Adaptive Sea Clutter Suppression for Marine Radar Systems to Enhance Uncrewed Surface Vehicle Autonomy

2.1 Situational Awareness in Maritime Environments

USVs operate in complex and dynamic maritime environments where conditions such as temperature, humidity, and wind intensity and direction vary continuously. These conditions can adversely affect the performance of USV systems, leading to object detection errors, signal disturbances, and other noise (Zhang, Wang et al., 2021). USVs must therefore possess advanced situational awareness capabilities to effectively comprehend and react to their surroundings. Numerous studies have accordingly explored different approaches for enhancing USV situational awareness in maritime contexts.

As an example, Fowdur et al. (2021) employed a principal axes Kalman filter that considered multiple sensor noise sources to track elliptical extended targets. Their method improved tracking accuracy under challenging conditions. In a separate study, Bloisi et al. (2016) introduced a modular architecture for automated sea monitoring systems that use visual data from cameras and radar, thereby enhancing situational awareness through multimodal data integration. More recently, Zhang, Yang et al. (2021) proposed a lidar detector that leverages images of proximate objects to detect distant ones via cloud-driven neural networks. This detector improved object detection in cluttered environments.

2.2 Radar Clutter Suppression Techniques

Marine radar systems are also essential for USV navigation, particularly for detecting long-range obstacles and tracking their motion. However, radar signals can be reflected from the sea surface and rain, resulting in clutter that obscures desired targets. Effective clutter suppression is therefore crucial for accurate target detection and safe navigation, and several studies have considered methods for removing or reducing sea clutter.

In one of the earliest such studies, Foreman et al. (1995) devised an algorithm to compute the impact of sensitivity time control on interference from diverse radar systems. Their method improved radar performance by mitigating interference effects. Later, Murai et al. (2002) employed a wavelet transform technique to analyze radar echo signal data and enhance clutter suppression. As part of their work, they defined the sea clutter ratio now used in maritime radar displays.

Other studies have used different algorithms for noise suppression. For example, Alaee et al. (2010) developed an adaptive threshold decision algorithm for maritime target detection in environments characterized by maritime clutter and anomalous noise. This algorithm used statistical methodologies to estimate noise variance and average values, thereby improving target detection accuracy. Liu et al. (2014) proposed a linear prediction error method for addressing sea clutter, extending the dynamic range of the radar receiver and preventing radar saturation. Finally, Liu et al. (2015) introduced an STC prediction method that employs a radial basis function neural network to enhance the traditional STC approach in a way that accounts for rapidly changing sea surface reflections. This method also significantly improved radar clutter suppression and target detection accuracy.

Overall, previous research on clutter suppression has primarily focused on the automatic mitigation of sea surface reflections solely at the signal processing stage, without considering the relationship between sea surface reflections and environmental variables. Moreover, previous studies have primarily relied on simulation-based verification without extensive validation in full-scale USV applications. These limitations underscore the need for an integrated approach that considers both radar signal processing and environmental factors and stands up to testing in real-world conditions.

3.1 Correlation and Multiple Regression Analysis

In probability theory and statistics, correlation analysis is used to examine the linear relationship between two variables, which can be either independent or correlated. The strength of this relationship is represented by the correlation coefficient ρ. When ρ is between 0 and +1, the variables are positively correlated; when ρ is between –1 and 0, they are negatively correlated; and when ρ equals 0, they are considered uncorrelated. A ρ of 0 does not necessarily mean the two variables are completely independent; rather, it indicates the absence of a linear correlation between them. Importantly, correlation analysis alone cannot determine causality between variables (Gogtay et al., 2017).

To investigate how multiple independent variables may affect a dependent variable, MRA is employed. MRA is a statistical technique used to predict the outcome of a dependent variable based on its relationships with multiple independent variables. When constructing a multiple regression model, all variables that independently influence the dependent variable should be included in the model (Chatterjee & Hadi, 2006). The regression function is given by Equation (1):

Yi=β0+β1Xi1+β2Xi2+…+βkXik+ei1

where Y_i is the dependent variable, X_i1,..., X_ik are the independent variables, β₀,..., β_k are the coefficients, and e is the model’s error term (i.e., residuals). Residuals represent the difference between observed values and those predicted by the model. The overall objective of regression analysis is to maximize the prediction accuracy of the dependent variable from the set of independent variables, which results in smaller error terms or residuals.

The explanatory power of a regression model, or the degree to which the independent variables explain the variability in the dependent variable, is given by the coefficient of determination R² as defined in Equation (2):

R2=SSRSST=1−SSESST2

where SSE is the sum of the squares of the errors in the predicted values of the dependent variable, SST is the sum of the squared differences between the observed dependent variable values and their mean, and SSR is the sum of the squared differences between the model-predicted dependent variable values and the mean of the observed values. The coefficient of determination typically ranges between 0 and 1, where values closer to 1 signify stronger explanatory power and a better fit for the regression equation. Conversely, values closer to 0 suggest weaker explanatory power and a poorer fit.

In MRA, the adjusted R² is a useful metric because it corrects for this overestimation by accounting for the number of samples and independent variables, as shown in Equation (3):

AdjustedR2=1−SSE÷(n−k−1)SST÷(n−1)=1−(n−1n−k−1)(1−R2)3

where n denotes the number of samples and k indicates the number of independent variables in the regression equation.

Hypotheses for regression testing are described according to Equations (4) and (5). The null hypothesis (H₀) is that the regression coefficients for each independent variable are 0, which would indicate no linear connection between the dependent and independent variables:

H0:β1=β2=…=βk=04

H1:allβ1(i=1,2,…,k)≠05

The measure used to test for statistical significance is the F-value F₀ given by Equation (6):

F0=SSR÷kSSE÷(n−k−1)6

If the null hypothesis (H₀) is true, F₀ tends to be close to 1. If the F₀ for a model exceeds the critical value, H₀ is rejected, as at least one independent variable exhibits a statistically significant association with the dependent variable. In multiple regression models, it is crucial to subsequently verify the significance of individual regression coefficients, which is often done using confidence intervals. Because the typical statistical significance level α is 5% (0.05), the 95% confidence interval is the common standard, indicating a 5% acceptable error rate. However, the 90% or 99% confidence intervals can also be chosen depending on flexibility or conservatism.

The p-value represents the probability that the observed correlation occurred by chance. To derive the p-value for each regression coefficient, the t-statistic for each regression coefficient β_i is calculated as

ti=βiSE(βi)7

where SE(β_i) is the standard error of the coefficient β_i. The degrees of freedom for the t-distribution are given by n – k – 1. With the computed t-statistic and degrees of freedom, the p-value can be determined by comparing the t-statistic to the t-distribution. This comparison can be done using statistical software or a t-distribution table. The p-value represents the likelihood of obtaining a t-statistic at least as extreme as the value computed under the null hypothesis.

In this study, we use correlation analysis to analyze relationships between pairs of parameters and determine their statistical significance. A significance level α of 0.05 is commonly chosen, such that parameters with a p-value of 0.05 or less are considered highly reliable. Lower p-values signify stronger evidence against the null hypothesis, suggesting that the relationship between variables is statistically significant.

3.2 MRA-based Procedure for Determining Appropriate Radar Parameters

The performance of marine radar detection is influenced by maritime environmental conditions. Specifically, the effectiveness of the target detection algorithms used in marine radar systems is highly sensitive to maritime environmental noises such as sea and rain clutter. Sea clutter originates from wave-related phenomena in maritime environments (Han & Park, 2021) and makes it difficult to detect actual target ships within radar images. Figure 2 shows two different radar images with differing intensities of sea clutter.

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FIGURE 2

Examples of marine radar images with two different levels of sea clutter. (a) High sea clutter (b) Low sea clutter

The returned signal power of a typical target varies according to 1/ r⁴ (Bole et al., 2013), where r represents the relative range between the radar and target. In contrast, the returned signal power of sea clutter does not follow the 1/ r⁴ rule. The status of the sea surface constantly changes, and its effective radar cross section therefore varies depending on the range. Moreover, because the mechanisms that produce sea clutter are complicated, complex statistical theories are required for their quantitative analysis (Bole et al., 2013). In other words, handling clutter in the context of actual maritime environmental conditions can be exceedingly challenging due to the intricate behavior patterns that clutter can exhibit.

Through field tests, we determined that the sea clutter level frequently changes based on environmental conditions. Effectively reducing sea clutter requires a signal attenuation approach that considers the gain profile, which varies with range. In this study, we applied a sea clutter adjustment parameter (SCAP) in the radar system to reduce reflections from the sea surface. We selected the range covered by the gain profile and set the gain to increase linearly from 0 to 1 depending on the attenuation range. Equation (8) describes the terms of the SCAP:

P′=αP, α={r/TSCAP0≤r≤TSCAP1r>TSCAP8

where P is the raw radar signal, P′ is the attenuated radar signal, a is the gain value, and T_SCAP is the SCAP value, which is adaptively estimated based on the environmental conditions.

Selecting the appropriate SCAP to reduce sea clutter is crucial for autonomous USV navigation, as an appropriate SCAP significantly enhances target detection performance (Cambridge Pixel, 2024; Han et al., 2020; Han et al., 2023). To determine the appropriate values, we performed correlation and regression analyses to identify the relationship between the SCAP and environmental conditions. Figure 3 outlines a process for selecting the SCAP value for STC for a radar system based on maritime environmental conditions. The process begins by measuring various environmental factors that could influence radar performance. A preliminary multiple regression analysis is then conducted to understand how these different parameters affect the radar system. Statistical tests are performed to determine the significance of each parameter, and non-significant parameters (i.e., those with a P-value exceeding 0.05) are excluded from further analysis. Parameters with p-values less than or equal to 0.05 are selected for further consideration.

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FIGURE 3

Process for identifying the main environmental factors affecting the marine radar and then determining the appropriate SCAP value.

If multiple environmental factors are considered significant after this initial screen, their combined effect is then re-analyzed using MRA. Environmental factors with a re-evaluated p-value greater than 0.05 are excluded from the analysis, and the remaining factors that significantly affect the radar system are used to create a regression equation. Finally, this regression equation is used to determine the appropriate SCAP value for the radar system. This structured approach ensures that only the most relevant environmental factors are used to calculate the SCAP value, thus enhancing the radar system’s performance in diverse maritime conditions.

4.1 USV System for Field Experiments

The Korea Research Institute of Ships and Ocean Engineering has developed a series of USV platforms, as detailed in Han et al. (2020). Of this diverse array of platforms, we selected the Aragon USV for this study. Specifications for Aragon are provided in Table 1. In brief, the Aragon USV features an 8-meter mono-planning hull, as depicted in Figure 4. Its primary operational components include a waterjet propulsion system, communication systems, and a navigation suite equipped with perception sensors. These features collectively enable the Aragon USV to effectively fulfill its role which involves autonomous navigation and data collection in maritime environments.

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FIGURE 4

The Aragon USV developed by the Korea Research Institute of Ships and Ocean Engineering (Han et al., 2020).

In the context of this study’s objective to analyze the effect of maritime environmental parameters on radar performance, a suite of positioned sensors was strategically installed on the Aragon USV. Notably, the Furuno FAR-2117 pulse radar system is employed to generate radar images achieved through the precise digitization of analog signals using a scan converter. The performance specifications of this pulse radar are presented in Table 2. The radar was installed at a height of 4 meters above the USV.

4.2 Multiple Regression Analysis using Maritime Environmental Conditions and Radar Parameters

The radar range equation 1/ r⁴ highlights how the power of a radar signal diminishes exponentially with distance. This property can cause the radar receiver to either saturate from strong short-range returns or, if the gain is adjusted to prevent saturation, have reduced sensitivity for weak long-range signals. The STC parameter addresses this shortcoming by applying a range-dependent attenuation function that reduces short-range signals and progressively reduces the attenuation for longer ranges. This function is crucial for ensuring that the radar can detect real targets at longer ranges without being overwhelmed by closer false targets. Implemented either during front-end analogue processing stage or post-digitization, STC ensures effective target detection across different ranges. In this study, we enhance the radar’s performance by adjusting the SCAP value to suppress near-range sea clutter.

We acquired Furuno radar data through ten field tests of the Aragon USV conducted over several years off the southern coast of South Korea. Using this data, we derived the appropriate gain by manually tuning the SCAP. We additionally collected data on maritime environmental conditions from the Korea Meteorological Administration (KMA, 2024), sourced from strategically placed maritime buoys and recorded every 30 minutes. Table 3 presents an example of the marine environmental data available from the KMA on June 2, 2016. This data includes wind speed and direction, barometric pressure, humidity, air and water temperature, maximum and significant wave heights, wave period, and sea level. However, because the KMA data interval does not align with the radar timescale, we interpolated the KMA data to match the timing of the radar data. Table 4 additionally shows the manually tuned attenuation range (SCAP value) at each time point. This composite data set of maritime weather conditions and SCAP values was then used to remove sea clutter via MRA.

We then tested for correlations between each of the maritime environmental conditions and the attenuation range in each data set. Table 5 presents the results of this analysis, including the correlation coefficients, number of observations, t-values, and p-values. The environmental factors that were significantly correlated with radar performance include wind speed, barometric pressure, humidity, and air and water temperature. Other parameters were excluded from further analysis because their p-values exceeded 0.05, indicating a lack of statistical significance.

Following the correlation analysis, we conducted two iterations of MRA using these selected factors following the workflow shown in Figure 3. Table 6 details the regression statistics from the first iteration of MRA, which used all four predictors implicated in the correlation analysis. This step was crucial for understanding the combined impact these predictors on radar performance. In the first MRA iteration, the p-values for barometric pressure, air temperature, and water temperature all exceeded the significance level of 0.05, resulting in their exclusion from further iterations of our analysis. This exclusion underscores the importance of rigorous statistical testing for refining the model to ensure that only the most relevant variables are included in the final analysis, thereby improving the reliability and accuracy of the radar performance.

Table 7 shows the results of a second iteration of MRA performed after excluding the barometric pressure, humidity, and air and water temperatures. The revised MRA results confirm that the remaining factor, wind speed, has a p-value well below the acceptable significance threshold, indicating its statistical significance and therefore validating its inclusion in the model.

The dominance of wind speed is likely due to its continuous variation over time, as the other environmental variables, such as wave height, and pressure, remain relatively stable over shorter intervals, as shown in Table 3. In addition, wind speed measurements were more consistent across different positions within the study area, while wave heights varied significantly by location. For these reasons, wind speed emerged as the dominant environmental factor for adjusting radar parameters. This result underscores the strong influence of wind speed on the USV marine radar system, suggesting that variations in wind speed have a considerable impact on radar performance. Radar performance, in turn, influences radar readings and the USV’s ability to accurately detect and track targets. Based on the results of the MRA, we derived the following empirical equation for estimating the SCAP value:

T^SCAP=491.997+81.955VW9

where T^SCAP is the estimated SCAP value and V_W is the wind speed.

4.3 Verification of MRA Performance

We verified our empirical equation for estimating the attenuation range from MRA by comparing the default radar images produced using the manufacturer’s radar settings to the noise-minimized images obtained using our proposed method. To facilitate automatic target detection using radar data and maintain consistency in our research framework, we used the cell averaging constant false alarm rate (CA-CFAR) detector introduced in our previous work (Han et al., 2020).

Figure 5 shows the results of this comparative visualization of radar detection performance. In the visual representation, raw radar signals are displayed in green, while their processed counterparts are depicted in red. In the processed data, detection results are indicated by crosses at the center of the data, providing a clear visualization of the radar’s performance. The images on the left-hand side of Figure 5 show the results obtained using the manufacturer’s default STC value. These visuals reveal the presence of multiple sea clutter instances near the radar, resulting in a significant number of false detections. In contrast, the images in the right-hand column show a considerable reduction in false detections when our proposed method is applied. In the adaptively corrected images, false detections are notably minimized, resulting in improved radar performance.

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FIGURE 5

Comparative analysis of USV radar detection performance on radar images, with the manufacturer’s default correction settings (left) and the adaptive correction method proposed in this study (right).

To better evaluate the effect of various attenuation values on detection performance, we conducted both qualitative and quantitative assessments on the default-corrected and adaptively corrected radar images. In the qualitative evaluation, just as navigators judge the noise level by visually examining the radar screen, we categorized the reduction in noise levels achieved by our proposed method, relative to the default settings, on a scale from ‘very poor’ to ‘excellent’. This categorization offers valuable insights into how effectively our methodology addressed noise-related issues. In the quantitative assessment, we counted and compared the number of false and true detections. These results are summarized in Table 8, providing a clear overview of the effectiveness of our approach.

Our proposed methodology notably decreased noise levels and significantly improved detection performance across various mission scenarios. The total number of detection errors across all scenarios decreased significantly, from 391 errors to just 69. As a result, our method enhanced the USV marine radar system performance by 82.35% compared to the default maritime operation system. This substantial improvement holds the potential to significantly enhance the operational efficiency and reliability of USV systems across diverse maritime environments.

Detailed examination of these parameters reveals that understanding and accounting for wind speed is essential for optimizing radar system settings. This environment-based correction ensures that the radar can function effectively under different maritime conditions, providing reliable data for navigation and safety. By focusing on statistically significant factors, the MRA correction technique proposed here creates a more robust and accurate model, improving the predictive capabilities of the radar system. In turn, an accurate model enhances the operational effectiveness of the USV marine radar system, making it more resilient to environmental variation and ensuring better performance in real-world scenarios. The exclusion of non-significant parameters like barometric pressure and air and water temperature simplifies the model and prevents overfitting, leading to more generalizable and reliable outcomes.

The results of our comparative analysis highlight the significant impact of radar improvements on USV operations, particularly in enhancing mission success rates and operational safety. By optimizing radar parameters for better obstacle detection and clutter suppression, our adaptive correction method has the potential to improve USV navigational efficiency and situational awareness. These improvements are especially important in mission-critical tasks such as search and rescue, environmental monitoring, and autonomous navigation, where accurate radar readings reduce the risk of collisions and mission failure. Optimized radar settings, tailored to environmental conditions, ensure USV performance remains effective even in challenging real-world maritime scenarios.