State-of-charge (SOC) estimation is an important aspect for modern battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter using spherical transform (Sqrt-UKFST) with unit hyper sphere is proposed. The Sqrt-UKFST does not require the linearization for nonlinear model and uses fewer sigma points with spherical transform, which reduces the computational requirement of traditional unscented transform. The square root characteristics improve the numerical properties of state covariance. The proposed method has been experimentally validated. The results are compared with existing SOC estimation methods such as Coulomb counting, portable fuel gauge, and EKF. The proposed method has an absolute root mean square error (RMSE) of 1.42% and an absolute maximum error of 4.96%. These errors are lower than the other three methods. When compared with EKF, it represents 37% and 44% improvement in RMSE and maximum error respectively. Furthermore, the Sqrt-UKFST is less sensitive to parameter variation than EKF and it requires 32% less computational requirement than the regular UKF.

The endoscopy capsule is a medical device capable of capturing images inside human's digestion system, specifically the small and big intestine. For medical diagnostics and surgery, it is required to know the position and direction of the image taken inside digestion system. This paper considers an alternative method of presurgery gastroscopy and colonoscopy monitoring procedure that allows the patient to freely move inside the medical ward. The direction-of-arrival (DOA) and inertial measurement unit (IMU) measurements are integrated to track the movement of capsule with respect to patient's body reference frame. The DOA is estimated via antenna arrays installed within a medical ward and the IMU is installed on the capsule endoscopy. The IMU sends the position information wirelessly to the antenna arrays in medical ward. Additional beacons are attached to the patient to allow body orientation and absolute position estimation due to the free movement. The nonhomogeneous nature of human body refracts the signal transmitted by the capsule, which leads to a highly nonlinear DOA function. This paper implements the unscented Kalman filter (UKF) to track the capsule by fusing the measurements made by DOA, IMU, and additional beacons attached to the patient. Simulations are conducted to investigate the capsule tracking and orientation estimation performance with respect to DOA resolution and beacons localization accuracy. Results confirm that compared with the DOA resolution, the beacons localization accuracy has a higher impact on the capsule orientation estimation performance. Furthermore, this paper investigates the impact of the number of available antenna arrays on multiplication required by UKF.

This paper investigates the performance of wireless capsule endoscopy (WCE) tracking within the irregular environment of digestion system by integrating directional-of-arrival (DOA), time-of-arrival (TOA) [or time-difference-of-arrival (TDOA)], and inertial measurement unit (IMU) measurements using the extended Kalman filter (EKF). Here, an emitter transmits a signal to the WCE, and the WCE replies the signal back to a set of antenna arrays. Then, the round trip TOA, and the DOA of the signals transmitted from WCE to the antenna arrays are measured. The digestion system path model is considered unknown, and the movement model between two consecutive signals is assumed linear. Simulations are conducted to investigate the EKF performance as a function of the number of antenna arrays, antenna array configuration, and TOA (and TDOA) estimation accuracy. In addition, the impact of IMU on the localization performance will be studied. Simulations confirm the proposed WCE tracking performance. In addition, the computational complexity of the EKF with respect to the number of antenna array is studied.

The extended Kalman filter (EKF) has been widely used for attitude determination in various satellite missions. However, it requires an extensive computational power that is not suitable for nanosatellite application. This paper proposes a gain-scheduled EKF (GSEKF) to reduce the computational requirement in the nanosatellite attitude determination process. The proposed GSEKF eliminates the online recursive Kalman gain computation by analytically determining the Kalman gain based on the sensor parameters, such as the gyroscope noise variance, the quaternion variance, the observation matrix, and the satellite rotational speed. Two GSEKF Kalman gains for two satellite operating modes are presented: the Sun-pointing and nadir-pointing modes. The simulation and experimental results show that the proposed method has comparable attitude estimation accuracy to the conventional EKF. In addition, the proposed GSEKF reduces 86.29% and 89.45% of the computation load compared with the multiplicative EKF and Murrell's version.