We consider partially observed discrete-time linear stochastic systems and assume that some entries of the system matrices are unknown. We propose a new method which identifies these unknown entries and the state vectors of these systems simultaneously. The key idea of the proposed method is utilization of the pseudomeasurement which is a fictitious and additional observation process on the unknown entries and will be modified so as to work for the partially observed systems. Augmenting the pseudomeasurement with the original observation process, we derive the new identification method by applying the extended Kalman filter. The proposed method is consistent with the conventional method (without pseudomeasurement).
Artificial respirators are widely used for patients with little or no autonomous breathing ability. Doctors are required to pay scrupulous attention for the use of the artificial respirators. And doctors must set the artificial respirator in consideration of each patient’s pulmonary characteristic. However,the setting of the artificial respirator is decided by the experience and the intuition of the doctor now. The purpose of this study is to develop a method to estimate the static P −V curve and the pulmonary elastance of the patient and to set a ventilation condition of the artificial respirator. In our previous work, we have presented an estimation technique of the pulmonary elastance by fuzzy logic. By improving previous technique, this study succeeded in improving the precision of the pulmonary elastance estimation. In addition, a ventilation condition of the artificial respirator is set using estimated static P −V curve and fuzzy logic.
Ionospheric anomalies are major error sources that affect the performance of the GPS (Global Positioning System). In particular, ionospheric scintillation may cause GPS satellite signal loss, and multiple losses can degrade the availability of positioning/navigation services such as GBAS (GroundBased Augmentation Systems). In order to improve the tracking performance of GPS receivers under scintillation conditions, an ultra-tightly coupled GPS/INS (Inertial Navigation System) integrated system has been developed and flight tests to evaluate it were conducted around the island of Ishigaki where ionosphere scintillation frequently occurs. Evaluation of phase tracking performance was carried out off-line using a software-defined GPS receiver which processes stored IF (Intermediate Frequency) data. The use of INS enabled a longer integration time of GPS signals, which increased the signal-to-noise ratio (SNR). As a result, the tracking loop of GPS/INS achieved continuous phase tracking even under strong scintillation conditions.
Bilinear systems are ubiquitous in dynamics and control literature. The concept of bilinear systems is attributed to input-output coupling terms. Stochastically influenced bilinear systems are described via bilinear stochastic differential equations. Bilinear systems are attractive and popular in dynamical systems and control literature for two reasons: (i) first, they offer closed-form solutions for time-varying as well as time-invariant settings (ii) they preserve some of the qualitative characteristics of non-linear stochastic systems. This paper chiefly intends to construct a mathematical theory of a scalar time-varying bilinear ‘Stratonovich’ stochastic differential equation with a vector random input by deriving its closed-form solution and related results. Secondly, the analytic results of the paper are applied to a series RL electrical circuit and sampling mixer circuit Stochastic Differential Equations (SDE). The theory of this paper hinges on the ‘Stratonovich calculus’. This paper will be of interest to dynamists, stochasticians looking for advances in bilinear systems and their control. More specifically, this paper opens up research directions in stability and control of bilinear stochastic systems by exploiting the analytic results of this paper.
It is well known that MUSIC (MUltiple SIgnal Classification algorithm) is a standard DOA (Direction of Arrival) estimation for persistently exciting continuous signals such as radio waves and ultrasonic waves. Recently it has been applied to estimate the DOA of speech sounds, which are not necessarily the persistently exciting continuous signals but are the signals intermittently repeating voice and silence sections. From this point of view, the present paper proposes a framebased DOA estimation for a target sound source by using two microphones. More specifically, first, two observed mixtures are transformed at each frame to complex spectra by the short-time Fourier transform. Then, based on their phase difference, local DOAs are calculated. Next, a distribution of these DOAs is created and evaluated by a sparseness measure, and the frame with its evaluated value being over some threshold is judged as a single-source frame. At that frame, finally, the angle taking the peak of the distribution is adopted as the DOA estimate. The validity of our proposed method has confirmed by simulations under the environment where SNR is more than 20[dB] and the reverberation time is within 200[msec].