IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 49, NO. 3 JULY 2013

Student : YuanHao Cheng

Comparative Studies of GPS Multipath Mitigation Methods Performance.

Outline Introduction describes the three techniques:

HRC MMT CADLL

Experimental results Conclusion References

AbstractCoupled 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.

HRCThe 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 :

HRCThe 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 theHRC as well as its early-minus-late correlation are:

HRCThe 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 carriertracking, which implies that the HRC in this paper cannot mitigate the carrier phase multipath error.

MMTThe 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 uniformpower 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

MMTAs 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.

MMTA 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.

CADLLThe 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.

CADLLInside 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

CADLLwhere 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.

CADLLA 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 carriertracking loop filter.

CADLLCADLL 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 trackingloop 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.

CADLLIf 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 ResultsIn 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 theestimator performance. An acceptable multipath performance assessment measure is the multipath RMSE envelope [14].

Experimental ResultsIn this paper the mean values of code phase RMSE of the in-phase (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 1In 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 reliableestimation for the number of multipath signals and their parameters by using only 10 ms integration time.

Conclusion 2It 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 3The 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.

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