Comparative Studies of GPS
Multipath Mitigation
Methods Performance.
IEEE TRANSACTIONS ON AEROSPACE AND
ELECTRONIC SYSTEMS VOL. 49, NO. 3 JULY 2013
Student : YuanHao Cheng
Outline
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Introduction
describes the three techniques:
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HRC
MMT
CADLL
Experimental results
Conclusion
References
Abstract
Coupled amplitude delay lock loops (CADLL) is a recently
proposed multipath estimation and mitigation technique based on
joint estimation of line-of-sight (LOS) and multipath signal
amplitude, code phase, and carrier phase. The CADLL
performance is evaluated against two widely known multipath
mitigation methods: the high-resolution correlator (HRC),
representative of the correlators combination methods, and the
multipath mitigation technique (MMT), representative of
multipath estimation methods. Multiple tests emulating various
scenarios are performed to demonstrate that CADLL always
generates better results than the other two methods. Additionally,
CADLL has better noise performance, can estimate multipath
signals using shorter integration time, and is capable of tracking
dynamic multipath signals.
INTRODUCTION(1/2)
Simulation tests using a statistical urban multipath signal model
prove that CADLL is effective in estimating and mitigating
multipath in severe multipath environments. These simulation
results are further validated using satellite signals generated by
Spirent Global Navigation Satellite System (GNSS) 6700.
Despite the major advances in Global Navigation Satellite
Systems (GNSS) technology in recent years, it is commonly
recognized that multipath remains a challenge in high-accuracy
GNSS applications such as surveying, precision aircraft landing
systems, remote sensing, and other navigation systems in urban
or indoor environments.
INTRODUCTION(2/2)
Over the past decades a number of techniques have been
proposed and implemented to mitigate multipath errors at the
receiver baseband signal processing level.
In this paper we demonstrate that CADLL can indeed accurately
estimate and track component signal parameters, not only in static
applications but also for dynamic multipath signals. We achieve
this by evaluating and comparing the performance of CADLL in
various scenarios with two representatives of the current
techniques mentioned above.
Describes the three techniques
In order to compare the performance of CADLL architecture with
the currently existing methods, we selected two representative
methods that are commonly used in industrials and scientific
research:
HRC, which is representative of the correlators combination class,
and MMT, which is a variant of the maximum likelihood (ML)
principle method with improved computational efficiency.
HRC
The HRC technique uses five correlators, equally spaced by a
chip fraction d, in each receiver channel [5]. Using E2, E1, P, L1,
and L2 to denote such correlators, the correlation functions
obtained at their output are given by :
HRC
The idea behind the HRC is to form a linear combination of
correlator outputs that yields a net correlation function that is
much narrower than the usual C/A code autocorrelation function.
The synthesized prompt, early, and late correlations of the
HRC as well as its early-minus-late correlation are:
HRC
The code tracking loop uses the dot-product discriminator:
where the I and Q denote the in-phase and quadrature components
of the corresponding correlator. Although theoretically one can use
the correlator PHRC to do carrier phase tracking in order to mitigate
the carrier phase multipath errors, the relevant reduction of signal
power in PHRC will degrade the carrier tracking performance,
which makes it impractical in applications. Thus, in the following
tests, we still use the normal prompt correlator P to do the carrier
tracking, which implies that the HRC in this paper cannot mitigate
the carrier phase multipath error.
MMT
The MMT is a multipath estimation method based on ML [8]. The
complex baseband signal model for the MMT method is given by
the I component si(t) and the Q component sq(t) respectively
where Ak, μk, and τk are respectively the amplitude, carrier phase,
and code phase delay of kth component signal; c(t) is the C/A code
sequence. The index 0 denotes LOS signal. ni(t) and nq(t) are
independent, zero-mean Gaussian noise processes with uniform
power spectral density. Here, we assume that only one multipath
signal exists, but it is straightforward to derive the signal model for
more complex multipath scenarios.
MMT
MMT
As shown MMT simplifies the ML method by reducing the
parameter estimation from six to two, thereby greatly decreasing
computational costs. For each set of τ ˆ0 and τ ˆ 1, the values of a,
b, c, d can be solved by (7), and the corresponding ¡ is also
calculated by (6).
Those values corresponding to minimal τ are the ML estimates.
The MMT formulas shown above are dependent on the assumed
number of component signals, but the number of multipaths is at
first unknown.
MMT
A commonly used method is to use a correlator bank to get a
detailed cross-correlation shape and to start with assuming that
there is no multipath in the incoming signal. The cross-correlation
shape is then compared with an ideal clean correlation shape. If
the residual error exceeds a threshold, it means that it is very
likely that there is multipath in the signal, and the one-multipath
model will be used to derive the parameters.
If the minimal residual error under this model is still greater than
the threshold, a higher degree model has to be adopted.
CADLL
The CADLL architecture was initially designed only for code
phase multipath mitigation [12]. Its enhanced version (ECADLL)
[13] extends mitigating multipath to carrier phase measurements.
Nevertheless, we refer to both methods as CADLL in this paper,
although the enhanced architecture is used in the following tests.
Its block diagram is shown in Fig. 1.
CADLL is composed of several parallel tracking units, each
tracking a component signal. The estimated multipath signals are
subtracted from the total input signal to reduce the error caused
by multipath in LOS signal.
CADLL
Inside each tracking unit a normal code delay lock loop (DLL) with
wide or narrow spacing is used to track the code phase, and two
amplitude lock loops (ALL) are used to track the amplitudes of I and
Q components of the signal.
The ALL is one of the key features of CADLL, which is composed
of an estimator, a loop filter, and an integrator. After the incoming
satellite signal is wiped off of the carrier frequency, cancelled out by
the estimates of multipath signals, de-spread by local codes, and
correlated over T, the correlation values in both I and Q channels are
CADLL
where τe and μe are the code phase difference and carrier phase
difference, respectively, between the incoming signal and the
locally generated signal.
Rf(τ) is the cross-correlation function of CA code.
A is the amplitude of the incoming signal. Notice that A, here, is
the amplitude value of the baseband that has been filtered,
amplified, and quantized by the front-end of a receiver and not
the original amplitude of the signal-in-space. NI and NQ are
white Gaussian noise random variables with zero mean.
The DLL uses the correlation values in (8) with the
corresponding early and late correlation values to estimate the
code delay τe.
CADLL
A normalized dot-product code discriminator is adopted, and the
noise bandwidth of the DLL is 2 Hz for all the simulations in this
paper. Assuming that the code phase error τe is small, we get a
rough estimation about the amplitude value projected on the I
channel:
where Zn means the correlation value of the nth unit and ¸ is
damping factor used to adjust the estimation accuracy. As soon as
a rough estimation is obtained, it is filtered to reduce the noise
effect in order to get a more accurate estimation of the amplitude.
The filter is designed in a very similar way to the carrier
tracking loop filter.
CADLL
CADLL follows specially designed rules to estimate and track
multiple multipath sources. It starts the units one by one and uses
the tracking information of those already started units to initialize
the newly started unit. CADLL first uses a conventional tracking
loop to lock onto the incoming signal and gets a rough estimation
about the position of LOS’ code phase; then, it activates one more
unit to try to track a multipath signal. If it fails it means there is no
multipath in the incoming signal; if it succeeds, it will continue
trying to insert a new unit into this feedback loop to look for a new
multipath component. The monitor block is governing the process
of searching a new multipath component by checking the tracking
results of the new unit.
CADLL
If it is considered that there is no new multipath component, then
the trial unit will be shut down by the monitor block.
The process will not stop until there is no new multipath found or
until the number of enabled units reaches the maximum number
MM, which is predefined according to available resources.
Following this specially designed working procedure, CADLL is
more efficient in searching and locating new multipath signals
and is able to adjust its structure to match the number of
multipaths. In practice the strongest multipath signal is usually
the first to be identified and tracked, and then the next strongest
one follows. The detailed principle about CADLL can be found in
[12].
Experimental Results
In order to compare the performance among these three techniques, a
good criterion should be adopted. Considering the estimation-based
nature of MMT or CADLL, the root mean square error (RMSE)
defined as
where var(xˆ) is the variance of the estimates and B(xˆ) is the
estimator bias, naturally a good criterion. The RMSE embeds both
the noise deviation and the estimation bias, so it can better reflect the
estimator performance. An acceptable multipath performance
assessment measure is the multipath RMSE envelope [14].
Experimental Results
In this paper the mean values of code phase RMSE of the inphase (0± phase shift) and the out-of-phase (180± phase shift)
multipath components are used to compute the code multipath
error envelope.
correspondingly, the mean value of the carrier phase RMSE of
the 90± phase shift and the τ90± phase shift multipath
components are used to compute the carrier multipath error
envelope.
Experimental Results
Experimental Results
Conclusion 1
In this paper we evaluate two widely used multipath mitigation
techniques, HRC and MMT, against a recently proposed technique,
CADLL. Their performances are compared under two different CNRs,
60 dB-Hz and 40 dB-Hz, two different multipath signal strengths,
attenuated 6 dB or stronger 3.5 dB with respect to LOS, and two
scenarios with different numbers of multipath signals. It is shown that
MMT is sensitive to noise and needs a long integration time to provide
trustable results, and therefore, it cannot work with the dynamic
multipath scenario. It is illustrated that error and also fails when the
multipath is stronger than LOS. However, CADLL can provide reliable
estimation for the number of multipath signals and their parameters by
using only 10 ms integration time.
Conclusion 2
It outperforms the other two methods consistently in terms of
either RMSE or mean error and is able to track dynamic
multipath signals whose parameters are time varying with respect
to LOS.
Finally, we use a statistical multipath urban model to test the
performance of the three methods in severe multipath
environments. It is demonstrated that CADLL has the smallest
RMSE and that the error is nearly constant for different numbers
of multipath signals.
Conclusion 3
The performance evaluations are also conducted and validated
using hardware simulator generated satellite signals. However, in
some scenarios like indoor or certain urban cases, the CNR might
be very weak (below 30 dB-Hz).
In these cases CADLL has difficulties with estimating multipath.
Working properly in these scenarios is a challenging task for
CADLL and will be a future research topic.
References 1

[1] Van Dierendonck, A. J., Fenton, P., and Ford, T. Theory and performance of narrow
correlator spacing in a GPS receiver. NAVIGATION: Journal of the Institute of
Navigation, 39, 3 (Fall 1992), 265—283.

[2] Garin, L., Van Diggelen, F., and Rousseau, J-M. Strobe and edge correlator multipath
mitigation for code. Proceedings of the 9th International Technical Meeting of
the Satellite Division of The Institute of Navigation (ION GPS 1996), Kansas City, MO,
Sept. 17—20, 1996, pp. 657—664.

[3] Braasch, M. S.Performance comparison of multipath mitigating receiver architectures.
Proceedings of the 2001 IEEE Aerospace Conference, vol. 3, Big Sky, MT, Mar. 10—17,
2001, pp. 3/1309—3/1315.

[4] Kanekal, S. M. and Braasch, M. S. Multipath mitigation with gated signal technology.
Proceedings of the 54th Annual Meeting of the Institute of Navigation, Denver, CO, June
1—3, 1998, pp. 535—542.

[5] McGraw, G. A. and Braasch, M. S. GNSS multipath mitigation using gated and high
resolution correlator concepts. Proceedings of the National Technical Meeting of The
Institute of Navigation (ION NTM 1999), San Diego, CA, Jan. 25—27, 1999, pp. 333—
342.
References 2

[6] Van Nee, R. D. J. The multipath estimating delay lock loop. Proceedings of the IEEE
2nd International Symposium on Spread Spectrum Techniques and Applications,
Yokohama, Japan, Nov. 29—Dec. 2, 1992, pp. 39—42.

[7] Van Nee, R. D. J., et al. The multipath estimation delay lock loop: Approaching
theoretical accuracy limits. Proceedings of the IEEE Position Location and Navigation
Symposium (PLANS 94), Las Vegas, NV, Apr. 11—15, 1994, pp. 246—251.

[8] Weill, L. R. Multipath mitigation using modernized GPS signals: How good can it get?
Proceedings of the 15th International Technical Meeting of the Satellite Division of The
Institute of Navigation (ION GPS 2002), Portland OR, Sept. 24—27, 2002, pp. 493—505.

[9] Jones, J. and Fenton, P. C. The theory and performance of NovAtel Inc.’s vision
correlator. Proceedings of the 18th International Technical Meeting of the Satellite
Division of The Institute of Navigation (ION GNSS 2005), Long Beach, CA, Sept. 13—
16, 2005, pp. 2178—2186.

[10] Lohan, E. S., Lakhzouri, A., and Renfors, M. Feedforward delay estimators in
adverse multipath propagation for Galileo and modernized GPS signals. EURASIP
Journal on Advances in Applied Signal Processing, 2006 (May 2006), 1—19.
References 3

[11] Li, X. and Pahlavan, K. Super-resolution TOA estimation with diversity for indoor
geolocation. IEEE Transactions on Wireless Communications, 3, 1 (Jan. 2004), 224—234.

[12] Chen, X., et al. Turbo architecture for multipath mitigation in Global Navigation
Satellite System receivers. IET Radar, Sonar & Navigation, 5, 5 (2011), 517—527.

[13] Chen, X. and Dovis, F. Enhanced CADLL structure for multipath mitigation in urban
scenarios. Proceedings of the 2011 International Technical Meeting of The Institute of
Navigation (ION ITM 2011), San Diego, CA, Jan. 24—26, 2011, pp. 678—686.