One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter technique. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluat...

The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because...

The paper presents a comparison of various soft computing techniques used for filtering and enhancing speech signals. The three major techniques that fall under soft computing are neural networks, fuzzy systems and genetic algorithms. Other hybrid techniques such as neuro-fuzzy systems are also available. In general, soft computing techniques have been experimentally observed to give far superi...

As an optimal estimation method, the Kalman filter is the most frequently-used data fusion strategy in the field of dynamic navigation and positioning. Nevertheless, the abnormal model errors seriously degrade performance of the conventional Kalman filter. The adaptive Kalman filter was put forward to control the influences of model errors. However, the adaptive Kalman filter based on the predi...

—This paper presents Current statistical model based Adaptive Unscented Kalman Filter (CAUKF) for maneuvering target tracking, which is based on Received Signal Strength Indication (RSSI). In order to introduce the Kalman filter, the state-space model, which uses RSSI values as the measurement equation, needs to be obtained. Thus a current statistical model for maneuvering target based on the ...

Filtering noise in image sequences is an important preprocessing task in many image processing applications, including but not limited to real-time x-ray image sequences obtained in angiography. The main objective in real-time noise filtering is to improve the quality of the resultant image sequences. Practically affordable approaches are generally suboptimal and deal with the spatial and tempo...

Adaptive quantizers/dequantizers are systems that are used to quantize efficiently signals with a large variation in short-term variance. They are typically found in telecommunication systems where highly non-stationary signals such as speech need to be represented digitally with the minimum number of bits. Channel errors that are introduced owing to non-ideal transmission significantly reduce ...

An adaptive tracking filter for maneuvering targets is developed. The approach is based on matched filtering of the innovations sequence. Analytical solutions based on the invariant subspace method for time-invariant kinematic models along with the corresponding steady-state Kalman filter kernels are derived. Asymptotic and finite-memory operating characteristics are deduced. The proposed adapt...

Multisensor data fusion has found widespread application in industry. The objective of data fusion is to provide an improved estimate of a state from a set of data provided by different sensors. Among the various multisensor approaches, Kalman filtering is one of the most significant. Reliability of the data provided by the sensor is a key factor for the integrity of the fusion process. Kalman ...