**1. Introduction**

Underground imaging is a noninvasive approach for creating a subsurface area model by transmitting low current, high alternating voltage, and low-frequency waves into the ground. This method has various applications such as detecting minerals, finding underground materials and faults, and detecting voids [1]. One of the techniques used in underground imaging is the capacitive resistivity (CR) method [1–3], where the ground-coupled transmitter and receiver antennas are designed and configured in a capacitive connection to measure resistivity by determining the potential difference [4–6]. The design of a capacitive resistivity imaging system can be either a single-pair antenna system that includes a transmitter and a receiver unit, or a multiple-antenna system with one transmitter and multiple receiver units [7]. Both systems use a vehicle towing mechanism during the surveying process, as presented in **Figure 1**, allowing for the mapping and location of buried utilities [8].

Accordingly, the CR method employs map-matching (MM) algorithms that utilize positioning sensors, such as the Global Positioning System (GPS) in combination with digital maps to determine the road link on which a vehicle is traveling and to obtain highly accurate data for mapping. GPS-based data collection is more efficient than traditional surveying and mapping approaches, requiring less equipment and labor [9]. It offers direct information on latitude and longitude coordinates without the need for angle and distance measurements between intermediary points. Despite its widespread use, GPS has some limitations that should be considered. For instance, GPS units cannot always provide locations with high accuracy beyond 3 meters, which could be problematic for certain applications. In addition, GPS devices rely on signals from at least four satellites to precisely pinpoint a location, and signal blocking or interference, such as in urban areas or under tree canopies, can impact performance. GPS accuracy may also be affected in environments with high ionospheric activity, such as during solar flares or geomagnetic storms. Finally, raw, uncorrected GPS data

#### **Figure 1.**

*The placement of the GPS-IMU device utilized for localization and tracking of the capacitive resistivity towed antenna system used for underground imaging is illustrated.*

#### *Adaptive Neuro-Fuzzy Inference System-Based GPS-IMU Data Correction for Capacitive… DOI: http://dx.doi.org/10.5772/intechopen.112921*

points may only be precise up to 5 meters, and a clear view of the sky is necessary to receive signals from GPS satellites [10, 11]. In capacitive resistivity imaging, lowering GPS accuracy error is critical for finding underground utilities and performing map matching, however, the mapping technology used in conjunction with GPS may not always be up-to-date or reliable, potentially leading to navigational errors [12, 13].

One known method to overcome the inaccuracy of mapping GPS sensor data alone is the IMU-GPS sensor fusion. This technique combines data from a GPS receiver and an Inertial Measurement Unit (IMU) to improve the accuracy and robustness of navigation and positioning systems [14]. It provides data regarding the orientation and acceleration of the device, while GPS provides information about its absolute position. By integrating the data from both sensors, the position and orientation estimates are more precise and reliable than the readings obtained from each sensor separately [14, 15].

In contrast to the state-of-the-art, which typically employs more conventional methods for localizing and land vehicle tracking, for instance, the Kalman filter, fuzzy logic is considered a commonly known artificial intelligence (AI) approach. Researchers in [16] created a strong Kalman filter utilizing vector tracking and integrated it with fuzzy logic to change filter parameters to follow weak signals in global navigation satellite system (GNSS) receivers. Thus, the results were superior to the standard procedure. Following this work, a fuzzy position correction method for latitude and longitude data from a GPS sensor was introduced, which was implemented on Field-Programmable Gate Arrays (FPGA) to speed up rectification results. Compared to other models, the FPGA-based approach provided a 40,000× speedup [17]. Combining antenna optimization techniques and sensor fusion with AI has been introduced to increase GPS accuracy [18]. Even when employing an inexpensive GPS sensor for location-based applications, this effectively computed correct latitude and longitude data. In another study, the authors utilized an unscented Kalman filter (UKF) and an unscented H-infinity (UH) filter to track ground vehicle position using fuzzy logic to decide which to use at any given time, lowering error by 5.6% and enhancing GPS accuracy [19]. Moreover, a fuzzy system model was developed [20] that flexibly adjusts the noisy covariance values of the extended Kalman filter (EKF) by combining data from GPS, an odometer, IMU, and the automobile's mathematical framework. This results in a 49% improvement in the precision of the vehicle's absolute position [20]. Similarly, Zhu et al. employed EKF to fuse data from a fourwheeled robot's GPS, IMU, odometers, and camera. They created a fuzzy system to adjust the noisy covariances of the EKF. The strategy successfully improves the robot's estimation of the trajectory to be followed by 80.6% [21]. These studies suggest that fuzzy logic has a great potential to be utilized for land vehicle tracking and localization, providing higher accuracy than the traditional approaches.

One type of artificial intelligence that integrates the capabilities and strengths of both neural networks and fuzzy logic systems is the adaptive neuro-fuzzy inference system (ANFIS), which is used for analyzing input–output relations, can handle inaccurate or imperfect data and has been useful to various domains such as in pattern recognition and predictive modeling [22]. Specifically, ANFIS has also been employed in the localization and tracking of land vehicles and shows significant findings in different scenarios. The work in [23] classified direct, multipath-affected, and non-line of sight (NLOS) findings using raw GPS measurements with an ANFIS algorithm. The correct classification rates were 100, 91, and 84%, respectively. Another study proposed an intelligent ANFIS system that modifies the position of a vehicle based on sensor data and latitude/longitude. According to the results, the fuzzy system outperformed the unscented Kalman filter by 69.2% [24]. Another study employs ANFIS to estimate an

IMU's inaccuracy over time. While it refrains from specifically addressing GPS-IMU incorporation, it offers details on the application of ANFIS for IMU data error estimation, and the outcome implies that the ANFIS could substantially enhance the accuracy of inertial navigation positioning, which is important for vehicle inertial navigation in intricate and covert settings [25]. Research by [26] presents an ANFIS-based approach to categorizing everyday life events using data collected by IMU sensors. Although it concentrates on identifying activities rather than GPS-IMU data correction, it still exhibits the usage of ANFIS in sensor fusion with a total accuracy of 98.88%. The realtime deployment of ANFIS for vehicle navigation is proposed in [27]. When evaluated on a vehicle, the suggested model outperforms standard methods in terms of accuracy. The experimental findings proved the benefits of the suggested AI-based INS/GPS integration strategies in terms of robustness while maintaining real-time system location accuracy [27]. Although ANFIS has several advantages for GPS-IMU localization and vehicle tracking, it is vital to evaluate the potential limits and challenges of employing this technology. In [24, 28], the study showed that one potential limitation of ANFIS is that it may necessitate a huge amount of data to accurately train the model, and ANFIS models can be complex and hard to understand, which renders it more challenging to comprehend how the model works and makes decisions, particularly for autonomous vehicle applications. Thus, future research still requires more exploration of ANFIS modeling to prove its advantages and disadvantages in certain fields.

In relation to this proposed study, the main objective is to correct the GPS sensor's latitude and longitude coordinates to avoid complex mathematical operations and achieve a comprehensive location system embedded in a towed antenna system. Thus, it is critical to reduce the error in the accuracy of GPS receivers, which ensures the correct location of underground utilities and for map matching purposes. With that, this study aims to propose an intelligent system-based fuzzy logic using ANFIS, which takes the information from Real-time Kinematics (RTK) GPS sensors with integrated IMU's linear acceleration and orientation data, which corrects the capacitive resistivity imaging system vehicle's absolute position according to its latitude and longitude. This correction uses two fuzzy systems, one for latitude and the other for longitude, which will be trained using the ANFIS tool. The positioning correction system will be trained and tested with datasets from constructed Arduino-based RTK-GPS with Integrated IMU. Moreover, the developed models are compared to the performance of two neural network models – long short-term memory (LSTM) and extreme learning machine (ELM) for the comparison and validation of the proposed method. This research is expected to provide significant benefits, including (1) facilitating the integration of different measurement modalities and improving the interpretation and visualization of the data [29] which can enhance the understanding of the subsurface properties being investigated in capacitive resistivity imaging (CRI) systems, (2) aid in mapping the precise location of the underground utility objects being surveyed by the underground imaging towed antenna system, and (3) the ability to automate data acquisition and processing, which can save time and reduce errors and provide more precise control over the measurement process, enabling more accurate and reproducible results.
