Relative navigation for satellite formation flight using a continuous-discrete converted measurement Kalman filter

The present paper develops an approach of relative orbit determination for satellite formation flight.Inter-satellite measurements by the onboard devices of the satellite were chosen to perform this relative navigation,and the equations of relative motion expressed in the Earth Centered Inertial frame were used to eliminate the assumption of the circular reference orbit.The relative orbit estimation was achieved through a continuous-discrete converted measurement Kalman filter design,in which the measurements were transformed to the inertial frame to avoid the linearization error of the observation equation.In addition,the situation of the coarse measurement period(only microwave radar measurements are available)existing was analyzed.The numerical simulation results verify the validity of the navigation approach,and it has been proved that this approach can be applied to the formation with an elliptical reference orbit.

Modified robust finite-horizon filter for discrete-time systems with parameter uncertainties and missing measurements

A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.

A Measure System of Zero Moment Point Using Wearable Inertial Sensors

Keeping balance is the premise of human walking. ZMP(zero moment point) is a point where total torque achieves balance. It is an important evaluation parameter of balance ability in walking, since it can be used to better reflect the dynamic balance during walking. ZMP can be used in many applications, such as medical rehabilitation, disease diagnosis, treatment and etc. In this paper, wearable inertial sensors system based on MEMS is used to measure ZMP(zero moment point) during walking, which is cheap, convenient, and free from the restriction of lab. Our wearable ZMP measurement system consists of inertial measurement subsystem and PC real-time monitoring station. Inertial measurement subsystem includes 9-axis inertial sensing nodes, the body communication network and the central node. Inertial sensing nodes are mounted on different parts of the body to collect body posture information in real-time, and then the best estimation of current posture are obtained by Kalman filter. The data from sensors is aggregated to the central node through the CAN bus, and then ZMP is calculated. Finally, it can be showed in the PC monitoring station. Experiments prove the system can achieve real-time ZMP detection during walking.

Applying nonlinear filtering in reverberation time measurement under strong background noise

Nonlinear filtering of impulse response obtained by M-sequence correlation method under strong background noise is presented. The research shows that the new method works very efficiently without the need to cut off impulse response data. Even if the ratio of signal to noise is below -15 dB, the same decay curve ranges can still be obtained as when S/N > 40 dB

Measurement on Spot Size Dependence of Dense WDM Dielectric Multilayer Filters

We present the spot size dependence of dielectric multilayer filters for use in dense WDM systems. We found large dependences of filter performances on the spot size and the incident angle of input light, which should be important for miniaturizing multi-channel add/drop filters.