements
an independent differential summation of L1/L2 correlator
outputs, and the third method uses a noncoherent plus dependent
differential summation. While each method provides increased
detection performance compared with the standard noncoherent
acquisition applied on L1 C/A, the noncoherent plus dependent
differential summation method outperforms all of the others in
the scenarios investigated.
I. INTRODUCTION
A. Background and Motivations
Global positioning system (GPS) receivers must
overcome extreme challenges to provide reasonable
performance when operating under harsh conditions.
In typical urban canyon and indoor environments,
attenuation levels exceeding 20 dB have been
observed [1]. The most common solution for this
attenuation is to increase the coherent integration
time used [2, 3]. For instance, high-sensitivity GPS
receivers rely on data bit estimation routines to
increase the integration time, while assisted GPS uses
known data bits sent from a reference station such that
the remote receiver can wipe the navigation message
off the incoming signal. Nevertheless, availability or
reliability of the needed external information is not
always guaranteed and data bit estimation methods
remain limited.
This paper focuses on the possibility of combining
the new L2C signal with the legacy L1 C/A signal
in terms of acquisition. The principal contribution
of this paper is the development of three novel
Manuscript received September 16 2009; revised May 3, 2010 and
August 29 2010; released for publication September 16, 2010.
IEEE Log No. T-AES/47/3/941795.
Refereeing of this contribution was handled by L. Kaplan.
0018-9251/11/$26.00 c° 2011 IEEE
CORRESPONDENCE 2239
combined acquisition methods and the assessment of
their performance through probabilities of detectionand false alarm using theoretical means and real
observations.
B. L1 C/A and L2C Signals
L2C is located at 1227.60 MHz and transmitted
with a power 1.5 dB lower than the legacy GPS L1
C/A itself located at 1575.42 MHz and transmitted
with a power of ¡158:5 dBW. The L1 C/A signalis composed of a satellite dependent pseudorangingcode of 1023 chips that is clocked at 1.023 MHz
using binary phase shift keying (BPSK), which is
also modulated with a 50-Hz navigation message.
The resulting signal is then used to BPSK modulatethe 1575.42-MHz carrier signal. On the other hand,
the L2C signal contains two pseudoranging codes
called the CM code and the CL code. A data channel
is formed by the 10230 chip CM code clocked at
511.5 kHz and modulated by a 50-Hz navigationmessage. Unlike the legacy L1 C/A, exactly one CM
code is present within one navigation data bit, thereby
removing the need for data bit synchronization. The
L2C navigation message, though also transmitted at50 bits/s, is independent of the L1 C/A navigation
message. The L2C CL code is 767 250 chips in length
and is also clocked at 511.5 kHz for a total length
of 1.5 s. The CL code is dataless. Because the L2
frequency also carries the military P(Y) signal, thedevelopment of L2C was limited to a single biphase
carrier. Therefore the two channels (CM and CL)
are time multiplexed, resulting in a 1.023-MCps
signal used to BPSK modulate the 1227.6-MHzcarrier. The CM and CL channels share the total L2C
power of ¡160 dBW equally. However, because of
its extreme length, the CL code is usually ignored
during correlation, which leads to a signal degradationof 3 dB, pushing L2C 4.5 dB lower than L1 C/A in
terms of acquisition [4].
C. Constraints Affecting Acquisition Process
During the acquisition process, a range of possible
Doppler frequencies and code delays must be
searched. However, if the estimated Doppler used to
remove the carrier is too far from the true Doppler,
the signal is averaged out. Therefore, the Dopplerbin size is dependent on the correlation time. The
relation commonly used to determine the number
of Doppler bins needed to cover a Doppler range of
¡5000 to 5000 Hz to the coherent integration time iswell known and can be expressed as
ND =10000 ¢3 ¢T
2(1)
with ND representing the number of Doppler bins and
T the coherent integration time used [5]. Therefore,
longer coherent integration means smaller Doppler binsize and increased complexity.
However, the fact that the acquisition of L2Cleads immediately to the determination of the databit boundaries remains advantageous. A FFT-basedmethod for CM/CL acquisition was investigated by [6]and used to operate in weak signal environments. Limet al. [7] proposed a fast-acquisition method makinguse of L1 aiding L2 to estimate L2C CM code delaysand carrier Doppler. Tracking performance of theL2C signal through combination of the data and pilotchannels has been analyzed by [8] and [9]. However,the first methods actually performing interfrequencyL1/L2 combinations based on the power duplicationresulting from the presence of two signals was onlyproposed by [10] and [11].The focus of this paper is on the theoretical
assessment of three different L1/L2 combinedacquisition techniques. Because both L1 C/A andL2C are time-synchronized signals transmitted bythe same satellite, one can directly combine themto improve receiver detection properties. However,whereas both L1/L2 signals are synchronized at theirgeneration, the presence of instrumental group delaywithin the satellite and receiver, and the signal transitthrough the ionospheric layer desynchronize thesignals. The instrumental relative group delay is onthe order of 1 ns, which is negligible [12, 13] in termsof acquisition for L1/L2 interfrequency combinations,and the ionospheric effect hardly affects the combinedacquisition either [10, 11].Finally, a direct relation exists between the
Doppler effect of L1 and the Doppler effect onL2 because this effect is mainly due to the relativeuser-satellite motion:
fD1 =f1f2fD2: (2)
The remainder of this paper is organized asfollows. Section II introduces the signal modelused throughout. Section III presents the differentcombined acquisition methods and their theoreticalstatistical properties. Section IV verifies thederivations of Section III using real data.
II. SIGNAL MODEL
After the integrate and dump process, the real andimaginary parts of the correlator output at the kthcoherent integration can be expressed as
I(k) =Ap2¢ d(k) ¢R(dt)
¢ sin(¼¢FDT)¼¢FDT
cos(¼¢FT(2k¡ 1)+Á)
Q(k) =Ap2¢ d(k) ¢R(dt)
¢ sin(¼¢FDT)¼¢FDT
sin(¼¢FT(2k¡ 1)+Á)
(3)
where A is the signal amplitude, d(k) the data bit,¢F the Doppler residual, dt the code misalignmentin chips, R the autocorrelation function, T the
2240 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 47, NO. 3 JULY 2011
coherent integration time, and ' the phase differencebetween the incoming signal and local oscillator atthe beginning of the first coherent integration (k = 1).Note that the independent Gaussian noises present atthe receiver input are also modified by the Dopplerremoval and integrate-and-dump processes. Thenoise present at the output of the correlators remainsindependent and identically distributed for the real andimaginary parts, namely N(0,¾2) with ¾2 = ¾2IF=NS ,where ¾2IF is the noise variance at the intermediatefrequency. This discussion is valid regardless of thesignal considered and, as such, is valid for both L1and L2C real and imaginary parts of the correlatoroutputs.
III. COMBINED ACQUISITION METHODS
A. General Presentation
The proposed acquisition methods arepostcorrelation methods. As such, they use the outputof the correlators as input and differ only after theintegrate-and-dump process takes place. The firstmethod, called NCL1L2, represents the summation ofnoncoherent acquisition performed on L1 and L2. Itsmathematical representation is
SNCL1L2 =
MXk=1
(I2k,L1 +Q2k,L1)+W
2 ¢MXk=1
(I2k,L2 +Q2k,L2)
(4)
with W representing a weight factor applied to L2Ccorrelator output.The second, named DiffL1L2, performs a
summation of differential acquisitions done on L1 andL2. Its mathematical expression is
SDiffL1L2 =
M=2Xk=1
(I2k,L1 ¢ I2k¡1,L1 +Q2k,L1 ¢Q2k¡1,L1)
+W2 ¢M=2Xk=1
(I2k,L2 ¢ I2k¡1,L2 +Q2k,L2 ¢Q2k¡1,L2):
(5)
Finally, the last method is named NCDiffL1L2 andis the summation of the noncoherent and differentialacquisitions on L1 and L2. Its mathematicalrepresentation is
SNCDiffL1L2 =
266664MXk=1
(I2k,L1 +Q2k,L1)
+
MXk=2
(Ik,L1 ¢ Ik¡1,L1 +Qk,L1 ¢Qk¡1,L1)
377775
+W2 ¢
266664MXk=1
(I2k,L2 +Q2k,L2)
+
MXk=2
(Ik,L2 ¢ Ik¡1,L2 +Qk,L2 ¢Qk¡1,L2)
377775 :(6)
The difference between the differential methods
DiffL1L2 and NCDiffL1L2 is that DiffL1L2 uses only
independent correlator outputs during the differential
summation, whereas NCDiffL1L2 uses dependent
correlator outputs during the differential summation
[see indices of (5) and (6)].
B. Theoretical Performance
The following section develops the theoretical
probability density functions under the hypotheses
H0: signal is not present
H1: signal is present
Note that in the following, the cell probabilities
are discussed. Indeed, the cell probabilities strongly
depend on the acquisition method used [14] as
well as the signal fading condition [15]. The
probability density functions derived are then used
in Section IVD to derive the probabilities of false
alarm and detection as well as the ROC used to
evaluate the performance of each method. Based on
(3), the correlator outputs for L1 C/A and L2C can be
expressed as
Ik,L1L2 = A1=2(k) ¢ cos(¼¢F1=2T(2k¡ 1)+Á1=2) +wIk,L1L2Qk,L1L2 = A1=2(k) ¢ sin(¼¢F1=2T(2k¡ 1)+Á1=2) +wQk,L1L2
(7)
with A1=2(k) = (A1=2=p2)d1=2(k) ¢R(dt1=2) ¢
(sin(¼¢F1=2T)¼¢F1=2T), wIk,L1 » wQk,L1 »N(0,¾2), and
wIk,L2 » wQk,L2 »N(0,2¾2).Note that all the noise components are
uncorrelated and that L2C noise variance is twice that
of L1 because the CL code is ignored.
1) NCL1L2 Method:
a) Under H0: The probability density function
(PDF) of the NCL1L2 method under H0 can be
expressed as a summation of independent chi-square
random variables [16] as
p(s) =
266641
2¾2exp
³ ¡s2¾2
´1
(M ¡ 1)!
μ¾2
¾2¡¾22
¶M¢M¡1Xi=0
(2(M ¡ 1)¡ i)!i!(M ¡ 1¡ i)!
μ¾22
¾22 ¡¾2¶M¡1¡i³
s
2¾2
´i37775
+
266641
2¾22exp
μ ¡s2¾22
¶1
(M ¡ 1)!
μ¾22
¾22 ¡¾2¶M
¢M¡1Xi=0
(2(M ¡ 1)¡ i)!i!(M ¡ 1¡ i)!
μ¾2
¾2¡¾22
¶M¡1¡iμs
2¾22
¶i37775(8)
with ¾22 = 2 ¢W2 ¢¾2. The NCL1L2 characteristicfunction under H0 is
ªS(!) =
μ1
(1¡ 2j!¾2) ¢ (1¡ 2j!¾22)¶M
: (9)
b) Under H1: From the previous discussion, one
can deduce that the PDF of the NCL1L2 under H1 is a
CORRESPONDENCE 2241
summation of two independent noncentral chi-squarevariables:
p(s) =1
2¾2
μ¾
¾2
¶2Mμs
¸1
¶M¡1=2exp
μ ¡s2¾2
¶¢ exp
μ¡1=2
μ¸1¾2+¸2¾22
¶¶
¢1Xi=0
1Xl=0
2666664¡ (M + i+ l)
i!l!¡ (M + l)
à ps¸2¾
2
2p¸1¾
42
!l
¢Ãp
s(¾22 ¡¾2)p¸1¾
22
!iI2M+i+l¡1
Ãps¸1
¾21
!3777775
(10)with ¸1 =
PMk=1A1(k)
2 ¼ (M ¢A21=2) and ¸2 =PMk=1A2(k)
2 ¼ (M ¢A22=2) and assuming that ¢F, dt1,and dt2 are small. The NCL1L2 characteristic functionunder H1 is
ªS(!) =
μ1
(1¡ 2j!¾2)(1¡ 2j!¾22)¶M
¢ expμ
j!¸11¡ 2j!¾2
¶¢ exp
μj!¸2
1¡ 2j!¾22
¶:
(11)
2) DiffL1L2 Method: The following derivationis based on the one-frequency-only differentialacquisition from [17].a) Under H0: The PDF under H0 for the DiffL1L2
method is expressed as
fS(s) =
KXi=1
2¡i+1=2kski¡1=2p¼ ¢¡ (i)
2664 aK,i¾¡(2i+1)K(i¡1=2)
μktk¾2
¶+bK,i¾
¡(2i+1)2 K(i¡1=2)
μktk¾22
¶3775(12)
where ¡ is the Gamma function, K is the modifiedBessel function of the second kind, and thecoefficients aK,i and bK,i can be determined using thefollowing recurrence relationship (where ± representsthe Dirac function) derived similarly to that in [18]:
aK,K¡i = μ1aK¡1,K¡i¡1 + μ2aK,K¡i+1 +BK¡1,i(13)
bK,K¡i = μ2bK¡1,K¡i¡1 + μ1bK,K¡i+1 +AK¡1,i(14)
AK¡1,i =
"K¡1Xn=1
μn+12 aK¡1,n
#±(i¡K +1) (15)
BK¡1,i =
"K¡1Xn=1
μn+11 bK¡1,n
#±(i¡K +1) (16)
μ1 = a1,1 =¾2
¾2¡¾22(17)
μ2 = b1,1 =¾2
¾22 ¡¾2: (18)
The characteristic function of DiffL1L2 under H0can be derived based on [16]:
ªS(!) =
μ1
1+¾4!2¢ 1
1+¾42!2
¶M=2: (19)
b) Under H1: A closed form solution for thePDF of SDiffL1L2 was not determined. Its characteristicfunction was used instead to derive the probabilityof false alarm and detection of the DiffL1L2 method.Assuming that the Doppler removal process isperformed perfectly or that the Doppler residual issimilar from one coherent integration to the nextand that the data bit remains unchanged during theacquisition period, one can show using [16] that
ªSDiffL1L2 (!) =ªSIL1+SQ
L1
(!) ¢ªSIL2+S
Q
L2
(!)
=
·1
(1+¾4!2)(1+¾42!2)
¸M=2¢ exp
"j! ˆ 1¡ ˆ 1¾2!22 ¢ (1+¾4!2) +
j! ˆ 2¡ ˆ 2¾22!22 ¢ (1+¾42!2)
#(20)
with ˆ 1 = (M ¢ A21=2) and ˆ 2 = (M ¢ A22=2).At this point, the characteristic function of SDiffL1L2
can be used to numerically determine its probabilitydensity function using the following property of thecharacteristic function:
fSDiffL1L2 (s) =1
2¼
Z +1
¡1ªSDiffL1L2 (!) ¢ ej!sd!: (21)
The numerical inverse Fourier transform involvedin (21) was done following the method proposedby [19].3) NCDiffL1L2 Method: Defining
XL1 =
266666666666666666666666666666666666664
I1,L1
Q1,L1
I1,L1 + I2,L1
Q1,L1 +Q2,L1
...
...
Ik,L1 + Ik+1,L1
Qk,L1 +Qk+1,L1
...
...
IM¡1,L1 + IM,L1
QM¡1,L1 +QM,L1
IM ,L1
QM,L1
3777777777777777777777777777777777777752242 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 47, NO. 3 JULY 2011
and
XL2 =W ¢
266666666666666666666666666666666664
I1,L2
Q1,L2
I1,L1 + I2,L2
Q1,L1 +Q2,L2
...
...
Ik,L2 + Ik+1,L2
Qk,L2 +Qk+1,L2
...
...
IM¡1,L2 + IM,L2QM¡1,L2 +QM,L2
IM,L2
QM ,L2
377777777777777777777777777777777775the mathematical representation of the output of the
NCDiffL1L2 becomes
SNCDiffL1L2 =12XTL1XL1 +
12XTL2XL2: (22)
Note that the vectors XL1 and XL2 are independent.
Moreover, as the covariance matrix CXL1 of XL1is a (2 ¢M +2)£ (2 ¢M +2) real symmetric matrix,
it is possible to decompose it using eigenvector
decomposition. The matrix of eigenvectors is then
orthogonal. The aforementioned properties of a real
symmetric matrix are summarized for XL1 and CXL1through
CXL1 =QDL1QT (23)
QT =Q¡1: (24)
When projecting XL1 in the orthogonal basis
defined by Q, the resulting vector YL1 has a diagonal
covariance matrix. Moreover, the norm of YL1 is equal
to the norm of XL1.
A similar derivation can be performed for XL2.
YL1 and YL2 are independent of each other and the
elements composing YL1 or YL2 are independent
between themselves (both vectors having a diagonal
covariance matrix). Moreover, the variance of the
elements of YL1 and YL2 are the elements located on
the diagonal of DL1 and DL2, respectively.
After this development, the characteristic functions
of SNCDiffL1L2 can readily be determined.
a) Under H0: SNCDiffL1L2 is the summation of
(4 ¢M +2) central chi-square random variables with
one degree of freedom. Its characteristic function is
expressed as
ªS(!) =
2M+1Yi=1
μ1
1¡ 2j!DL1(i)
¶1=2μ1
1¡ 2j!DL2(i)
¶1=2:
(25)
DL1(i) represents the ith element of the diagonal of
DL1.
b) Under H1: SNCDiffL1L2 is the summation of
(4 ¢M +2) noncentral chi-square random variables
with one degree of freedom and noncentrality
parameters the square of the mean of the elements of
YL1 and YL2. Its characteristic function is expressed as
ªS(!) =
2M+1Yi=1
μ1
1¡ 2j!DL1(i)
¶1=2¢ exp
μj! ¢ YL1(i)2
1¡ 2j!DL1(i)
¶
¢2M+1Yi=1
μ1
1¡ 2j!DL2(i)
¶1=2¢ exp
μj! ¢ YL2(i)2
1¡ 2j!DL2(i)
¶:
(26)
Once the PDFs and characteristic functions of
each method have been theoretically computed, it is
possible to determine the theoretical probabilities of
false alarm and detection. The theoretical results are
verified using real data, and the performance of each
method is discussed.
IV. RESULTS
A. Correlator Outputs from Real Data
The two signals were collected using a L1/L2
antenna and passed through a variable attenuator. The
signals were then split between a L1/L2 RF front end
and a GPS L1/L2 industrial receiver used for C/N0reference. The C/N0 on L1 during the data collection
was 30 dB¢Hz. Finally, the L1/L2 IF signals werestored on the PC performing the acquisition methods.
B. ROC Curves, Comparison, and Analysis
The ROC curve is defined as the probability of
detection Pd as a function of the probability of false
alarm Pfa. In the specific case investigated here, the
probability of detection is defined as the probability
of detecting the presence of the signal under the H1hypothesis. The probability of false alarm is defined
as the probability of detecting the presence of the
signal under the hypothesis H0.
In the following, the coherent integration length
considered is 1 ms and the number of coherent
integrations needed to combine is M = 4.
Prior to any analysis, the optimal weight W
applied on L2 must be determined for each method.
To do so, the probability of detection for a fixed
probability of false alarm of 0.005 was computed for
a set of weight values ranging from 0 to 1. Indeed,
because the signal power and noise variance are
larger on L2 and the weight applied on L1 is 1, it is
CORRESPONDENCE 2243
Fig. 1. Theoretical and “real data” estimated ROC curves for NCL1L2.
Fig. 2. Theoretical and “real data” estimated ROC curves for DiffL1L2.
expected that the weight to apply on L2 would be
smaller than 1. The optimal weight found is similar
for each method and has a value of 0.55.
Once the optimal weight was determined, the
performance of each method is investigated through
comparison of its ROC curve with the ROC curves of
a noncoherent acquisition on L1, only using the same
number of milliseconds. Because the signal power on
L2 is smaller than on L1, it is expected that the ROC
curves on a noncoherent acquisition on L2 will show
poorer performance than a noncoherent acquisition
on L1. However, it is still shown here to underline
the improvement brought by an L2-only acquisition,
which is most interesting because it removes the need
for data bit synchronization on L2 and also on L1.
Figs. 1—4 show the ROC curve and ROC curve
differences with a noncoherent summation performed
on L1 only for each method.
2244 IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 47, NO. 3 JULY 2011
Fig. 3. Theoretical and “real data” estimated ROC curves for NCDiffL1L2.
Fig. 4. Difference between new methods ROC and ROC obtained for noncoherent acquisition on L1 only using real data.
Note that because the Doppler residual was kept
low (about 30 Hz) during the creation of the correlator
outputs, the DiffL1L2 theoretical ROC matches the
ROC obtained with real data very well even though
the Doppler residual was ignored in the theoretical
development. Because one tries to maximize the
probability of detection while minimizing the
probability of false alarm, the fact that the ROC curve
of the DiffL1L2 method passes under the ROC curve of
the L1 noncoherent acquisition has no impact on the
validity of this method.
From the figures, it is clear that each combining
method outperforms the noncoherent acquisition on
L1 only. Moreover, when used to determine the ROC
curves, the theoretical models developed matches
properly the estimated ROC obtained through real
data. Finally, NCDiffL1L2 not only outperforms the
noncoherent acquisition performed on L1 only but
CORRESPONDENCE 2245
also the two other L1/L2 combining acquisition
proposed as shown in Fig. 4.
V. CONCLUSIONS
By combining the GPS signals transmitted on the
L1 and L2 frequency bands, one cannot only profit
from the advantages of L1 (higher signal power) and
L2 (no need for data bit synchronization) but also
improve overall detection capability compared with
using only one signal. Three methods were proposed
to combine L1 C/A and L2C signals at the acquisition
level and all proved to outperform the legacy L1 C/A
noncoherent acquisition. The first method, NCL1L2,
combining the noncoherent acquisition on L1 and
L2, shows promising performance when the proper
weight is applied. An improvement of 8% compared
with the standard noncoherent acquisition on L1 only
was observed. Similarly, for the NCDiffL1L2 method,
combining noncoherent and differential acquisitions
on L1 and L2 significantly improved the probability
of properly detecting the signals. An improvement of
25% is expected for the NCDiffL1L2 method. While
not shown in the earlier discussion, the DiffL1L2method combining differential acquisitions performed
on L1 and L2 is strongly dependent on the residual
Doppler frequency remaining after the Doppler
removal process. Poor performance has been observed
when the residual Doppler error varies over the whole
range of half a Doppler bin size defined by a 1-ms
coherent integration (0—333.33 Hz). However, by
limiting the Doppler error to 100 Hz, performance is
greatly improved and the DiffL1L2 method showed an
improvement of 15% compared with the noncoherent
L1 acquisition. Because limiting the Doppler bin
size allows for increased coherent integration times,
the DiffL1L2 is recommended for longer coherent
integration. Indeed, 2 ms of coherent integration
proved to reduce the range of the residual Doppler
error such that the DiffL1L2 method is no longer
negatively affected by this error. Finally, while the
NCDiffL1L2 method outperforms the noncoherent L1
acquisition, it also shows better performance than the
other two combining methods. However, because of
its complexity compared with the NCL1L2 method, one
would employ it only when appropriate computational
power is available.
CYRILLE GERNOT
KYLE O’KEEFE
GERARD LACHAPELLE
Position, Location and Navigation (PLAN) Research Group
Dept. of Geomatics Engineering
University of Calgary Schulich School of Engineering
2500 University Drive NW
Calgary, Alberta, T2N 1N4
Canada
E-mail: ([email protected])
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Magnetically Jointed Module Manipulators: NewConcept for Safe Intravehicular Activity in SpaceVehicles
Robots for intravehicular (IVA) support are being studied for
their potential to reduce the workload of astronauts. IVA support
robots must meet strict safety requirements, and they need to be
compact to share limited room with astronauts. A magnetically
jointed module manipulator (MagMo) solves these problems in a
unique manner. The manipulator is not harmful when it contacts
humans unexpectedly, because it disassembles by contact force,
and it can be easily assembled only when it is needed. In this
paper, the basic concept of the MagMo is introduced.
I. INTRODUCTION
About 50 years have passed since the first
human space flight. Manned space utilization is
becoming a mature endeavor, partly because of the
establishment of manned space infrastructure. The role
of the International Space Station (ISS) has steadily
increased, and it has become an established space
base for various types of experiments that may lead
to further space utilization. The Japanese Experiment
Module was attached successfully to the ISS in 2008.
It contains various types of equipment and is ready to
begin operation as part of the space infrastructure.
Astronauts must perform diverse tasks, including
housekeeping tasks to maintain their space vehicle,
as well as different types of experiments. Reducing
and optimizing the astronauts’ workload is a focus
of interest so that a manned space system can be
operated more effectively. A substantial number of
astronaut tasks are simple and do not require the
astronauts’ intelligence.
It has been proposed that robots may be used to
reduce the astronauts’ workload. An autonomous
robot could perform a simple repetitive task that
would not require intelligence. Furthermore, a
teleoperated robot may be able to handle unexpected
situations with the support of ground personnel.
However, even though some functions can be
performed by teleoperations, these robots would
Manuscript received October 3, 2008; revised January 28, 2010;
released for publication September 20, 2010.
IEEE Log No. T-AES/47/3/941797.
Refereeing of this contribution was handled by R. Michelson.
Authors’ addresses: Tokyo University of Science, 2641 Yamasaki,
Noda, Chiba 278-8510 Japan, E-mail: ([email protected]).
0018-9251/11/$26.00 c° 2011 IEEE
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