Normalized Innovation Squared Research Articles

A computational model of motion sickness dynamics during passive self-motion in the dark.

Predicting the time course of motion sickness symptoms enables the evaluation of provocative stimuli and the development of countermeasures for reducing symptom severity. In pursuit of this goal, we present an Observer-driven model of motion sickness for passive motions in the dark. Constructed in two stages, this model predicts motion sickness symptoms by bridging sensory conflict (i.e., differences between actual and expected sensory signals) arising from the Observer model of spatial orientation perception (stage 1) to Oman's model of motion sickness symptom dynamics (stage 2; presented in 1982 and 1990) through a proposed "Normalized Innovation Squared" statistic. The model outputs the expected temporal development of human motion sickness symptom magnitudes (mapped to the Misery Scale) at a population level, due to arbitrary, 6-degree-of-freedom, self-motion stimuli. We trained model parameters using individual subject responses collected during fore-aft translations and off-vertical axis of rotation motions. Improving on prior efforts, we only used datasets with experimental conditions congruent with the perceptual stage (i.e., adequately provided passive motions without visual cues) to inform the model. We assessed model performance by predicting an unseen validation dataset, producing a Q2 value of 0.91. Demonstrating this model's broad applicability, we formulate predictions for a host of stimuli, including translations, earth-vertical rotations, and altered gravity, and we provide our implementation for other users. Finally, to guide future research efforts, we suggest how to rigorously advance this model (e.g., incorporating visual cues, active motion, responses to motion of different frequency, etc.).

An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones

Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios.

Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.

Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

Once the spoofer has controlled the navigation system of unmanned aerial vehicle (UAV), it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error. Aiming at this problem, the influence of the spoofer's state estimation error on spoofing effect and error convergence conditions is theoretically analyzed, and an improved adaptively robust estimation algorithm suitable for steady-state linear quadratic estimator is proposed. It enables the spoofer's estimator to reliably estimate UAV status in real time, improves the robustness of the estimator in responding to observation errors, and accelerates the convergence time of error control. Simulation experiments show that the mean value of normalized innovation squared (NIS) is reduced by 88.5%, and the convergence time of NIS value is reduced by 76.3%, the convergence time of true trajectory error of UAV is reduced by 42.3%, the convergence time of estimated trajectory error of UAV is reduced by 67.4%, the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%, and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8% when the improved algorithm is used. The improved algorithm can make UAV deviate from preset trajectory to spoofing trajectory more effectively and more subtly.

Two-Step Trajectory Spoofing Algorithm for Loosely Coupled GNSS/IMU and NIS Sequence Detection

Satellite navigation spoofing has become a central issue of jamming technology research because of its serious threat and ability to conceal itself. Increasingly, targets are equipped with more robust GNSS/IMU systems and normalized innovation squared (NIS) is used to detect interference. Therefore, it is harder to implement covert trajectory spoofing on a GNSS/IMU system than a GNSS-only target. In practice, spoofing is needed to control unknown targets. Therefore, covert trajectory spoofing for GNSS/IMU targets is an important issue. Hence, using the information fusion of a GNSS/IMU system, the influence of spoofing on loosely coupled GNSS/IMU positioning is derived. To avoid ill-posed equations when introducing a measurement deviation, a Kalman gain matrix local regularization method is proposed to accurately determine the measurement deviation. To avoid triggering the NIS detection alarm, the range that enables the introduced measurement deviation to remain concealed is calculated. Then, a two-step trajectory guidance algorithm is proposed to quickly guide the target onto the spoofing trajectory. The simulation results show that the proposed trajectory spoofing algorithm can guide a loosely coupled GNSS/IMU target along a spoofing trajectory without triggering the NIS detection alarm. The proposed method can remain concealed and has good theoretical and practical application value.

Adaptive Information Fusion for Human Upper Limb Movement Estimation

Accurate human movement estimation techniques are widely used in various applications, such as robotics, human-machine interaction, sports, and rehabilitation. With rapid advances in microsensors, human movement estimation using wearable micro inertial sensors has become an active research topic. The main challenges for the wearable sensor motion estimation are the inertial sensor drift problem and the linear acceleration interference problem. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion estimation. In this paper, we take the upper limb as our research subject and present a novel upper limb movement estimation algorithm to cope with these two challenges by adaptive fusion of sensor data and human skeleton constraint. In the sensor fusion part, a quaternion-based unscented Kalman filter is invoked to fuse the gyroscope, accelerometer, and magnetometer measurement information. In the Kalman filter framework, an acceleration interference detection scheme is implemented based on the exponentially discounted average of the normalized innovation squared (NIS). According to the detection results, the process and measurement noise levels are scaled up or down automatically. To further compensate for the drift, we present a novel solution by modeling geometrical constraint in the elbow joint and fuse the constraint to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results in comparison to the BTS SMART-D optical motion tracker.

USING DELTA RANGE ON GPS POSITIONING, FAULT DETECTION AND FAULT EXCLUSION

Conventionally, the well known position-velocity-acceleration (PVA) model is adopted as the dynamic model of Kalman filter for GPS positioning. However, as a moving vehicle corners at faster speeds, the conventional PVA model without using extra sensors can no longer be adequate for describing the motion of the vehicle. Thus, the positioning result of the vehicle will be less accurate, and normalized innovation squared (NIS) is not suitable as the test statistic for FDE anymore. Therefore, the delta range (DR) equation is proposed to accurately model the dynamic behavior of a maneuvering vehicle. Simulation results show that the proposed DR can perform both positioning and FDE well as the vehicle manoeuvres.