Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

This paper deals with the peer-to-peer data communication to connect each distributed levels of developed unmanned system according to the JAUS. The JAUS is to support the acquisition of unmanned system by providing a mechanism for reducing system life-cycle costs. Each of distributed levels of the JAUS protocol divides into a system, some of subsystems, nodes and components/instances, each of which may be independent or interdependence. We have to distribute each of the levels because high performance is supported in order to create several sub-processor computing data in one processor with high CPU speed performance. To complement such disadvantage, we must think the concept that a distributed processing agrees with separating each of levels from the JAUS protocol. Therefore, each of distributed independent levels send data to another level and then it has to be able to process the received data in other levels. So, peer-to-peer communication has to control a data flow of distributed levels. In this research, we explain each of levels of the JAUS and peer-to-peer communication structure among the levels using our developed unmanned ground vehicle.

To calculate the attitude in ARS(Attitude Reference System) using 3 gyros and 3 accelerometers, gyro drift must be compensated with accelerometer to avoid divergence of attitude error. Kalman filter is most popular method to integrate those two sensor outputs. In this paper, new Kalman filtering method is proposed for roll and pitch attitude estimation. New states are defined to make linear equation and algorithm for changing Kalman filter parameters is proposed to ignore disturbances of acceleration. This algorithm can be easily applied to low cost ARS.

In this paper, a closed-loop observer to extract the center of mass (CoM) of a bipedal robot is suggested. Comparing with the simple conversion method of just using joint angle measurements, it enables to get more reliable estimates by fusing both joint angle measurements and F/T sensor outputs at ankle joints. First, a nonlinear-type observer is constructed to estimate the flexible rotational motion of the biped in the extended Kalman filter framework. It adopts the flexible inverted pendulum model which is appropriate to address the flexible motion of bipeds, specifically in the single support phase. The predicted estimates of CoM in terms of the flexible motion observer are combined with measurements (that is, output of the CoM conversion equation with joint angles). Then, we have final CoM estimates depending on the weighting values which penalize the flexible motion model and the CoM conversion equation. Simulation results show the effectiveness of the proposed algorithm.

To achieve the challenges of low-cost MEMS gyros for the precise self-localization of mobile robots, this paper examines an effective method of minimizing the drift on the heading angle that relies solely on integration of rate signals from a gyro. The main idea of the proposed approach is to use wavelet de-noising filter in order to reduce random noise which affects short-term performances. The proposed method was applied to Epson XV3500 gyro and the performances are verified by the comparisons with an existing commercial gyro module of vacuum cleaning robots.

In most cases, the trajectory of a ballistic missile is well explained by the Kepler's laws. It implies that the remaining trajectory of the ballistic missile including its final destination can be easily predicted if the position and velocity vector of the ballistic missile at any point on its path can be exactly known. Hence, an effective tracking algorithm based on an exact radar measurement model is very important for developing Ballistic Missile Defense(BMD) system. In this paper, we address to design a nonlinear filter, Unscented Kalman Filter(UKF), to track the ballistic missile.

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

Recently, the interests in the development and application of unmaned robots are increasing in various fields including surveillance and reconnaissance, planet exploration, and disaster relief. Unmaned robots are usually controlled from distance using radio communications but they should be equipped with an autonomous travelling function to cope with unexpected terrains and obstacles. This means that they should be able to evaluate terrain's characteristics quantitatively using mounted sensors so as to traverse harsh natural terrains autonomously. For this purpose, this paper presents a method for extracting terrain information, that is, slope and roughness from elevation maps as a prior step of traversability analysis. Slope is extracted using the curve fitting based on the least squares method and roughness using three metrics and their weighted average. The effectiveness of the proposed method is verified on both a fractal map and the world model map of a real terrain.

In this paper dynamic modeling parameters were estimated using a frequency domain estimation method. A systematic flight test method was employed using preprogrammed multistep excitation of the swashplate control input. In addition when one axis is excited, the autopilot is engaged in the other axis, thereby obtaining high-quality flight data. A dynamic model was derived for a small scale unmanned helicopter (CNUHELI-020, developed by Chungnam National University) equipped with a Bell-Hiller stabilizer bar. Six degree of freedom equations of motion were derived using the total forces and moments acting on the small scale helicopter. The dynamics of the main rotor is simplified by the first order tip-path plane, and the aerodynamic effects of fuselage, tail rotor, engine, and horizontal/vertical stabilizer were considered. Trim analysis and linearized model were used as a basic model for the parameter estimation. Doublet and multistep inputs are used to excite dynamic motions of the helicopter. The system and input matrices were estimated in the frequency domain using the equation error method in order to match the data of flight test with those of the dynamic modeling. The dynamic modeling and the flight test show similar time responses, which validates the consequence of analytic modeling and the procedures of parameter estimation.

Many sensor network applications require that each node's sensor data stream be annotated with its physical location in some coordinate system. Equipping GPS on every sensor node is often expensive and does not work in indoor deployments. Recently, cricket-based localization system is often used for indoor localization system. It is very important to know the exact position of beacons in cricket-based localization system for identifying moving sensor node's position. In this paper, a new method, Mobile Listener Detect Algorithm (MLD) which can automatically calculate the unknown newly installed beacons is proposed. For the verification of the feasibility of the proposed scheme, we have conducted several experiments.

In this paper, a novel localization algorithm robust to the unmodeled systematic odometry errors is proposed for low-cost non-holonomic mobile robots. It is well known that the most pose estimators using odometry measurements cannot avoid the performance degradation due to the dead-reckoning of systematic odometry errors. As a remedy for this problem, we tty to reflect the wheelbase error in the robot motion model as a parametric uncertainty. Applying the Krein space estimation theory for the discrete-time uncertain nonlinear motion model results in the extended robust Kalman filter. This idea comes from the fact that systematic odometry errors might be regarded as the parametric uncertainties satisfying the sum quadratic constrains (SQCs). The advantage of the proposed methodology is that it has the same recursive structure as the conventional extended Kalman filter, which makes our scheme suitable for real-time applications. Moreover, it guarantees the satisfactoty localization performance even in the presence of wheelbase uncertainty which is hard to model or estimate but often arises from real driving environments. The computer simulations will be given to demonstrate the robustness of the suggested localization algorithm.

The GPS tracking loop consists of three parts in general: discriminator, loop filter and DCO (Digitally Controlled Oscillator). The loop filter is the main part of the tracking loop designed to ensure a good tracking performance. Generally, the loop filter is designed using classical PI(Proportional Integral) control. Although the carrier Doppler and code Doppler are generated by the same relative movement between the satellite and the user, often, the loop filters for each tracking loop are designed separately and independently. Sometimes, they are used in a combined manner such as carrier aided code tracking, FLL assisted PLL, etc. For better GPS signal tracking, we need to design the FLL/PLL/DLL altogether optimally. The purpose of this paper is to design a GPS receiver tracking loop based on the Kalman filter in a combined manner. Also, the proposed GPS receiver tracking loop is compared with a conventional tracking loop in terms of the transfer function and the DCO input. This paper shows that conventional tracking loop is equal to the Kalman filter based tracking loop.

The conventional PID controller has been widely employed in industry. However, the PID controller with one degree of freedom(DOF) can not optimize both set-point tracking response and disturbance rejection response at the same time. In order to solve this problem, a few types of 2DOF PID controllers have been suggested. In this paper, a tuning formula for a 2DOF PID controller is presented. The optimal parameter sets of the 2DOF PID controller are determined based on the first-order plus time delay process and a real-coded genetic algorithm(RCGA) such that the ITAE performance criterion is minimized. The tuning rule is then addressed using calculated parameter sets and another RCGA. A set of simulation works are carried out on three processes with time delay to verify the effectiveness of the proposed rule.

This paper is devoted to yarn tension control problem in winding machines. The passive take-up unit is replaced by active one with ADRC(Active Disturbance Rejection Control) and it was compared with the method using conventional PD(Proportional-Derivative) controller. The main part of ADRC is ESO(Extended State Observer) which continuously estimates nonlinearities of the system, such as intrinsic nonlinearity, external disturbance and sensor noise. Then the estimated nonlinearity is used to compensate the real one, thus making controlled system linear. A number of experiments have been conducted in order to verify the performance of the original winding system to the modified one. Experiments have shown improved efficiency of the system with adopting active yarn tension control. Experimental results show that the ADRC achieves a better tension response than PD controller and is robust to parameters variation.

A new method to deal with GPS indoor positioning by means of time synchronized switching GPS repeater has been developed by authors[1]. But the developed indoor positioning system has problems. Therefore, we proposed a method for indoor positioning using GNSS Repeater-Assisted. To solve the 3-dimensional user's position, the 4 or more retransmission antennas are needed in the previously proposed methods. If a GPS repeater periodically transmits the signal like as pseudollite, the information for assisting an acquisition and tracking can be informed to receiver. Then, the user position can be calculated using the induced weak signal. The advantage of the proposed algorithm is use of only 1 re-transmission antenna because the re-transmitted signal are not used for positioning but used for assisting an acquisition and tracking weak signals induced indoor. We analyze the propose algorithms through the experiment and performed the test of feasibility.