Since Confirm Sensor Theory And Applied Research

Self-validating sensor (SEVA sensor) is a new type of sensor which can generate the indicator of measurement quality and measurement value status at the same time of outputting the measurements. It is an intelligent measurement system including the use of fault detection techniques, the application of digital technology and the use of uncertainty analysis. By applying new mathematic methods and new modeling methods, the problem of state estimation of SEVA sensor in the condition of unknown measurement noise, time-varying measurement bias and unknown state equation are researched in this paper, aiming at finding new implementation of SEVA. It is of great importance in theory and practical application to the development of SEVA sensor.It is well known that the model-based state estimation algorithm such as Kalman filter is prone to invalid in the condition of unknown measurement noise. To solve the problem, by analyzing the effects of the inaccurate measurement noise covariance on the filter performance, a Kalman filter with unknown measurement noise is proposed. The feature of a wavelet transform separating a noise signal into the signal and noise in real time is combined into Kalman filter. The measurement noise covariance is estimated by wavelet transform and the covariance estimated is then used in state estimation algorithms. The proposed method applying for multisensor data fusion is discussed at last. The analytical results and simulation prove that the method proposed can estimate the measurement covariance in real time and making the Kalman filter under the condition of unknown measurement noise covariance valid.In the complex practical application environment, every sensor will have time-varying measurement bias with the time passing by, no matter its nominal precision. It is obvious that the measurement output of sensor and the state estimated from the measurement will be affected by the time-varying measurement more or less. In order to solve the problem, the method of estimating single sensor time-varying measurement bias in real time is proposed. By introducing a controllable and measurable physical variant which is linear with the measurement bias of a sensor and modeling the system states the sensor observed and the measurement bias of the sensor respectively by polynomial prediction models, both the system states and the measurement bias are observable and can be estimate with Kalman filter. The analytical results and simulation prove that the measurement bias of the sensor can be estimated by a Kalman filter together with its system states.Based on the research of single sensor time-varying measurement bias estimation method, the real-time estimation method of multisensor time-varying measurement bias is researched. Although there is no problem of observability, the modeling of unknown time-varying bias is another difficulty. In this paper, by modeling the state equations of the dynamically varying sensor biases with polynomial prediction model, the estimation method of multisensor time-varying bias in real time is proposed. The analytical results and simulation prove that the method proposed has better performance and less estimation covariance comparing with other methods.State equation is very important to the model-based state estimation method. In practical application, to obtain the prior information of unknown state is very difficult and as a result, the state model based on it has large uncertainty. Based on a constant acceleration motion law represented by a polynomial, a novel dynamic model of maneuver target--polynomial prediction model is proposed in this paper. Any target motion represented by the polynomials can be self-validating modeled by the polynomial prediction model which does not require any prior knowledge of the target dynamics, so the optimal filtering algorithm corresponding to the new model can track any maneuvering motion of a target. The simulation results of the maneuvering target tracking verify that the proposed model and algorithm have similar tracking performance as the interacting multiple model (IMM) method when IMM method works in ideal condition, but when IMM method works in nonideal condition, such as unexpected maneuver, the proposed model and algorithm have better trackingperformance.