Treatment of the Climate Change and International Security Discourse in Latin American and the Caribbean: A Reluctant Approach

The correlation process is at the base of the GNSS receiver processing. First of all, a correlator output model is given and some notations that are used in the continuation of this manuscript are introduced.

Then, correlation functions that are derived by GNSS receivers are presented taking into account correlation function distortions induced by the analog section of the receiver. Finally, some important correlation function properties are given.

3.3.1.1 Correlator output model

To extract information from the GNSS signal buried in the noisy RF front-end output, the receiver has to perform a correlation operation between the received signal and two local copies of the GNSS signal of interest (at least a copy of its PRN code and carrier, which are known by the receiver). One copy of the signal represents the in-phase component (𝑠𝑙𝑜𝑐𝑎𝑙_𝐼(𝑡)) and the other one the quadrature-phase component (𝑠_{𝑙𝑜𝑐𝑎𝑙_𝑄}(𝑡)).

𝑠𝑙𝑜𝑐𝑎𝑙_𝐼(𝑡) = 𝑐(𝑡 − 𝜏̂) 𝑐𝑜𝑠 (2𝜋𝑡𝑓_𝐼𝐹+ 𝜙̂(𝑡)) (3-31)

3.3 Digital processing of GNSS receiver

73 where

- 𝜏̂ is the incoming signal group delay estimated by the receiver.

- 𝜙̂(𝑡) is the incoming signal carrier phase delay estimated by the receiver.

The correlation operation is generally performed over one or an integer multiple of the PRN code period. The resulting correlation function 𝑅 (considering only the in-phase local replica) can be expressed as:

- 𝑇_𝑖𝑛𝑡 is the coherent integration duration in second.

- 𝑠̃^∗(𝑡) is the conjugate of the signal at the output of the GNSS receiver analog section.

- 𝜏̂ is the delay of the local PRN code in second.

- 𝜙̂(𝑡) is the phase of the local carrier (that can evolve over time) in radian.

- 𝜀_𝜏 is the difference between the incoming signal and the local code replica delay in second.

- 𝜀_𝜑 is the difference between the incoming signal and the local phase replica in radian.

This processing thus takes advantage of the correlation properties of the spreading codes used by GNSS signals. GNSS spreading code families have two main properties:

- Poor cross-correlation: the correlation function between any two different PRN codes is very small whatever the delay between the two signals is.

- Strong autocorrelation: the correlation between a PRN code and a local replica of itself is maximum when they are synchronised and is very low when they are not synchronized within one chip (the autocorrelation functions of the signals of interest are provided in 3.1).

In addition, the correlation process permits to accumulate the power of the data bit over the coherent integration duration, while it averages out all the other signals that are not containing the PRN code (such as the noise or interference). The consequence is such that the correlator output of the useful signal should dominate the noise (when the local replica and the incoming signal are synchronized).

A simplified model for the in-phase correlator output affected by thermal noise (assuming a constant code and carrier Doppler during the coherent integration duration and considering that the correlation is realized within one data bit) is given by [Julien, 2006]:

where

- 𝑅_𝑠̃ is the correlation function between the local replica and the received signal code (which is an element of the received signal). Note that it takes into account the effect on the incoming

𝑠_{𝑙𝑜𝑐𝑎𝑙_𝑄}(𝑡) = 𝑐(𝑡 − 𝜏̂) 𝑠𝑖𝑛(2𝜋𝑡𝑓_𝐼𝐹+ 𝜙̂(𝑡)) (3-32)

74

signal of the propagation channel, the antenna and the RF front-end equivalent filter. The expression of filtered correlation function is given i n appendix A.

- 𝑛_𝐼 is the noise at the in-phase correlator output. This noise is assumed Gaussian and its power is derived in appendix A taking into account the antenna and RF front-end equivalent filter.

- 𝐴 is the amplitude of the received signal at the receiver input.

- 𝐷 is the sign of the incoming signal navigation data bit (if any) during the correlation operation.

- 𝜀_𝜏 is the code delay difference between the local replica and the received signal in second.

- 𝜀𝜑 is the carrier phase difference between the local replica and the received signal. It corresponds to the carrier phase difference in the middle of the correlation interval in radian.

- 𝜀_𝑓 is the Doppler difference between the local replica and the received signal in hertz.

From the previous model (equation (3-34)), it can be seen that the correlator output will dominate the noise only if 𝜀𝜏, 𝜀𝜑 and 𝜀𝑓 are close to zero. There is thus a need to synchronize the local replica generated by the receiver with the incoming signal of interest.

All GNSS receivers also use the so-called quadrature-phase correlator outputs obtained from the correlation of the incoming signal with a local replica of the signal quadrature-phased component (𝑠𝑙𝑜𝑐𝑎𝑙_𝑄(𝑡)). The model of the quadrature-phase component is given by:

Where 𝑛_𝑄 is the noise at the quadrature-phase correlator output. This noise is assumed Gaussian and is independent from 𝑛𝐼. It has the same power as 𝑛𝐼.

3.3.1.2 Correlator output model function of the code delay

𝐼 and 𝑄 are dependent upon the code delay error, the carrier phase error and the Doppler error.

Nevertheless in signal processing, correlator outputs are also estimated for code delay errors different from zero. Assuming that 𝜀𝜑= 0 and 𝜀_𝑓 = 0, the following notations will be used in the continuation.

Figure 3-9 gives an illustration of the normalized code correlation function on a BPSK code sequence of seven chips. It corresponds to a simplified configuration where 𝜀_𝜑= 0 and 𝜀_𝑓= 0. Three cases are represented, on the top the local replica is in advance compared to the incoming signal (𝜀_𝜏= − 𝑇_𝑐⁄ ). 2 On the middle, both signals are synchronized. On the bottom, the local replica is delayed compared to the incoming signal (𝜀_𝜏= 𝑇_𝑐⁄ ). The blue point on correlation function corresponds to the correlator 2 output given by the convolution of the two represented signals.

𝑄(𝜀_𝜏,𝜀_𝜑, 𝜀_𝑓) = 𝐴𝐷𝑅_𝑠(𝜀_𝜏)𝑠𝑖𝑛 (𝜋𝜀_𝑓𝑇_𝑖𝑛𝑡)

𝜋𝜀_𝑓𝑇_𝑖𝑛𝑡 𝑠𝑖𝑛(𝜀_𝜑) + 𝑛_𝑄 (3-35)

𝐼_𝜀𝜏= 𝐼(𝜀_𝜏, 0,0) = 𝐴𝐷𝑅_𝑠(𝜀_𝜏) + 𝑛_𝐼 (3-36)

𝑄_𝜀𝜏= 𝑄(𝜀_𝜏,0,0) = 𝑛_𝑄 (3-37)

3.3 Digital processing of GNSS receiver

75

Figure 3-9. Illustration of the code correlation process for different code delays between the incoming signal and the local replica.

3.3.1.3 GNSS receiver correlation function

For GPS L1 C/A, GPS L5 and Galileo E5a signals, local replicas generated by the receiver have the same modulation as the incoming signals. By consequence, the correlation functions estimated by the GNSS receiver are defined by autocorrelation expressions presented in section 3.1. Nevertheless, in the same section, the expression of the 𝐶𝐵𝑂𝐶(6,1, 1 11⁄ , −) autocorrelation (𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)) is mathematically defined while civil aviation receivers will generate a 𝐵𝑂𝐶(1,1) instead of a

𝜀_𝜏= −^𝑇𝑐

2

Satellite code sequence

Receiver local replica code

sequence

−𝑇_𝑐 2

𝜀_𝜏 0

𝜀_𝜏 = 0

Satellite code sequence

Receiver local replica code

sequence

𝜀_𝜏 0

𝑇_𝑐 2

𝜀𝜏 0

𝜀_𝜏=^𝑇𝑐

2

Satellite code sequence

Receiver local replica code

sequence

76

𝐶𝐵𝑂𝐶(6,1, 1 11⁄ , −) local replica to simplify receivers signal generation. By consequence, civil aviation receivers will track Galileo E1C signals via a 𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)/𝐵𝑂𝐶(1,1) correlation function.

Even if it does not change significantly tracking performance, the difference of shape between 𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−) and 𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)/𝐵𝑂𝐶(1,1) has an influence on the impact of signal distortions on the tracking.

The correlation function of a 𝐵𝑂𝐶(1,1) and a 𝐶𝐵𝑂𝐶(6,1, 1 11⁄ , −)-modulated signals can be modeled as:

𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)/𝐵𝑂𝐶(1,1)(𝜏) = ((√10

11𝑠𝑐_{𝐵𝑂𝐶(1,1)}− √1

11𝑠𝑐_{𝐵𝑂𝐶(6,1)}) ∗ (𝑠𝑐_{𝐵𝑂𝐶(1,1)})^∗) (𝜏)

The expressions of 𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)/𝐵𝑂𝐶(1,1) and 𝑅𝐵𝑂𝐶(1,1)are given in section 3.1.5.

The difference between the 𝐶𝐵𝑂𝐶(6,1, 1 11⁄ , −) autocorrelation function in blue and the 𝐶𝐵𝑂𝐶(6,1, 1 11⁄ , −)/𝐵𝑂𝐶(1,1) correlation function in red is illustrated on Figure 3-10.

Figure 3-10. Correlation functions between a modulated signal and a CBOC(6,1,1/11)-modulated signal (blue) or a BOC(1,1)-CBOC(6,1,1/11)-modulated signal (red).

3.3.1.4 Filtered correlation functions

When the incoming signal goes through the analog section of the receiver, its characteristics are modified as defined in section 3.2. In particular, the equivalent filter of the antenna and the RF front-end induces distortions on the signal before the correlation function process. The consequence is that correlation functions are also distorted depending on the pre-correlation function characteristics.

Figure 3-11 gives an example of correlation function shapes after applying a 6^th-order Butterworth pre-correlation filter of 24 MHz (in red) and 12 MHz (in blue) double-sided. Correlation functions are delayed because of the filtering effect and the delay is higher when the filter bandwidth is lower.

𝑅𝐶𝐵𝑂𝐶(6,1,1 11⁄ ,−)/𝐵𝑂𝐶(1,1)(𝜏)

= √10

11𝑅_{𝐵𝑂𝐶(1,1)}(𝜏) − √1

11𝑅𝐵𝑂𝐶(1,1)/𝐵𝑂𝐶(6,1)(𝜏) (3-38)

3.3 Digital processing of GNSS receiver

77

Nevertheless, in Figure 3-11, the delay is not represented to underline difference that appears on the shape of the correlation function because of the filter.

Figure 3-11. Correlation functions for different pre-correlation filters.

3.3.1.5 Correlation function and filtering properties

Taking back notations introduced earlier in the document:

- 𝑠(𝑡) is the nominal signal at the antenna output and at the input of the RF front-end.

- 𝑠_{𝑙𝑜𝑐𝑎𝑙}(𝑡) is the local replica.

Considering that a distortion affects the signal, new notations are introduced:

- 𝑠𝑑(𝑡) = 𝑠(𝑡) + 𝑑(𝑡) is the distorted signal at the antenna output and equivalently at the input of the RF front-end.

- 𝑑(𝑡) is the distortion affecting the temporal signal, represented as an additive component.

Due to the linearity property of the convolution product, the filtered distorted signal 𝑠̃𝑑(𝑡) can be rewritten as:

where

- 𝑑̃(𝑡) = (ℎ_𝑅𝐹∗ 𝑑^∗)(𝑡) is the signal distortion affecting the temporal signal filtered by the RF front-end.

- 𝑠̃(𝑡) = (ℎ_𝑅𝐹∗ 𝑠^∗)(𝑡) is the nominal signal filtered by the RF front-end.

- ℎ_𝑅𝐹(𝑡) is the RF front-end filter impulse response.

The linearity property of the convolution product entails that the correlation function of 𝑠̃𝑑(𝑡) with the local replica is equal to:

where

- 𝑅_𝑠̃(𝜏) = (𝑠̃ ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ )(𝜏) is the correlation function between the filtered nominal signal and the local replica.

𝑠̃_𝑑(𝑡) = (ℎ_𝑅𝐹∗ 𝑠_𝑑^∗)(𝑡) = (ℎ_𝑅𝐹∗ (𝑠^∗+ 𝑑^∗))(𝑡) = 𝑠̃(𝑡) + 𝑑̃(𝑡) (3-39)

𝑅_𝑠̃𝑑(𝜏) = 𝑅_𝑠̃(𝜏) + 𝑅_𝑑̃(𝜏) (3-40)

78

- 𝑅_𝑑̃(𝜏) = (𝑑̃ ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ )(𝜏) is the correlation function between the filtered distortion and the local replica.

It is noticeable that the associative property of the convolution product leads to:

Where 𝑅𝑠(𝜏) = (𝑠 ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ )(𝜏) is the correlation function between the unfiltered nominal signal and the local replica.

The three conclusions of these mathematical expressions are that:

- It is equivalent to apply a filter on a distorted signal or to apply the filter on the ideal signal and the distortion separately. (Equation (3-39))

- It is equivalent to apply a filter on a distorted correlation function or to apply the filter on the ideal correlation function and the distortion convolved with the local replica separately.

(Equation (3-40))

- It is equivalent to apply a filter on the incoming signal or to apply it on the correlation function.

(Equation (3-41))

3.3.2 Acquisition

The acquisition consists in determining if a given GNSS signal is visible and can be processed by the receiver. The only way to do so is to try to see if a local replica of this signal can correlate appropriately with it. Based on the analysis of the correlator outputs, if such a correlation occurs, it should also provide the receiver with a rough estimation of the GNSS signal delay and Doppler.

The typical acquisition detector does not need a local replica synchronized in phase:

where 𝐾 referred to as the number of non-coherent summations.

The acquisition stage is thus accomplished sequentially by testing all possible combinations of code, delay and Doppler that the incoming signal of interest can take. This is thus equivalent to use discrete bins on a grid of code delays and Doppler frequencies, all these bins representing a 2-D grid. For each bin of the so-called acquisition grid, a correlation is performed between the incoming signal and a local replica with a delay and a Doppler frequency corresponding to that bin. During acquisition, if the receiver does not have the knowledge about satellites in view, and by consequence does not know incoming signal code waveform, all possible PRN codes are tested.

The typical size for a bin is a fraction (one half) of a chip (to have a bin hitting a high part of the PRN code correlation function peak) on the code delay axis and about (2/3)𝑇𝑖𝑛𝑡 on the Doppler frequency axis [Kaplan and Hegarty, 2006].

𝑅_𝑠̃(𝜏) = (𝑠̃ ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ )(𝜏) = ((ℎ_𝑅𝐹∗ 𝑠) ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ )(𝜏) = (ℎ_𝑅𝐹∗ (𝑠 ∗ 𝑠_{𝑙𝑜𝑐𝑎𝑙}^∗ ))(𝜏)

= (ℎ_𝑅𝐹∗ 𝑅_𝑠)(𝜏) = 𝑅̃(𝜏) _𝑠 (3-41)

𝑇 = ∑(𝐼²(𝑘) + 𝑄²(𝑘))

𝐾

𝑘=0

(3-42)

3.3 Digital processing of GNSS receiver

79

Figure 3-12. Example of GPS L1 C/A acquisition grids. On the left, a signal is acquired, whereas on the right no signal is found by the receiver.

Figure 3-12 gives an example of two acquisition grids for a GPS L1 C/A signal. On the left, the acquisition is successful. The estimated Doppler frequency is equal to 1500 Hz and the estimated code delay is equal to 185 chips.

It is noticeable that the code delay swept 1023 chips and 10 000 Hz of Doppler frequencies.

Considering a code delay resolution of half a chip and a Doppler frequency resolution equal to 100 Hz, the number of correlations to perform for each PRN is equal to 2046 × 10000/100 = 204 600 . Acquisition is generally a relatively long and cumbersome process.

Different algorithms exist to find out the Doppler frequency and the code delay and run through the correlation grid more rapidly as described in [Kaplan and Hegarty, 2006]. Nevertheless no such technique is used in the context of this study.

3.3.3 Tracking

After being acquired, GNSS signals are tracked by the receiver. Tracking means that the receiver attempts to generate a local replica that follows the parameters of the incoming signal. During this phase, the code delay and the carrier phase of the incoming signal are estimated more precisely than at the output of the acquisition phase, generally using feedback loops known as Phase Lock Loop (PLL) for carrier phase tracking and Delay Lock Loop (DLL) for code delay tracking.

Also, the process of tracking is much less demanding in processing power than that of the acquisition.

Both code delay and carrier phase parameters are evaluated continuously until a loss of tracking. Like the acquisition, tracking is based on the correlation process but the two approaches are different.

To understand a feedback loop, three important principles have to be introduced:

- Discriminator functions: discriminators use the correlator outputs to provide measurable values of code delay and carrier phase tracking errors. Different discriminators with different characteristics can be used to track the code delay and the carrier phase.

- Numerical Controlled Oscillator (NCO): it converts the filtered discriminator output into a frequency that drives the generation of the local replica. One NCO is used to generate the PRN code and another one to generate the carrier.

80

- Loop filters: the discriminator outputs are filtered to reduce the noise at the input of the NCO.

Note that the level of filtering influences the reaction time of the loop. Note also that the filter order influences the ability of the loop to react to parameters dynamic.

In the following, delay lock loop and phase lock loop are presented separately. In GNSS receiver, both tracking loops are used jointly. Note that instead of tracking the carrier phase of the incoming signal, it is possible to track its carrier frequency using a Frequency Lock Loop (FLL). FLL concepts are not detailed but information can be found for example in [Navipedia, 2015] or [Kaplan and Hegarty, 2006].

In this section, the second order loop filter that was adopted to process some GNSS signals is described to show mathematically the impact of different tracking parameters on the NCO input.

3.3.3.1 Delay lock loop (DLL)

In this part, the DLL concept is exposed. Then, performance of the DLL is assessed and parameters with an influence on this performance are listed. DLL concept and performance are of interest because it conditions GNSS receiver performance.

3.3.3.1.1 DLL concept

A DLL is a feedback loop aiming to keep the code-phase of the local replica aligned with the incoming signal. The general structure of a DLL is shown in Figure 3-13.

Figure 3-13. General structure of a DLL. Dashed block is outside the DLL.

It is important to notice that several correlator outputs are fed into the discriminator. In general, three PRN code local replicas are used by the receiver (considering one signal component): an early, a prompt, and a late replica. In ideal tracking conditions, the prompt PRN code replica is synchronized with the PRN code of the incoming signal, the early PRN code replica has an advance, noted 𝐶𝑠⁄ , 2

I&D

3.3 Digital processing of GNSS receiver

81

compared to the PRN code of the incoming signal and the late PRN code replica is delayed by 𝐶𝑠⁄ 2 compared to the PRN code of the incoming signal. 𝐶_𝑠 is known as the correlator spacing, or Early-Late spacing, and corresponds to the time delay between the two early and late PRN code local replicas used for the tracking. The mathematical definitions of replicas delivered by the code generator are function of the estimated code delay 𝜏̂ and are defined as:

- 𝑐 (𝑡 − 𝜏̂ +^𝐶𝑠

2) for the late replica, - 𝑐(𝑡 − 𝜏̂) for the prompt replica, - 𝑐 (𝑡 − 𝜏̂ −^𝐶𝑠

2) for the early replica.

The most common discriminators are the non-coherent Early Minus Late Power (EMLP) discriminator and the quasi-coherent Dot Product (DP). The two discriminators are not considered as coherent because they are insensitive to carrier phase error, which is interesting for a tracking robustness point of view. These two discriminators are defined by:

where

A DLL aims at achieving a zero code delay tracking error. The goal of the discriminator is thus to provide an unbiased estimation of the actual code delay error so that the loop can react accordingly (to do so, the aforementioned discriminators need to be normalized). In ideal condition, perfect synchronization is achieved when the Early and Late correlator outputs are levelled. Some discriminator outputs are shown in Figure 3-14 for GPS L1 C/A as a function of the code delay tracking error at the input of the discriminator. In the example given in Figure 3-14, the discriminator functions are estimated from a normalized unfiltered ideal correlation function and the correlator spacing is equal to 𝐶_𝑠= 0.2 chip.These plots are also called S-curves.

The S-curve represents the discriminator output. As a consequence, a zero-crossing of the S-curve represents a point at which the tracking loop can be locked. It is noticeable on Figure 3-14 that only a stable S-curve zero-crossing can be locked: it corresponds to a S-curve zero-crossing surrounded by a negative value on the left and a positive value on the right. On these plots, it is assumed that the carrier phase tracking error is equal to zero. As a consequence, all terms on the quadrature component are equal to zero (no distortion on the in-phase and/or the quadrature-phase signal components).

𝐷_{𝐸𝑀𝐿𝑃}= (𝐼_𝐸²+ 𝑄_𝐸²) − (𝐼_𝐿²+ 𝑄_𝐿²) (3-43) 𝐷_𝐷𝑃= (𝐼_𝐸− 𝐼_𝐿)𝐼_𝑃+ (𝑄_𝐸− 𝑄_𝐿)𝑄_𝑃

(3-44)

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Figure 3-14. Examples of S-curves for an unfiltered GPS L1 C/A signal and different DLL discriminators, 𝐶𝑆 = 0.2 chip. In blue is the early minus late discriminator, in orange the DP discriminator, in yellow

the EMLP and in purple the early minus late normalized by the early plus late.

The important part of the DLL discriminator outputs is the linear part around the code delay error equal to zero. Indeed, if a code delay error is affecting the DLL in the discriminator linear section range, a NCO command proportional to the error will be generated by the discriminator output, and the error will be corrected in the next loop iteration.

The choice of the discriminator has an impact on the S-curve shape and on performance of the DLL that is described in the next part.

3.3.3.1.2 DLL performance

Classical DLL performance is dependent upon three errors:

- the thermal noise, - multipath and

- the dynamic stress error.

In this section, only the main source of carrier tracking error is treated: the thermal noise. It is assumed that no signal distortion and multipath affect the incoming signal. Details about other sources of errors can be found as example in [Julien, 2006]. Using a non-coherent EMLP discriminator, assuming a perfect normalization, no frequency uncertainty in the carrier wipe-off, a RF front-end filter with unity gain within ± 𝐵𝑓𝑒⁄ Hz and null elsewhere and a code delay error remaining small, the standard 2 deviation of the DLL tracking error due to noise is given in meter by [Betz and Kolodziejski, 2000]:

3.3 Digital processing of GNSS receiver

83

In the same way and in same conditions, the DLL tracking error standard deviation can be estimated for a DP discriminator and is equal to [Julien, 2006]:

where

- 𝑇_𝑐 is the chips period in second.

- 𝑇_𝑖𝑛𝑡 is the coherent integration time in second.

- 𝑆 is the power spectral density of the signal at the receiver antenna (which depends upon the modulation type), normalized to unit area over infinite bandwidth.

- 𝐶′ 𝑁⁄ ₀ is the carrier to noise ratio in hertz.

- 𝐵𝑓𝑒 is the double-sided font-end bandwidth in hertz.

- 𝐶_𝑠 is the early to late correlator spacing in chip.

- 𝐵_𝐷𝐿𝐿 is the code loop noise bandwidth in hertz.

It is important to notice that DLL performance is dependent upon several receiver parameters that are:

- The order of the DLL. It has an impact on the dynamic stress error. The higher the order of the

- The order of the DLL. It has an impact on the dynamic stress error. The higher the order of the

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