Static Filtering Research Articles

Research on Kinematic and Static Filtering of the ESKF Based on INS/GNSS/UWB.

With the widespread development of multiple sensors for UGVs, the multi-source fusion-navigation system, which overcomes the limitations of the use of a single sensor, is becoming increasingly important in the field of autonomous navigation for UGVs. Because federated filtering is not independent between the filter-output quantities, owing to the use of the same state equation in each of the local sensors, a new kinematic and static multi-source fusion-filtering algorithm based on the error-state Kalman filter (ESKF) is proposed in this paper for the positioning-state estimation of UGVs. The algorithm is based on INS/GNSS/UWB multi-source sensors, and the ESKF replaces the traditional Kalman filter in kinematic and static filtering. After constructing the kinematic EKSF based on GNSS/INS and the static ESKF based on UWB/INS, the error-state vector solved by the kinematic ESKF was injected and set to zero. On this basis, the kinematic ESKF filter solution was used as the state vector of the static ESKF for the rest of the static filtering in a sequential form. Finally, the last static ESKF filtering solution was used as the integral filtering solution. Through mathematical simulations and comparative experiments, it is demonstrated that the proposed method converges quickly, and the positioning accuracy of the method was improved by 21.98% and 13.03% compared to the loosely coupled GNSS/INS and the loosely coupled UWB/INS navigation methods, respectively. Furthermore, as shown by the error-variation curves, the main performance of the proposed fusion-filtering method was largely influenced by the accuracy and robustness of the sensors in the kinematic ESKF. Furthermore, the algorithm proposed in this paper demonstrated good generalizability, plug-and-play, and robustness through comparative analysis experiments.

Dynamic gravity compensation does not increase detection of myocardial ischemia in combined accelerometer and gyro sensor measurements

Previous studies have shown that miniaturised accelerometers can be used to monitor cardiac function and automatically detect ischemic events. However, accelerometers cannot differentiate between acceleration due to motion and acceleration due to gravity. Gravity filtering is essential for accurate integration of acceleration to yield velocity and displacement. Heart motion is cyclic and mean acceleration over time is zero. Thus, static gravity filtering is performed by subtracting mean acceleration. However, the heart rotates during the cycle, the gravity component is therefore not constant, resulting in overestimation of motion by static filtering. Accurate motion can be calculated using dynamic gravity filtering by a combined gyro and accelerometer. In an animal model, we investigated whether increased accuracy using dynamic filtering, compared to using static filtering, would enhance the ability to detect ischemia. Additionally, we investigated how well the gyro alone could detect ischemia based on the heart’s rotation. Dynamic filtering tended towards lower sensitivity and specificity, using receiver operating characteristics analysis, for ischemia-detection compared to static filtering (area under the curve (AUC): 0.83 vs 0.93, p = 0.125). The time-varying gravity component indirectly reflects the heart’s rotation. Hence, static filtering has the advantage of indirectly including rotation, which alone demonstrated excellent sensitivity to ischemia (AUC = 0.98).

A dynamic filtering obstacle detection method based on ultrasonic array

In research on the navigation and control of an Autonomous Land Vehicle (ALV), the ultrasonic obstacle detection system plays an important role extending the environment cognition capability of the ALV. With a goal of improving the accuracy of ultrasonic obstacle detection, a dynamic data filtering method based on ultrasonic array is presented. The sonar return data is first processed through static filtering according to the geometric relationship of the ultrasonic array. Then, dynamic filtering is executed using the orientation and trajectory information of the vehicle. The dynamic filtering method is compared to the traditional ultrasonic obstacle detection method, which is the static filtering method in a typical field environment. The experiment result demonstrates the validity of the method for solving the fake data problem, and the accuracy of obstacle detection is improved.

Energy-to-peak filtering for singular systems: the discrete-time case

Both full-order and reduced-order energy-to-peak filtering problem for discrete-time singular systems are investigated. The purpose is to design a filter with desired order, such that the resulting error systems are regular, casual and stable while the closed-loop transfer function from the noises to the filtering error output satisfies a prescribed l2−l∞ norm bound constraint. First, based on some augment technique, the generalised bounded real lemma with guaranteed energy-to-peak performance for discrete-time singular systems is derived. Then, the necessary and sufficient conditions for the solvability of the energy-to-peak filter are obtained in terms of linear matrix inequalities (LMIs) coupling with a non-convex rank constraint. An explicit parameterisation of all desired energy-to-peak filters is presented. When the full-order or the static filtering is considered, convex LMI conditions and a simple parameterisation of all the desired filters are also given. Furthermore, the presented results are shown to cover the existing results on energy-to-peak filtering for regular state-space systems.

On ACK filtering on a slow reverse channel

AbstractAcknowledgement (ACK) filtering has been proposed as a technique to alleviate the congestion at the input of a slow channel located on the reverse path of a TCP connection. Old ACKs waiting at the input of the slow channel are erased when new ACKs are to be queued. In the literature the case of one‐ACK per connection at a time has been studied. In this paper we show that this is too aggressive for short transfers where ACKs arrive in bursts due to the slow start phase, and where the TCP source needs to receive the maximum number of ACKs to increase fast its window. We study first static filtering where a certain ACK queue length is allowed. We show analytically how this length needs to be chosen. We present then some algorithms that adapt the filtering of ACKs as a function of the slow channel utilization rather than the ACK queue length. These algorithms provide a good compromise between reducing the ACK queueing delay and passing a large number of ACKs that guarantee a fast window increase. Copyright © 2003 John Wiley & Sons, Ltd.

Human fetuses have nonlinear cardiac dynamics.

Approximate entropy (ApEn) is a statistic that quantifies regularity in time series data, and this parameter has several features that make it attractive for analyzing physiological systems. In this study, ApEn was used to detect nonlinearities in the heart rate (HR) patterns of 12 low-risk human fetuses between 38 and 40 wk of gestation. The fetal cardiac electrical signal was sampled at a rate of 1,024 Hz by using Ag-AgCl electrodes positioned across the mother's abdomen, and fetal R waves were extracted by using adaptive signal processing techniques. To test for nonlinearity, ApEn for the original HR time series was compared with ApEn for three dynamic models: temporally uncorrelated noise, linearly correlated noise, and linearly correlated noise with nonlinear distortion. Each model had the same mean and SD in HR as the original time series, and one model also preserved the Fourier power spectrum. We estimated that noise accounted for 17.2-44.5% of the total between-fetus variance in ApEn. Nevertheless, ApEn for the original time series data still differed significantly from ApEn for the three dynamic models for both group comparisons and individual fetuses. We concluded that the HR time series, in low-risk human fetuses, could not be modeled as temporally uncorrelated noise, linearly correlated noise, or static filtering of linearly correlated noise.

Coupling of a neural filter and a neural controller for improvement of fermentation performance

The noise associated with fermentation processes is normally minimised by a filtering technique. However, sometimes the noise may be beneficial if it is properly regulated. For recombinant β-galactosidase production in a fed-batch fermentation subject to Gaussian disturbances, it is shown that a neural network trained to act as a noise filter can allow disturbances of only a particular variance which maximizes β-galactosidase synthesis. By coupling such a filter with a neural controller, the productivity may be enhanced beyond what is possible with static filtering and either proportional-integral-derivative or neural control.

Polarisation-independent 20 Gbit/s soliton datatransmissionover 12 500 km using amplitude and phase modulationsoliton transmission control

Polarisation-independent error-free 20 Gbit/s soliton data transmission over 12500 km, with flat system Q dependence on distance, has been demonstrated using static filtering and a semiconductor polarisation-independent amplitude and phase modulator for soliton transmission control.

Static filtering on stratified programs

We extend static filtering in [8], a query optimization strategy based on seminaive evaluation on system graphs, so that it can efficiently handle stratified programs without extra overhead. The computation of static filters is formalized as a transformation so that the least fixed point of the transformation can be the static filters. The static filtering on stratified programs is shown to be complete with respect to the iterated fixed point semantics.