BeiDou Satellite-Based Augmentation System Algorithm Optimization and Performance Validation of Ionospheric Degradation Parameters with RTCA Protocol

Abstract

The BeiDou Satellite-Based Augmentation System (BDSBAS), based on the Radio Technical Commission for Aeronautics (RTCA) protocol, aims to provide high-precision, single-frequency positioning with integrity assurance for civil aviation users in China and surrounding regions. Given the anticipated high solar activity between 2023 and 2025, ionospheric anomalies may degrade positioning accuracy and significantly impact BDSBAS integrity performance. To enhance BDSBAS integrity, this study evaluates and analyzes the system’s ionospheric degradation parameters for 2023. The results indicate that during the active ionospheric period in 2023, the rate of ionospheric grid delay changes exceeding the limits of the currently broadcasted parameters increased by 0.86%, posing potential integrity risks compared to 2022. To address this issue, we propose a novel algorithm for ionospheric degradation parameters and assess its applicability, stability, and effectiveness using BDSBAS single-frequency service message data from IGS monitoring stations in China. Statistical analysis in the localization domain demonstrates that the new method reduces the rate of ionospheric degradation parameters exceeding the threshold by 1.10% in 2023–2024. This approach significantly enhances BDSBAS integrity service capabilities, supporting its performance improvement and official deployment.

1. Introduction

With the widespread adoption of Global Navigation Satellite Systems (GNSS), many countries and regions have established satellite-based augmentation systems (SBAS) to meet higher demands for positioning accuracy and integrity [1,2]. Currently, ten SBAS are either operational or under construction worldwide, including, as follows: the Wide Area Augmentation System (WAAS) in the United States; the European Geostationary Navigation Overlay System (EGNOS) in Europe; the Multi-functional Transport Satellite-based Augmentation System (MSAS) in Japan; the GPS Aided Geo Augmented Navigation (GAGAN) in India; the System for Differential Correction and Monitoring (SDCM) in Russia; the BeiDou Satellite-Based Augmentation System (BDSBAS) in China; the SBAS for Africa and the Indian Ocean (A-SBAS); the Algerian Satellite-Based Augmentation System (AL-SBAS); the Southern Positioning Augmentation Network (SPAN) in Australia and New Zealand; and the Korea Augmentation Satellite System (KASS) in Korea [3]. Among these, WAAS, EGNOS, MSAS, and GAGAN have officially launched RTCA single-frequency services, while BDSBAS, SDCM, A-SBAS, AL-SBAS, SPAN, and KASS are currently in testing or trial operations.

BDSBAS broadcasts comprehensive correction information, including differential corrections for satellite orbits and clocks, ionospheric grid corrections, and integrity data such as User Differential Range Error (UDRE), Grid Ionospheric Vertical Error (GIVE), and degradation parameters [4,5]. These parameters ensure the timeliness and accuracy of differential corrections in both spatial and temporal domains, particularly benefiting users experiencing correction delays or who are operating at the service area’s edge [6]. This information is transmitted via three geosynchronous pseudo-random noise satellites (PRN 130, PRN 143, and PRN 144) to augment GPS signals and provide APV-I level services in China and the surrounding regions, with service indicators detailed in

Table 1. Signal-in-space performance requirements for SBAS on APV-I.

Flight Operation	Horizontal/Vertical Accuracy (95%)	Integrity Level	HPL/VPL	Alarm Time	Continuity	Availability
APV-I	16 m/20 m	2 × 10−7/approach	40 m/50 m	10 s	8 × 10−6/15 s	0.99~0.99999

[7,8].

BDSBAS message type 10 retains the spatiotemporal characteristics of the most recent effective differential information, minimizing service degradation even if differential correction data are lost.

Table 2. Message type 10 detailed content.

Parameter	Effective Range	Units	Parameter	Effective Range	Units
$B_{rrc}$	0–2.046	m	$I_{geo}$	0–511	s
$C_{ltc\_lsb}$	0–2.046	m	$C_{er}$	0–31.5	m
$C_{ltc\_v1}$	0–0.05115	m/s	$C_{iono\_step}$	0–1.023	m
$I_{ltc\_v1}$	0–511	s	$I_{iono}$	0–511	s
$C_{ltc\_v0}$	0–2.046	m	$C_{iono\_ramp}$	0–0.005155	m/s
$I_{ltc\_v0}$	0–511	s	$RS{S}_{UDRE}$	0–1	unitless
$C_{geo\_lsb}$	0–0.5115	m	$RS{S}_{iono}$	0–1	unitless
$C_{geo\_v}$	0–0.05115	m/s	$C_{covariance}$	0–12.7	unitless

provides a detailed description of the efficacy degradation parameters [9], which are categorized by function into the following four groups: (1) fast-correction degradation parameters, encompassing category $B_{rrc}$; (2) long-term correction degradation parameters, including variation limit $C_{ltc\_v0}$ and update period $I_{ltc\_v0}$, etc.; (3) geosynchronous orbit (GEO) message degradation parameters, consisting of categories update period $I_{ltc\_v0}$, variation limit $C_{geo\_lsb}$ and variable speed limit $C_{geo\_v}$; and (4) ionospheric degradation parameters are divided into variation limit $C_{iono\_step}$, update period $I_{iono}$, and variable speed limit $C_{iono\_ramp}$. In addition, $RS{S}_{iono}$ and $RS{S}_{UDRE}$ are, respectively, the root sum square flag and user differential range error flag, and $C_{covariance}$ is equivalent to the residual variance.

Ionospheric degradation parameters define the change limits of vertical delays at Ionospheric Grid Points (IGPs) broadcast via message type 26 and are disseminated through message type 10. The parameters are as follows: $C_{iono\_step}$ denotes the limit on the variation between successive ionospheric grid delay values; $I_{iono}$ indicates the interval between updates to ionospheric delay corrections; and $C_{iono\_ramp}$ defines the rate limit for changes in successive ionospheric grid delay values. $RS{S}_{iono}$ serves as a root sum square flag for the ionospheric degradation parameters. Currently, different SBAS broadcast ionospheric degradation parameters are constant values, as shown in

Table 3. Global SBAS ionospheric degradation parameters.

SYSTEM	${\mathit{C}}_{\mathit{i}\mathit{o}\mathit{n}\mathit{o}\_\mathit{s}\mathit{t}\mathit{e}\mathit{p}}$	${\mathit{I}}_{\mathit{i}\mathit{o}\mathit{n}\mathit{o}}$	${\mathit{C}}_{\mathit{i}\mathit{o}\mathit{n}\mathit{o}\_\mathit{r}\mathit{a}\mathit{m}\mathit{p}}$	$\mathit{R}\mathit{S}{\mathit{S}}_{\mathit{i}\mathit{o}\mathit{n}\mathit{o}}$
BDSBAS	0.952	240	0.000000	0
WAAS	0.836	300	0.000000	0
EGNOS	0.657	300	0.000000	0
SDCM	0	300	0.000740	1
MSAS	0.836	300	0.000000	0
GAGAN	0.952	300	0.000000	0
KASS	0.657	300	0.000000	0
SPAN	0.657	300	0.000000	0
ASBAS	0.657	300	0.000000	0

.

SBAS services aim to enhance the accuracy, integrity, availability, and continuity of GNSS positioning [10]. Integrity performance is a key reliability indicator, evaluated using Horizontal Protection Levels (HPL) and Vertical Protection Levels (VPL) derived from SBAS integrity messages. These protection levels quantify error bounds in positioning, ensuring navigation system integrity and safety.

Degradation parameters contribute to protection level calculations. If the computed HPL and VPL remain below the required Horizontal and Vertical Alert Limits (HAL and VAL) for a given service level, the service is deemed available; otherwise, it is considered unavailable. Additionally, if the user’s protection level falls below actual Horizontal and vertical Positioning Errors (HPE and VPE), an integrity risk arises. Thus, ionospheric degradation parameters are crucial for ensuring SBAS service reliability and availability [11,12].

In BDSBAS studies, service performance and integrity have received significant attention [13,14,15]; however, degradation parameters have been less explored. Ionospheric correction plays a vital role in achieving high-accuracy, single-frequency positioning [6,16,17]. Therefore, this study focuses on ionospheric degradation parameters from message type 10. Section 2.1 analyzes cases where BDSBAS ionospheric degradation parameters failed to provide effective envelopment during peak ionospheric activity, evaluating their impact on service performance. Section 2.2 identifies limitations in BDSBAS ionospheric degradation representation during high solar activity and proposes an improved algorithm. Section 3 details the experimental results validation of this new method and Section 4 discusses the impact of the new degradation parameters on positioning services.

2. Materials and Methods

2.1. Performance Analysis of Ionospheric Degradation Parameters

Ionospheric degradation parameters, defined as an integrity threshold, are designed to encapsulate and mitigate ionospheric delay errors. This capability ensures compliance with APV-I’s performance criteria for integrity services. The research presented in this paper indicates that with BDSBAS degradation parameters $C_{iono\_step}$ set at 0.952 and $C_{iono\_ramp}$ at 0, the probability of exceeding the threshold for BDSBAS remained relatively low in 2021. However, as ionospheric activity has increased in recent years, the probability of integrity parameter leakage has risen annually. The frequency of ionospheric variations $\Delta IGPdelay$ exceeding the threshold $I_{\lim it}$ has also increased. The threshold of ionospheric delay change is determined by the ionospheric degradation parameter in Equation (1), as follows:

$$I_{\lim it}=C_{iono\_step}+C_{iono\_ramp}*I_{iono}$$

(1)

The parameters $C_{iono\_step}$, $C_{iono\_ramp}$ and $I_{iono}$, in Equation (1) have different fixed values in different systems. The specific values are shown in Table 1. In the previous paragraph, $\Delta IGPdelay$ represents the difference in the same grid points delays between the two adjacent epochs, as follows:

$$\Delta IGPdelay={IGPdelay}_t-{IGPdelay}_{t-1}$$

(2)

The probability of ionospheric grid delay variations exceeding the threshold can be calculated by Equation (3), as follows:

$$P(\Delta IGPdelay>I_{\lim it})={\displaystyle \frac{{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{j=1}^J(\Delta IGPdela{y}_{t,j}>I_{\lim it})}}}{T*J}}$$

(3)

In Equation (3), $T$ is the total number of the epoch and $J$ is the total number of ionospheric grid points. $\Delta IGPdela{y}_{t,j}$ is the variation in ionospheric delay at the grid point $j$ on the epoch $t$.

This study selects a grid point at 15°N latitude and 100°E longitude, a region of high ionospheric activity, for detailed analysis. The ionospheric delay variation envelope for the first day of each year from 2021 to 2024 in BDSBAS is shown in Figure 1. The horizontal axis is in hours and the vertical axis is in meters, the blue dots represent the ionospheric delay variation between adjacent epochs at this grid point from 2021 to 2024, and the red line indicates the variation threshold $I_{\lim it}$. It is equal to $C_{iono\_step}$ because $C_{iono\_ramp}$ is the fixed value 0.

In 2021, the degradation parameter threshold nearly encompassed all ionospheric delay variations, as shown in Figure 1a. However, as ionospheric activity intensified in 2022, Figure 1b shows that the degradation parameter could no longer fully envelop the actual variation in ionospheric delay; the probability of exceeding the threshold increased further in 2023 and 2024, as shown in Figure 1c,d.

Table 4. The annual probability of exceeding the threshold probability in 2021–2024.

	2021	2022	2023	2024
BDSBAS	0.12%	0.78%	1.64%	1.96%
MSAS	0.54%	1.44%	2.42%	3.24%
EGNOS	0.15%	0.36%	0.38%	0.40%
WASS	0.02%	0.07%	0.16%	0.23%

provides more detailed data on the annual probability of exceeding the limits calculated by Equation (3) from 2021 to 2024 for BDSBAS, EGNOS, MSAS, and WAAS. The probability of exceeding the threshold for BDSBAS and MSAS has increased greatly, whereas EGNOS and WAAS have shown no significant rise. This discrepancy is likely due to the higher proportion of low-latitude regions in China, where ionospheric anomalies have a greater impact [18,19,20]. In contrast, EGNOS and WAAS operate in regions with fewer ionospheric anomalies [21].

The daily probability of exceeding the threshold for BDSBAS and MSAS from 2021 to 2024 is shown Figure 2. The vertical axis represents the probability of exceeding the threshold, while the horizontal axis indicates the number of days. The exceeding threshold rate of MSAS is generally higher than that of BDSBAS, with both showing synchronized variations. The probability of ionospheric changes in both BDSBAS and MSAS exceeding the degradation parameter threshold increases over time. This trend reflects the growing impact of increased solar activity on China and the surrounding region.

To further investigate the relationship between ionospheric anomalies and the exceeding threshold rate, as well as their connection to solar activity, a time-series analysis of the BDSBAS exceeding threshold rate and sunspot number (SSN) from 2021 to 2024 was conducted. In Figure 3, the daily exceeding threshold rate is plotted on the left vertical axis, and SSN is plotted on the right vertical axis, with the horizontal axis representing the number of days. SSN, derived from sunspot counts, is a widely used index for quantifying solar activity [22]. It is strongly correlated with most solar activity indicators and serves as a key driver of ionospheric variations [23].

The proportion of ionospheric delay variations exceeding the threshold has risen annually, as shown in Figure 3. The exceeding threshold rate of ionospheric degradation parameters correlates with ionospheric activity, which in turn is influenced by solar activity. While SSN and total electron content (TEC) exhibit a strong long-term correlation, their short-term correlation is weaker. Both SSN and the over-limit rate exhibit oscillatory increases over four years; however, local correlations vary due to factors such as geomagnetic variations [21,24]. Additionally, the solar activity index reveals hysteresis and saturation effects in TEC, where TEC values during the ascending and descending phases of the solar cycle differ despite similar levels of solar activity. Saturation implies that beyond a certain threshold, increasing solar activity does not proportionally increase TEC [25,26]. Overall, solar activity and ionospheric changes exhibit a strong correlation.

A statistical analysis of ionospheric grid points in the BDSBAS service area was conducted to examine the exceeding threshold probability across different latitude bands. Figure 4 illustrates the changes in exceeding the threshold daily probability from 2021 to 2024 for latitudes between 5° and 55°. The horizontal axis is in days, and the vertical axis shows the probability of exceeding the threshold. The changes in exceeding the threshold probability vary across latitude bands, with an overall cyclical upward trend displaying seasonal periodicity; it is especially conspicuous in low-latitude areas.

Figure 5 summarizes the relationship between latitude and the probability of exceeding the threshold. The horizontal axis is in latitudes and the vertical axis shows the probability of exceeding the threshold. The lowest probability occurs at the 40° latitude and increases toward the edges, with more significant issues in low-latitude areas. The ionosphere is particularly active below the 35° latitude, directly affecting ionospheric degradation parameters and, consequently, service availability and integrity. Therefore, during periods of ionospheric activity, its impact on the integrity in southern China is significantly heightened.

To assess the impact of ionospheric activity on service performance, BDSBAS service data from 2022 and 2023 were analyzed, focusing on enhanced positioning, protection levels, and service availability. The positioning performance and protection levels of selected IGS stations were evaluated using degradation parameters. The algorithm of the protection level refers to the RTCA [27]. Present service availability statistics for the WUH2 station from DOY (day of the year) 001–003 in 2022 are shown in Figure 6, showing the service availability of WUH2 station from DOY 001–003 in 2023, where blue represents the Horizontal Protection Level, yellow denotes the Horizontal Position Error, and red marks the Protection Level threshold, with the horizontal axis in days and the vertical axis in meters. The figures indicate that while positioning errors remained stable, the probability of integrity and misleading information events increases. In addition, the increased protection level can lead to reduced availability in areas with larger protection levels.

The service availability maps in the China region for DOY 001–015 in 2022 and 2023 are shown in Figure 7. The color axis denotes service availability; the horizontal axis is the latitude, and the vertical axis is precision. This shows a decline in single-frequency enhancement service performance in 2023. This decline is particularly pronounced in lower-latitude regions, where ionospheric activity is higher.

The data in

Table 5. Service performance change table for fixed value degradation parameters in 2023 (unit: meters).

STATION	DATE	HPE	VPE	HPL	VPL	Availability	PMI	PHMI
JFNG	1-1	0.08	−0.23	0.54	1.04	0.00%	0.28%	0.000%
	5-1	−0.10	−0.11	0.06	0.19	0.00%	0.57%	0.000%
	9-1	−0.23	−0.66	−0.02	0.12	0.00%	0.17%	0.000%
URUM	1-1	0.03	−0.15	0.39	0.99	0.00%	0.20%	0.000%
	5-1	−0.04	−0.07	−0.48	−0.24	0.00%	0.12%	0.000%
	9-1	−0.12	−0.36	−0.59	−0.28	0.00%	0.25%	0.000%
WUH2	1-1	0.14	0.01	0.57	1.11	0.00%	0.20%	0.000%
	5-1	−0.03	0.10	0.04	0.20	0.00%	0.52%	0.000%
	9-1	−0.16	−0.38	−0.03	0.11	0.00%	0.39%	0.000%
HKSL	1-1	0.20	0.47	0.66	1.38	−0.01%	0.87%	0.000%
	5-1	0.14	0.34	0.03	0.24	0.00%	1.57%	0.000%
	9-1	0.04	0.18	−0.11	0.06	0.00%	0.64%	0.000%
HKWS	1-1	0.20	0.44	0.65	1.37	−0.01%	0.91%	0.000%
	5-1	0.14	0.35	0.02	0.23	0.00%	1.57%	0.000%
	9-1	0.04	0.19	−0.10	0.07	0.00%	0.98%	0.000%

compare BDSBAS service metrics between 2022 and 2023 when broadcasting a fixed degradation parameter value. Performance metrics include enhanced positioning, protection levels, service availability, and integrity risk, using select International GNSS Service observation stations as examples. The table presents differences between the 2023 and 2022 results, with positive values indicating increases and negative values indicating decreases. Each statistical period starts on January 1st, May 1st, and September 1st, covering 15 days.

Table 5 shows the decline in service availability located in edge-service areas in 2023 compared to 2022. Additionally, some misleading information (MI) events are present and the probability of hazardous misleading information (HMI) events remains consistently zero in 2023, particularly in the vertical direction, due to ionospheric influences.

2.2. New Calculation Method of Ionospheric Degradation Parameters

The current method for BDSBAS broadcasting ionospheric degradation parameters is as follows: Set $C_{iono\_step}$ as a fixed value of 0.952; set $I_{iono}$ as a fixed value of 240; set $C_{iono\_ramp}$ as a fixed value of 0; and set $RS{S}_{iono}$ as 0. Due to the current ionospheric activity, the predefined threshold for ionospheric variations in the degradation parameter fails to fully encompass actual ionospheric delay changes. This paper proposes a new algorithm for determining the broadcast value of the ionospheric degradation parameter.

Real-time calculation of ionospheric grid point vertical delay values is possible through observations from evenly distributed monitoring stations utilizing a dual-frequency approach to correct ionospheric errors. Consequently, this method enables fitting vertical delay parameters at grid points, relying on ionospheric delay measurements at piercing points.

Initially, dual-frequency analysis is used to extract high-precision ionospheric delay values at piercing points, as follows:

$$IGPdelay={\displaystyle \frac{f_2^2}{f_1^2-f_2^2}}(P_2-P_1)$$

(4)

In Equation (4), $f$ denotes frequency and $P$ represents corrected pseudo-range observations.

Ionospheric delays are determined for piercing points within four quadrants around grid points, based on their distances from the grid points. If a minimum of three piercing points exist within a 5° radius $(\phi \pm 5°,\lambda \pm 5°)$ of a grid point $(\phi ,\lambda )$, an inverse-distance-weighted fitting for zenith ionospheric delay is performed, considering the spherical geometry, as follows:

$$IGPdela{y}_j={\displaystyle \frac{{\displaystyle \sum _{i=1}^n\frac{I_j}{I_i}·IPPdela{y}_i·W_{ij}}}{{\displaystyle \sum _{i=1}^n{W}_{ij}}}}$$

(5)

In Equation (5), $IGPdela{y}_j$ is the vertical delay of the ionosphere grid point $j$, $IPPdela{y}_i$ is the vertical delay of the piercing point $i$. $I_j$ and $I_i$ are the ionospheric delay values for the grid point $j$ and ionospheric piercing point $i$. $W_{ij}$ and $W_{kj}$ are the weights assigned to piercing points $i$ and $k$ in relation to grid point $j$.

$W_{ij}$ is defined as the reciprocal of the distance from the piercing point $i$ to the grid point $j$, as follows:

$$W_{ij}={\displaystyle \frac{1}{R\cdot \arccos (\sin ({\phi }_i)\sin ({\phi }_j))+\cos ({\phi }_i)\cos ({\phi }_j)\cos ({\lambda }_i-{\lambda }_j)}}$$

(6)

In Equation (6), $R$ denotes the Earth’s radius, $({\phi }_i,{\lambda }_i)$ refers to the latitude and longitude of the piercing point, and $({\phi }_j,{\lambda }_j)$ indicates the latitude and longitude of the grid point.

According to the change in ionospheric grid delay between the adjacent epoch $\Delta IGPdelay$ by Equation (2), the new degradation parameters are calculated according to Equation (7), as follows:

$$\begin{array}{l}\left\{\begin{array}{l}{C}_{iono\_step}=\Delta IGPdela{y}_{MAX}\\ C_{iono\_ramp}=0\end{array}\right.\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{if} \Delta IGPdela{y}_{MAX}\le {(C_{iono\_step})}_{LIMIT}\\ \left\{\begin{array}{l}{C}_{iono\_step}={(C_{iono\_step})}_{LIMIT}\\ C_{iono\_ramp}=(\Delta IGPdela{y}_{MAX}-C_{iono\_step})/I_{iono}\end{array}\right.\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{if} \Delta IGPdela{y}_{MAX}>{(C_{iono\_step})}_{LIMIT}\\ C_{iono\_ramp}={(C_{iono\_ramp})}_{LIMIT}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{when} C_{iono\_ramp}>{(C_{iono\_ramp})}_{LIMIT}\end{array}$$

(7)

In Equation (7), the $\Delta IGPdela{y}_{MAX}$ is the maximum ionospheric grid delay increments at all valid ionospheric grid points determined for each epoch. The ${(C_{iono\_step})}_{LIMIT}$ and ${(C_{iono\_ramp})}_{LIMIT}$ are specified by the RTCA [27], as follows:

$$\begin{array}{l}\Delta IGPdela{y}_{MAX}=MAX(\left|\Delta IGPdela{y}_{1,2,\cdots ,j}\right|) \\ {(C_{iono\_step})}_{LIMIT}=1.023\\ {(C_{iono\_ramp})}_{LIMIT}=0.005155\end{array}$$

(8)

The $C_{iono\_step}$ is determined by the maximum ionospheric grid delay increment $\Delta IGPdela{y}_{MAX}$ of the delay values from all real-time valid ionospheric grid points. If the $\Delta IGPdela{y}_{MAX}$ is less than the maximum ${(C_{iono\_step})}_{LIMIT}$ specified by the RTCA protocol, then $\Delta IGPdela{y}_{MAX}$ is broadcast and the $C_{iono\_ramp}$ is set to 0. If $\Delta {\mathrm{I}}_{\max }$ exceeds the maximum ${(C_{iono\_step})}_{LIMIT}$ specified by the RTCA protocol, the ${(C_{iono\_step})}_{LIMIT}$ is broadcast directly, and $C_{iono\_ramp}$ is broadcast within the maximum range ${(C_{iono\_ramp})}_{LIMIT}$ specified by the RTCA protocol to envelop $\Delta IGPdela{y}_{MAX}$. Additionally, $I_{iono}$ is set to the actual update cycle of the ionospheric grid, which is 240 s, and the ionospheric square root and factor parameter $RS{S}_{iono}$ is set to 1. The degradation parameters are synchronized with the ionospheric delay update time, which is also 240 s.

In recent years, ionospheric activity has led to frequent large variations in the ionospheric grid delay between consecutive epochs. Directly setting the maximum ionospheric delay change to meet the envelope requirement is therefore unreasonable. In this study, large ionospheric grid delay jumps were excluded to ensure service integrity. However, frequent meter-level ionospheric delay jumps may reduce availability while maintaining integrity. Despite this, integrity remains ensured with the application of degradation parameters.

3. Results

The comparative analysis of the features between the newly calculated parameter and the existing parameter in the first week of 2024, as shown in Figure 8, is derived from Equation (7). The green line represents the current fixed parameter, while the red dot denotes the new parameter computed in this study. The horizontal axis is in days and the vertical axis is in meters. Since the values of the majority of the new $C_{iono\_ramp}$ are zero for the most part, the current $C_{iono\_ramp}$ is almost entirely covered in the figure at present. This approach updates degradation parameters in real time rather than using fixed values, allowing for greater flexibility to accommodate significant and occasional ionospheric changes to reduce the probability of exceeding the threshold.

Real-time calculation of ionospheric degradation parameters using the proposed method significantly improves the envelope rate. As shown in Figure 9 below, the probability of exceeding the threshold at grid points is illustrated for the first day of 2023 and 2024. The blue dots represent ionospheric delay changes. The green line represents the current fixed envelope threshold; the horizontal axis is in hours and the vertical axis is in meters, while the red line illustrates the proposed real-time varying envelope threshold derived from Equation (1), aiming for more effective envelopment. This approach adapts flexibly to ionospheric delay variations.

Applying the new method to the real-time calculation of ionospheric degradation parameters for BDSBAS and MSAS significantly reduces the rate of ionospheric grid delay changes exceeding the threshold. This reduction is especially noticeable in the more active ionospheric years of 2023 and 2024.

Table 6. The probability of exceeding the threshold of grid ionospheric delay variations under different ionospheric degradation parameters.

	Degradation	2021	2022	2023	2024
BDSBAS	present	0.12%	0.78%	1.64%	1.96%
	new	0.05%	0.33%	0.67%	0.74%
MSAS	present	0.54%	1.44%	2.42%	3.24%
	new	0.05%	0.2%	0.41%	0.59%

provides the more specific annual probability of exceeding the threshold for the ionospheric grid during the highly active ionospheric period from 2022 to 2024, the BDSBAS probability of exceeding the threshold dropped from 1.46% to 0.58%, while the MSAS probability decreased from 2.37% to 0.4%.

The changes in the probability of exceeding the threshold for ionospheric grid delay calculated by Equation (3) from 2021 to 2024, for both BDSBAS and MSAS, are shown in Figure 10, after implementing the new parameters. The horizontal axis represents time in days, while the vertical axis shows the probability of exceeding the threshold. The blue line corresponds to the probability of exceeding the threshold with the new degradation parameter, and the red line represents the probability of exceeding the threshold with the current degradation parameter.

The probability of grid ionospheric delay variations exceeding the threshold decreases overall when the new parameter is used. A higher exceeding threshold rate corresponds to a larger reduction amplitude, with a more significant decrease in the rate of exceeding the threshold for MSAS after the new degradation parameter is applied. Specifically, the maximum rate for exceeding the threshold for MSAS drops from 11.8% to 3.9%, indicating that the proposed new degradation parameter strongly inhibits the growth of the rate for exceeding the threshold during ionospheric activity, effectively buffering the impact of ionospheric anomalies.

4. Discussion

To assess the impact of service availability after updating the efficiency reduction parameter algorithm, this paper employs the BDSBAS single-frequency user performance positioning algorithm and protection level calculation method to evaluate BDSBAS service performance across various dates in 2023. The new ionospheric degradation parameter proposed in this study was applied. The location performance, protection levels, probability of misleading information events, and probability of hazardously misleading information events for several IGS stations were calculated and compared with the results obtained using a fixed degradation parameter.

The data in

Table 7. Service performance improvement table under new ionospheric degradation parameters (unit: meters).

STATION	DATE	HPE	VPE	HPL	VPL	Availability	PMI	PHMI
JFNG	1-1	0.98	2.11	0.26	0.56	0	−0.19%	0.000%
	5-1	1.14	3.07	0.29	0.58	0	−1.21%	0.000%
	9-1	1.02	2.51	0.29	0.62	0	−0.43%	0.000%
URUM	1-1	0.68	1.36	0.28	0.64	0	−0.01%	0.000%
	5-1	0.85	1.86	0.28	0.60	0	−0.07%	0.000%
	9-1	0.77	1.57	0.29	0.68	0	−0.08%	0.000%
WUH2	1-1	1.01	2.40	0.26	0.57	0	−0.55%	0.000%
	5-1	1.21	3.29	0.29	0.58	0	−1.66%	0.000%
	9-1	1.08	2.81	0.30	0.64	0	−0.86%	0.000%
HKSL	1-1	0.97	2.98	0.25	0.57	−0.01%	−0.68%	0.000%
	5-1	1.29	4.22	0.26	0.59	0	−2.42%	0.000%
	9-1	1.19	4.06	0.27	0.60	0	−2.18%	0.000%
HKWS	1-1	0.97	2.94	0.25	0.57	−0.01%	−0.74%	0.000%
	5-1	1.29	4.17	0.26	0.59	0	−2.34%	0.000%
	9-1	1.19	4.02	0.27	0.60	0	−2.13%	0.000%
BJ01	1-1	0.55	0.98	0.25	0.57	0	0.00%	0.000%
	5-1	0.74	1.36	0.32	0.77	0	0.00%	0.000%
	9-1	0.53	1.36	0.27	0.59	0	0.00%	0.000%
CD01	1-1	0.83	1.90	0.24	0.52	0	−0.13%	0.000%
	5-1	1.32	2.17	0.32	0.74	0	−0.50%	0.000%
	9-1	0.90	2.07	0.25	0.57	0	−0.16%	0.000%
SY01	1-1	1.23	2.23	0.27	0.61	−0.01%	−0.27%	0.000%
	5-1	1.71	2.99	0.36	0.90	−0.01%	−1.29%	0.000%
	9-1	1.69	3.03	0.24	0.60	0	−1.05%	0.000%
KS01	1-1	0.69	1.13	0.10	0.26	−0.01%	0.00%	0.000%
	5-1	0.80	1.12	0.42	1.04	0	0.00%	0.000%
	9-1	0.71	0.94	0.30	0.71	0	0.00%	0.000%
ST01	1-1	2.78	3.40	0.34	0.80	−0.03%	−1.90%	0.000%
	5-1	2.04	2.89	0.30	0.78	−0.01%	−2.21%	0.000%
	9-1	1.96	2.53	0.23	0.53	0	−1.91%	0.000%

reflect the performance indicators of five IGS stations, including JFNG, URUM, WUH2, HKSL and HKWS, as well as five regional monitoring stations located in Beijing, Chengdu, Sanya, Kashi and Shantou in the same order as the table, during the selected time periods. Table 7 summarizes the positioning accuracy of ten stations across three time periods, highlighting changes in protection levels, availability, and integrity following the implementation of new degradation parameters. Positive values indicate improvements, such as an increase in protection levels, whereas negative values denote a decline, including misleading integrity information. Each statistical period spans 15 days. As shown in the table, during the high-activity ionospheric year of 2023, the updated degradation parameter algorithm enhanced protection levels. However, this improvement led to reduced service availability for stations located in edge-service areas. Despite this, misleading information significantly decreased, and integrity was markedly improved.

The vertical integrity Stanford diagrams for the URUM station on DOY 135 in 2023 using different ionospheric degradation parameters, as shown in Figure 11a–d, display the vertical integrity Stanford diagrams for the WUH2 station on DOY 225 as a more concrete example. The horizontal axis represents the VPL, while the vertical axis denotes the VPE. The Alert Limit is depicted as a horizontal line at 50 m. Each dot represents a specific (VPE, VPL) pair, with its color, based on a logarithmic scale, indicating the frequency of occurrences. The diagrams clearly illustrate a reduction in misleading information. However, during periods of highly active ionospheric conditions, the increased protection level results in fewer available epochs.

The new degradation parameters improve integrity more significantly in regions with higher ionospheric activity, similar to their effect on reducing the ionospheric grid delay exceedance rate. The reduction in misleading integrity information is particularly effective in southern and edge regions of China. In contrast, the probability of misleading integrity events in northern regions is inherently low or even zero, with availability generally remaining stable at 100%. Furthermore, these parameters do not reduce the availability of stations in northern regions. Broadcasting the new degradation parameters decreases the exceedance rate and enhances integrity during periods of high ionospheric activity, while having no adverse effects during low-activity periods. In ionospheric active regions, such as MSAS and BDSBAS, broadcasting these parameters effectively reduces the exceedance rate. Likewise, in ionospheric inactive regions, SBAS systems, such as EGNOS and WAAS, can also feasibly implement this method for broadcasting degradation parameters.

5. Conclusions

This study evaluates the impact of ionospheric delay variations in grid-based ionospheric models and assesses the effect of ionospheric degradation parameters on service availability and integrity by statistically analyzing the packet loss rate of BDSBAS from 2021 to 2024. The results indicate that in 2024, when ionospheric activity was more intense, the rate of grid ionospheric delay variation exceeding the threshold increased by 1.85%, leading to decreased service availability and the emergence of misleading information compared to 2021.

To address the ineffective envelopment of grid-based ionospheric delay variations and its impact on BDSBAS service integrity caused by fixed broadcast values of ionospheric degradation parameters during periods of high ionospheric activity, this study proposes a new method for ionospheric degradation parameters. The proposed method was verified and analyzed using IGS data. The results show that after implementing the new ionospheric degradation parameters, the probability of exceeding the threshold of grid-based ionospheric delay variations during peak ionospheric activity decreased by 1.10%. Misleading information events were decreased by 1.04%, ensuring service integrity.

In summary, the new BDSBAS degradation parameters proposed in this study effectively reduce the exceeding threshold probability of grid-based ionospheric delay variations during high ionospheric activity periods and improve service integrity for users.