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Overload-Checking and Edge-Finding for Robust Cumulative Scheduling

INFORMS JOURNAL ON COMPUTING 2023年第6期35卷 1419-1438页

作者： Fahimi, Hamed Quimper, Claude-Guy Shahid Chamran Univ Ahvaz Fac Math Sci & Comp Dept Comp Sci Ahvaz *** Iran Univ Laval Fac Sci & Genie Dept Informat & Genie Logiciel Quebec City PQ G1V 0A6 Canada

Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust if it absorbs some level of unforeseen events when at most a certain number of activities are delayed. Taking advantage of constraint programming, we present two new filtering algorithms for a constraint that models cumulative scheduling problems in robust contexts where up to r out of n tasks can be concurrently delayed while keeping the schedule valid. We adapt the overload-checking and edge-finding filtering rules for this framework. We show that our robust versions of these algorithms run in & UTheta;(r2nlog(n)) and O(r2znlog(n)), respectively, where z denotes the number of distinct capacities of all tasks. This achievement implies that the complexities of the state-of-the-art algorithms for these techniques are invariable when r is constant. Experiments illustrate that our algorithms scale, with respect to n and r. As a practical application, the experimental results on a special case of crane assignment problem also verify a stronger filtering for these methods in terms of backtrack numbers as well as computation times when used in conjunction with time tabling. Finally, in order to show that our CP-based algorithms improve to solve a robust scheduling problem, we make a comparison against temporal protection as an external robust scheduling approach.

关键词： constraint programming constraint propagation filtering algorithms robust cumulative scheduling uncertainty data structure

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Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhao, Huijun Niu, Xiaoming Cao, Huiliang Shen, Chong Wang, Chenguang Li, Jie Tang, Jun Liu, Jun North Univ China Sch Instrument & Elect State Key Lab Dynam Measurement Technol Taiyuan 030051 Peoples R China Chongqing Univ Coll Math & Stat Chongqing 400044 Peoples R China China South Ind Grp Automat Res Inst Co Ltd Mianyang 621000 Peoples R China

For the polarization compass (PC), maintaining high-precision and high-robustness autonomous measurement of the heading information under conditions where the atmospheric polarized light is being blocked or disturbed represents a challenging problem. In this article, the $E$ -vector orientation algorithm, which provides high orientation accuracy and text real-time performance in sunny weather, and the symmetric axis orientation algorithm, which offers a strong anti-interference ability in occluded environments, are integrated to solve this problem. Furthermore, an improved state error model is established to increase the accuracy of the heading estimation. To track the system state rapidly and circumvent the uncertainties caused by measurement noise variance, an improved variational Bayesian strong tracking cubature Kalman filter (VBSTCKF) is proposed to estimate both the state and the time-varying measurement noise variance accurately. In addition, a multifrequency data fusion algorithm based on residual compensation is proposed to overcome the inconsistent sampling operation frequency problems of the two orientation algorithms. The outstanding feature is that the proposed multifrequency VBSTCKF (MF-VBSTCKF) can improve the orientation precision and robustness while also ensuring the high-frequency output performance. Finally, experimental results acquired in occluded environments verify the superiority of the proposed method.

关键词： filtering algorithms Real-time systems Kalman filters Compass Robustness Noise measurement Estimation Autonomous orientation cubature Kalman filter (CKF) heading fusion polarization compass (PC)

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Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： He, Shan Wu, Panlong Li, Xingxiu Bo, Yuming Yun, Peng Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Math & Stat Nanjing 210094 Peoples R China AVIC Leihua Elect Technol Res Inst Wuxi 214063 Peoples R China

Due to the complexity, time variability, and mutability of the noncooperative maneuvering target, the dynamic tracking model of the target does not match with the actual state, which reduces the tracking accuracy or even completely tracking loss. In this article, an expectation maximization-based adaptive modified unbiased minimum-variance estimation (EM-AMUMVE) algorithm is proposed for highly maneuvering target tracking with model mismatch. First, the virtual maneuvering noise and the first-order Markov process model are integrated to quantitatively describe the maneuvering acceleration according to the boundness of the maneuvering acceleration. Then, the maneuvering acceleration and state estimation are derived based on the unbiased minimum-variance criterion, and the expectation maximization (EM) is introduced in the updating procedure of the quadratic Bayesian filter to adaptively estimate the mean and covariance of virtual maneuvering noise and thus to improve the accuracy of maneuvering acceleration estimation. Finally, the optimality of the adaptive modified maneuvering acceleration estimation is theoretically proved, and the tracking scenarios of time-varying maneuvering target with model mismatch and reentry gliding trajectory of typical hypersonic vehicle hypersonic technology vehicle 2 (HTV-2) are built to demonstrate the validness, convergence, sensitivity, and engineering practicability of the proposed algorithm.

关键词： Target tracking Adaptation models Mathematical models filtering algorithms Aerodynamics Computational modeling State estimation Expectation maximization (EM) quadratic Bayesian filter Index Terms target tracking unbiased minimum variance virtual maneuvering noise

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L2min^2/2s: Efficient Linear Reconstruction Filter for Incremental Delta-Sigma ADCs

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 3229-3241页

作者： Wang, Bo Law, Man-Kay Schneider, Jens Hamad Bin Khalifa Univ Coll Sci & Engn Doha 34110 Qatar Univ Macau Inst Microelect State Key Lab Analog & Mixed Signal VLSI Macau 999078 Peoples R China Univ Macau FST ECE Macau 999078 Peoples R China

it becomes more challenging to improve the energy efficiency of incremental delta-sigma data converters (IDCs) from the analog circuit design perspective, we propose two novel linear reconstruction filters for IDCs to enhance their performance in a digital way, including the L(2)min(2) filter and its symmetric version, the L(2)min(2s) filter. Compared to the classical linear reconstruction filters, such as the cascade-of-integrators (CoI) and cascaded integrator-comb (CIC) filter (an implementation of sinc filter), the proposed filters can achieve efficient quantization and thermal noise suppression, with the lowest thermal noise penalty factor of 1.2 among the high-order linear reconstruction filters. In this paper, we present analytical, numerical, and experimental results to demonstrate the superior performance of the filters for first-order and second-order IDC output reconstruction. The proposed filters are hardware-friendly and example digital implementations in a standard complementary metal-oxide-semiconductor (CMOS) and field-programmable gate array (FPGA) platforms are included in this paper.

关键词： Nonlinear filters Maximum likelihood detection Digital filters Quantization (signal) filtering algorithms Modulation Noise reduction Analog-to-digital data converter incremental delta-sigma ADC reconstruction filter digital linear filter thermal noise penalty L(2)min(2) L(2)min(2s) frequency notch

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FMDPgram: An Improvement of FMD for Rotating Machinery Fault Diagnosis

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Li, Hua Wang, Tianyang Zhang, Feibin Chu, Fulei Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Recently, feature mode decomposition (FMD) has been proposed and has demonstrated robust performance in the application of rotating machinery fault diagnosis. However, FMD does not have parameter adaptability, and its influencing parameters (i.e., segment number, mode number and filter length) need to be defined in advance. Inspired by the decomposition mode of wavelet packet transform, this article cleverly proposes a binary decomposition structure of FMD to form the concept of FMD packet (FMDP). Furthermore, the tiling structure of FMDP is constructed, thus forming the FMDPgram. In FMDPgram, the frequency band of the raw signal is evenly divided into 2 segments, and each FMD generates two submode components (SMCs), thus simplifying the hyperparameter optimization into a single-parameter optimization. Additionally, an indicator called narrowband peak-to-average ratio (NPAR) is proposed for optimal SMC positioning, and which is displayed in the FMDPgram tile map. First, performing FMDPgram with NPAR on the raw signal. Then, the optimal SMC is located based on the NPAR in the FMDPgram tile map. Finally, the envelope power spectrum of the optimal SMC is calculated to characterize the fault characteristic information. FMDPgram is tested on simulated and experimental data, and compared with FMD, spectral kurtosis, VMD and improved Kurtogram to evaluate its performance in rolling bearing diagnosis under low signal-to-noise ratio and non-Gaussian interference.

关键词： Finite impulse response filters Optimization Filter banks Signal resolution filtering algorithms Fault diagnosis Machinery Heuristic algorithms filtering theory Wiener filters feature mode decomposition (FMD) FMD packet (FMDP) FMDPgram main submode component narrowband peak-to-average ratio (NPAR)

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Microphone Channel Frequency Response Calibration Using Circular Shifting and Spatio-Temporal Prediction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhao, Zhao Wang, Ziyi Xu, Zhiyong Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

Microphone channel frequency response calibration (MCFRC) can compensate for frequency response mismatches among channels and has attracted increasing attention in recent years, which is accompanied by booming applications of microphone arrays. State-of-the-art MCFRC techniques translate the frequency response calibration (FRC) into the optimization of calibration filter coefficients and have shown remarkable performance. However, a compromise between inter-microphone distance and passband bandwidth shrinkage still exists, which limits the wide application of those techniques to a great extent. To address this problem, an MCFRC method using circular shifting and spatio-temporal prediction is proposed in this article. First, all microphones from uncalibrated channels are deployed in a rotating symmetric arrangement, and circular shifting is conducted, making each channel experience all different predetermined positions. Then, for each channel, a spatio-temporal prediction technique is utilized to separately enhance those segments containing received calibration signals at different positions. Next, the summation of all enhanced segments for each channel is calculated, which can almost ensure that differences among microphone channel (MC) outputs are only caused by channel frequency response mismatches. Finally, the Newton algorithm is employed to design the calibration filter coefficients corresponding to each channel. Simulation results reveal that the proposed approach considerably outperforms existing calibration methods across various signal-to-noise ratio (SNR), reverberation time, and inter-microphone distance conditions. Meanwhile, the passband bandwidth shrinkage problem is well handled. Real-world experiments also verify its effectiveness.

关键词： Calibration Frequency response Microphone arrays Passband Bandwidth Wiener filters filtering algorithms Circular shifting frequency response calibration (FRC) microphone channel (MC) spatio-temporal prediction

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A New Strategy for Combining Nonlinear Kalman Filters With Smooth Variable Structure Filters

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IEEE ACCESS 2023年 11卷 146262-146281页

作者： Akhtar, Salman Setoodeh, Peyman Ahmed, Ryan Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada

Bayesian filters exemplified by the celebrated Kalman Filter (KF), and its non-linear variants rely on a fairly accurate state-space model of the system under study. To address the issue of modelling uncertainty in state estimation, the Smooth Variable Structure Filter (SVSF) was proposed in 2007. Since then, several SVSF variants have been proposed to extend its domain of applicability. In some of these algorithms, SVSF has been viewed as a complementary approach alongside the well-established nonlinear Kalman Filters. This paper seeks a general framework for SVSF formulation to unify some of the recent developments in SVSF literature under one umbrella. In this way, the SVSF variants are revisited as special cases of the proposed framework. This paper proposes a new strategy to combine SVSF filters with other nonlinear filters and puts existing SVSF filters under one umbrella. Six filters are formulated based on the proposed method of combining filters. The proposed filters relax limitations of existing SVSF variants, making the proposed filters more universal. In simulations, the new filters outperform state-of-the-art nonlinear KFs and some existing SVSF filters. To demonstrate the merits of the proposed framework, the new filters are applied to target tracking and are comparatively evaluated.

关键词： filtering theory Target tracking filtering algorithms Kalman filters Noise measurement Covariance matrices Uncertainty state estimation estimation theory smooth variable structure filter variable structure systems maneuvering targets

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A Bidirectional Guided Filter Used for RGB-D Maps

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhang, Ruijie Wu, Jun Tsinghua Univ State Key Lab Tribol Adv Equipment Beijing 100084 Peoples R China Tsinghua Univ Inst Mfg Engn Dept Mech Engn Beijing 100084 Peoples R China Beijing Key Lab Precis Ultraprecis Mfg Equipment & Beijing 100084 Peoples R China

The red-green-blue-depth (RGB-D) cameras are widely used in the fields of robotic spray painting, packaging, and tracking. However, the quality of the depth maps of RGB-D maps is relatively low. Guided filters utilizing the color image as a guide map is a common method to deal with these problems. However, the noise in guidance will make the filtering output contains unexpected noise that does not contain before filtering. In this article, based on the classical guided filter, a bidirectional guided filter is proposed to filter the filtering input and the guidance simultaneously. The basic equations are proposed and the filter kernel is derived. The properties of the proposed bidirectional guided filter, such as edge-preserving filtering property, insensitivity to the noise of guidance, and filtering effects on guidance, are investigated. Four different groups of experiments are performed to further verify the effectiveness of the proposed method. The classical guided filter and the bidirectional guided filter are compared to verify that the proposed bidirectional guided filter can reduce the impact of the noise of the guidance;and the bidirectional guided filter and the box mean filter are compared to verify the filtering effect on guidance of the proposed bidirectional guided filter.

关键词： Mathematical models Information filters Image edge detection Cameras Kernel filtering algorithms Robot vision systems 3-D vision edge-preserving filtering guided filter machine vision red-green-blue-depth (RGB-D) map

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Gaussian Synthesis for High-Precision Location in Oriented Object Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Li, Zhonghua Hou, Biao Wu, Zitong Ren, Bo Ren, Zhongle Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China

In aerial image scenes, the objects have properties of arbitrary orientation, large-scale range, and dense distribution. Thus, the object detector uses an oriented bounding box (OBB) to locate objects, which is more complex and challenging than a horizontal bounding box (HBB) detector. Mainstream OBB detectors mostly use a one-to-many label assignment strategy to predict multiple bounding boxes for the same object and filter out repeat predictions by nonmaximum suppression (NMS). NMS ranks with confidence and drops the detection box with intersection over union (IoU) higher than the threshold, which makes it easy to get the local optimum result. The clustered synthesis method gets more accurate results than the original NMS, but applying it to the OBB detector leads to border shift, which arises from the angular discontinuity problem. Therefore, we use Gaussian OBB (G-OBB) to deal with the angular discontinuity and thus eliminate the offset generated by direct synthesis. G-OBB is not easy to understand and describe representation. For this reason, we analyze the properties of G-OBB and design a decoding method to convert a G-OBB to a rotated rectangular box, further discussing its conditions. Based on the decoding method, we propose a Gaussian synthesis (GauS) algorithm, which transforms the OBB into Gaussian space, followed by synthesis, and finally transforms the synthesis result back into a new OBB. We have derived the synthesis and decoding methods and further verified their effectiveness. The extensive experiments on several existing models show that GauS takes very little computation and improves the detector's high-precision performance. Extensive experiments verify the effectiveness, stability, and universality of the proposed algorithm. In addition, the RTMDet using GauS achieves a performance of 81.61 AP50 and gains a 0.39% improvement in mean average precision (mAP), which achieves the state-of-the-art (SOTA) performance. Our implementation is available a

关键词： Detectors Object detection Prediction algorithms Decoding Shape Task analysis filtering algorithms Aerial object detection Gaussian synthesis (GauS) nonmaximum suppression (NMS) oriented object detection postprocess

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Joint Design of Transmit Sequence and Receive Filter Based on Riemannian Manifold of Gaussian Mixture Distribution for MIMO Radar

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Chen, Xixi Wu, Hao Cheng, Yongqiang Feng, Weike Guo, Yifan Air Force Engn Univ Air Def & Antimissile Sch Xian 710051 Shaanxi Peoples R China Natl Univ Def Technol Coll Elect Sci Changsha 410073 Hunan Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China

To improve target detection performance in non-Gaussian backgrounds, the joint design of transmit sequence and receive filter for multiple-input-multiple-output (MIMO) radar is studied. By approximating the probability density function of observed non-Gaussian data with the Gaussian mixture model (GMM), a Riemannian manifold of Gaussian mixture distribution is developed to depict the complicated background first. Then, maximizing the geometric distance on manifolds, which is converted by maximizing the discrimination between the target and clutter, is proposed as the criterion for the joint design of transmit sequence and receive filter. Thereby, under the constant-modulus constraint, the joint design problem can be transformed into an optimization problem. However, the proposed optimization problem is nonconvex and constrained. To solve this problem, a Riemannian optimization framework is provided. By taking the advantage of the underlying geometric and algebraic structure of the constraint space, the original constrained optimization problem in Euclidean space can be transformed into the unconstraint optimization problem over Riemannian product manifolds. Moreover, to obtain the global optimal solution, the Riemannian gradient of the geometric distance cost is derived for the conjugate gradient algorithm. Experiments demonstrate that the proposed method shows advantages in detection performance compared with competitive methods.

关键词： Manifolds Optimization Clutter MIMO radar Object detection filtering algorithms filtering theory Complex circle manifold Gaussian mixture model (GMM) joint design manifold optimization matrix information geometry detection multiple-input-multiple-output (MIMO) radar product manifold

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