Real-Time Joint Filtering of Gravity and Gravity Gradient Data Based on Improved Kalman Filter

Integrated gravity and gravity gradient 3-D inversion can effectively improve the resolution of inversion results. This study aims to find a better way to extract geological information from gravity and gravity gradient data and to obtain the recovered model with a higher resolution. We present three different methods for integrating gravity and gravity gradient data in inversion: directly integrating gravity and gravity gradient data (DIGG), the sequential inversion of gravity and gravity gradient data (SIGG), and integrating gravity and gravity gradient components using a weighting matrix (IGGW). In addition, we propose a new discrete method for smooth inversion. We discretize the stabilizing functional into two parts. One is the square of the 2-norm of the model parameter, and the other one is the square of the Frobenius norm of the gradient of the model parameter. We adopt a conjugate gradient algorithm (CGA) to solve the objective function. We used the DIGG, SIGG, and IGGW inversion methods in a dip-slab model with multiple anomalous bodies. The inversion results show that by integrating gravity and gravity gradient in inversion, more reasonable results are obtained than by inverting a single data type. Among the presented methods, the IGGW method achieved the best performance in using information contained in gravity and gravity gradient data, followed by the SIGG method, and finally, by the DIGG method. We used the methods on actual data from Vinton salt dome, southwestern Louisiana, USA. The obtained results support the abovementioned conclusions.

For airborne gravity gradient data, it is a challenge to distinguish between high-frequency intrinsic and dynamically produced noise caused by the aircraft and small-scale effects from shallow density variations. To facilitate consistent interpretation, techniques that include all of the measured gravity gradient components are particularly promising. We represented the measurements by a common potential function accounting for lateral and height variations. Thus, it was possible to evaluate the internal consistency between the measured components and to identify components with bias or particularly strong noise. As an extra benefit for data sets that contain terrain-corrected and nonterrain-corrected gravity gradient measurements at flight altitude, we estimated terrain-corrected anomalies on the topographic relief using downward continuation and retrieved nonterrain-corrected gravity gradient data suitable for inversion using upward continuation. For a field data set from northern Sweden, the largest differences (up to 50 eotvos) between the measured and estimated components of the gravity gradient data were found in areas of high topographical relief. But the average residual standard deviations of the individual components were between 3.6 and 7.4 eotvos, indicating that the components were consistent in an average sense. We have determined the successful conversion of terrain-corrected airborne gravity gradient data to Bouguer gravity data on the topographic relief using ground-based vertical gravity data as a reference. A 3D inverse model computed from the nonterrain-corrected data clearly showed the depth extent of the geologic structures observed at the surface, but it only produced a weak representation of the shallow structure. In contrast, a 2D surface density model in which only lateral variations of density in the topographic relief was allowed exhibited more realistic density distributions in fair correlation with geology.

A improved algorithm to perform channel estimation and equalization for multiple input multiple output(MIMO) systems in frequency and time-selective fading environments is put forward. lt contains two steps: training and tracking. In training phase, improved Kalman filtering, namely robust Kalman filtering(RKF), is exploited to estimate channel impulse response(CIR). After that, in tracking stage,the RKF and minimum mean-square error feedback decision equalizer (MMSE-DFE) cooperate to track the time-varying channel. The RKF recursions is presented and a closed-form solution for baud rate MIMO MMSE-DFE under perfect knowledge of CIR and correct past decisions conditions is derived. In addition, it regards unknown dc-offset due to zero intermediate frequency(IF) at the receiver as the mean of measurement noise, which is estimated as a byproduct through robust Kalman filters. Finally, it is compared with well-known ones,such as least mean-square (LMS), recursive least square (RLS), Kalman filtering (KF). All these show that the proposal exhibits better performance.

The speech enhancement is one of the effective techniques to solve speech degraded by noise. In this paper a fast speech enhancement method for noisy speech signals is presented, which is based on improved Kalman filtering. The conventional Kalman filter algorithm for speech enhancement needs to calculate the parameters of AR (auto-regressive) model, and perform a lot of matrix operations, which usually is non-adaptive. The speech enhancement algorithm proposed in this paper eliminates the matrix operations and reduces the calculating time by only constantly updating the first value of state vector X(n). We design a coefficient factor for adaptive filtering, to automatically amend the estimation of environmental noise by the observation data. Simulation results show that the fast adaptive algorithm using Kalman filtering is effective for speech enhancement.

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

A method for implementing Gaussian low-pass filter by using switched-current technique has been presented in this paper. The stable rational transfer function for Gaussian-shaped low-pass filter is approximated by the multiplication of the follower-integrator functions based on central limit theorem. An SI filter structure constructed from the inverse follow-the-leader feedback configuration has been proposed to synthesize the obtained transfer function, in which the switched-current bilinear integrator is employed as the building cell. Explicit design formulas are also derived. The 8th-order Gaussian low-pass filter is selected as the example to elaborate the design details. Simulation result shows that the proposed design method has high approximation precision and low magnitude sensitivity.

With the rapid development of aviation technology, especially in intelligent situation awareness, autonomous flight control and electric propulsion, the number of unmanned aerial vehicles (UAVs) has grown explosively, bringing challenges to low-altitude flight safety. Autonomous UAV detect and avoid (DAA) technology is a key future direction. Firstly, this paper focuses on UAV multi-sensor data fusion, proposing an asynchronous trajectory fusion method via comprehensive weighting that accounts for multiple influencing factors. Secondly, for trajectory tracking of ownship and intruders, improved Kalman filter (KF) and enhanced Sage-Husa adaptive unscented Kalman filter (AUKF) algorithms are used for vertical and horizontal tracking to enhance accuracy. Then, a hybrid method combining detect and avoid alerting logic for unmanned aircraft systems (DAIDALUS) with Markov decision process (MDP) is proposed to solve the UAV collision avoidance decision-making problem. It calculates collision avoidance strategies via the state space, takes them as MDP’s action space, sets up reward functions and state transition probabilities to build an MDP model, and explores the influence of discount factors. Finally, through experimental tests, results show the proposed UAV detection and avoidance decision method has a 27.2% efficiency increase compared to standalone DAIDALUS, the loss of well clear (LoWC) rate is only 5.8%, and the nearest distance between the intruder and the ownship is 343 m, meeting the collision avoidance requirements for UAV flight.

For the lack of current extraction methods of traffic basic data, a traffic information acquisition method based on improved Kalman filtering using traffic video was presented in this paper. The Gaussian mixture model was improved for multi-vehicle moving targets detection. In order to further improve the detection efficiency, a heuristic improvement method was proposed. For the matching problem of multiple targets in the continuous video frame, combining the vehicle movement characteristic, the Kalman filter was used to estimate the vehicle position optimally, a real-time traffic detection method of matching the target chain was proposed. Finally, the experiment was carried out with the actual transportation video, results show that the proposed method can effectively improve the noise interference and foreground blurring in Multi-target vehicle detection, and can extract the vehicle moving target information from different traffic environments with high accuracy, different models and vehicle color The lowest detection rate was 93.08%.

In this article, the event-based predictive control problem for a class of interval type-2 Takagi–Sugeno fuzzy system is investigated. To reduce the influence of Gaussian noise, an improved Kalman filter is proposed, in which the system state is filtered or not depends on an event-triggered mechanism. Then, by designing the network predictive control scheme via interval type-2 fuzzy model, some sufficient conditions are derived, which guarantee the Schur stabilization of the concerned system subject to network delays. Finally, the correctness and effectiveness are verified by two simulation examples.

The utilization of hand gesture recognition (HGR) can achieve a simpler and more humanized human–computer interaction (HCI) system, which can be used to simplify some aspects of production life and can also be used for sign language interpretation robots. However, HGR has a highly customized design. It is a very time-consuming process to design and train a separate model for each set of datasets. Therefore, designing a hand gesture information (HGI) acquisition instrument to analyze the rigid body motion and internal posture of the hand at the same time is a necessary process. This article presents a lightweight vision-based measurement (VBM) system to achieve this function. The system is implemented based on the convolutional neural network (CNN) structure, the improved Kalman filter (KF) is used to compensate error of fast moving, and the testing effect is good. After testing, the improved hand position detection system has the highest recall rate in the single-stage network. Compared with the original you look only once (YOLO)-v3, the number of parameters is reduced by 46.7M, but the recall rate is increased by 0.28%, which solves the loss of hand detection under complex background. With the help of the improved KF algorithm, the average precision (AP) metric of the prediction box is improved by 2.0 compared with the previous one. Compared with the simple baseline, the area under curve (AUC) metric of the keypoints detector is improved by 0.5%.

Accurate estimation of road roughness is crucial for vehicle dynamics analysis and road performance evaluation. To enhance the accuracy of estimating road roughness, this study introduces an estimation method utilizing the improved Kalman filter (KF) with discrete trapezoidal load. Firstly, the theoretical formulation of the vehicle response under road roughness load is derived, and the system equation is derived in a continuous time state-space form. Next, the vehicle system equation is discretized and an improved KF algorithm with trapezoidal load is proposed, along with the derivation of the structural state estimation formula. Then, a vehicle model with seven degrees of freedom (DOFs) is established for numerical calculation, with the improved KF algorithm is utilized to estimate road roughness based on the vehicle response. Finally, the estimation accuracy of the proposed method is verified via field tests involving the vehicle driving through bumps and driving on a rough road.

Terrestrial gravity anomalies selected from three extended continental regions having a smooth gravity field were used in order to determine the appropriate size of the area for gravity data collection as well as the required data-sampling for calibration of the GOCE satellite gravity gradient (SGG) data. Using Least Square Collocation (LSC), prediction of gravity gradient components was carried out at points on a realistic orbit. Based on the mean error estimation it was shown that up to 80% of the signal of the gravity gradient components, as it is expressed through the covariance function of the terrestrial gravity data, can be recovered in the case of an optimal size of the collection area and of the optimum resolution of the data. These optimal conditions e.g. for the Australian gravity field, correspond to an 10° × 12° area extend and a 5′ data-sampling. It was also numerically demonstrated that it is possible to calibrate the GOCE SGG data for systematic errors such as bias and tilt.

Over the past few decades, convolutional neural networks (CNNs) have achieved outstanding results in addressing a broad scope of computer vision problems. Despite these improvements, fully convolutional Siamese neural networks (FCSNN) still hardly adapt to complex scenes, such as appearance change, scale change, similar objects interference, etc. The present study focuses on an enhanced FCSNN based on convolutional block hyperparameters optimization, a new activation function (ModReLU) and Gaussian low pass filter. The optimization of hyperparameters is an important task, as it has a crucial ascendancy on the tracking process performance, especially when it comes to the initialization of weights and bias. They have to work efficiently with the following activation function layer. Inadequate initialization can result in vanishing or exploding gradients. In the first method, we propose an optimization strategy for initializing weights and bias in the convolutional block to ameliorate the learning of features so that each neuron learns as much as possible. Next, the activation function normalizes the output. We implement the convolutional block hyperparameters optimization by setting the convolutional weights initialization to constant, the bias initialization to zero and the Leaky ReLU activation function at the output. In the second method, we propose a new activation, ModReLU, in the activation layer of CNN. Additionally, we also introduce a Gaussian low pass filter to minimize image noise and improve the structures of images at distinct scales. Moreover, we add a pixel-domain-based color adjustment implementation to enhance the capacity of the proposed strategies. The proposed implementations handle better rotation, moving, occlusion and appearance change problems and improve tracking speed. Our experimental results clearly show a significant improvement in the overall performance compared to the original SiamFC tracker. The first proposed technique of this work surpasses the original fully convolutional Siamese networks (SiamFC) on the VOT 2016 dataset with an increase of 15.42% in precision, 16.79% in AUPC and 15.93% in IOU compared to the original SiamFC. Our second proposed technique also reveals remarkable advances over the original SiamFC with 18.07% precision increment, 17.01% AUPC improvement and an increase of 15.87% in IOU. We evaluate our methods on the Visual Object Tracking (VOT) Challenge 2016 dataset, and they both outperform the original SiamFC tracker performance and many other top performers.

The sigma-delta cellular neural network (SD-CNN) is a complete framework of a spatial domain sigma-delta modulator, and has a very high image reconstruction (AD-to-DA) performance. In this architecture, the A-template, given by a 2D low pass filter (LPF), is used for a digital to analogue converter (DAC), the C-template works as an integrator, and the nonlinear output function is for the bilevel output. By exploiting to the nonlinear optimization ability of CNN spatio-temporal dynamics, optimal binary and reconstruction image can be obtained. However, in the conventional SD-CNN, the Gaussian LPF, whose coefficients are real number, is used as the A-template. This filter coefficients requirement is one of major factors that restricts a hardware implementation. In this paper, a SD-CNN having hardware-friendly filter coefficients is proposed. Moreover its AD and DA performance is confirmed by some experiments.