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Multi-domain collaborative filter for interference suppressing

IET signal processing 2016年第9期10卷 1157-1168页

作者： Mao, Xing-Peng Yang, Yun-Long Hong, Hong Deng, Wei-Bo Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Harbin Inst Technol Collaborat Innovat Ctr Informat Sensing & Underst Harbin 150001 Peoples R China

Filters with antenna arrays are widely used for interference suppression in the temporal domain, frequency domain, space domain and so on. Meanwhile, the interference mitigation performance of a single-domain-based filter depends on a noticeable difference between the target signal and interference in the corresponding domain. However, the interference cannot be efficiently suppressed by a single-domain-based filter when the difference in this individual domain is small. To solve this problem, a multi-domain collaborative oblique projection filter is proposed in this study. First, multi-domain spaces are theoretically derived to distinguish the target signal and the interference, and then a multi-domain oblique projection operator is provided to recover the original target and suppress the interference. Depending on the multi-domain spaces, the filter has excellent performance when the difference is insignificant in each individual domain, whereas the performances of the single-domain-based filter and cascade filter degrade significantly. Finally, a space-polarisation-frequency domain collaborative filter based on oblique projection is given as an illustration. Performance analysis and simulation results are provided to illustrate the superiority of the proposed filter for interference mitigation.

关键词： space domain analysis collaborative filtering multidomain collaborative oblique projection filter Electromagnetic compatibility and interference space-polarisation-frequency domain collaborative filter Mathematical analysis frequency-domain analysis interference suppression antenna array temporal domain analysis frequency domain analysis interference (signal) filtering theory filtering methods in signal processing signal processing theory single-domain-based filter interference mitigation performance cascade filter

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Ellipsoidal state-bounding-based set-membership estimation for linear system with unknown-but-bounded disturbances

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IET CONTROL THEORY AND APPLICATIONS 2016年第4期10卷 431-442页

作者： Liu, Yushuang Zhao, Yan Wu, Falin Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100191 Peoples R China

This study proposes a fast and stable recursive ellipsoidal state-bounding-based set-membership estimation algorithm for the state estimation of linear system with unknown-but-bounded (UBB) disturbances. The proposed algorithm has a prediction-correction structure in time update and observation update, which is similar to Kalman filter. For both time update and observation update, there is a data-depending weighting factor. The observation weighting factor is acquired by minimising the upper bound on a Lyapunov function of the posteriori estimation error. It has been demonstrated that the estimation error of the proposed algorithm is input-to-state stable when the system is uniformly observable. With the property of selective observation update, the proposed algorithm not only improves the point estimation accuracy but also decreases the computation load of observation weighting factors and the volume of feasible ellipsoid sets. The efficiency of the proposed algorithm has been demonstrated by numerical simulations with different UBB disturbances.

关键词： signal processing theory numerical simulations Other topics in statistics time update ellipsoid sets Kalman filters prediction-correction structure point estimation accuracy stable recursive ellipsoidal state-bounding-based set-membership estimation algorithm Stability in control theory observation weighting factors posteriori estimation error state estimation Lyapunov function data-depending weighting factor input-to-state stable selective observation update Lyapunov methods UBB disturbances filtering methods in signal processing unknown-but-bounded disturbances estimation theory linear system Kalman filter

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Set of uniquely decodable codes for overloaded synchronous CDMA

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IET COMMUNICATIONS 2016年第10期10卷 1236-1245页

作者： Singh, Amiya Amini, Arash Singh, Poonam Marvasti, Farokh Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela India Sharif Univ Technol ACRI Dept Elect Engn Tehran Iran

In this study, the authors consider the designing of a new set of uniquely decodable codes for uncoded synchronous overloaded code division multiple access for the number of codes exceeding the assigned code length. For the construction, the proposed recursive method at iteration-k generates a matrix that can be classified into k orthogonal subsets of different dimensions. Out of them, all besides the largest (binary Hadamard) one are ternary in nature. There resides an inbuilt twin tree structured cross-correlation hierarchy that facilitates an advantageous balance between the auto and intergroup cross-correlation for the signatures in a subset. This opportunity is further leveraged by the proposed multi-stage detector to maintain the uniquely decodable (errorless) nature of the matrices for noiseless transmission. The simple logic of matched filtering serving as the basic designing block of the decoder provides an enormous saving over the complexity of optimum maximum likelihood decoder. For the noisy channel, the authors derive the theoretical expression of the average bit error rate for the individual subset. Moreover, the authors explain the role of the two factors (cardinality of the subset, and net level of interference) in being responsible for the non-uniformity in the order of their error performance.

关键词： matched filtering Electromagnetic compatibility and interference maximum likelihood decoding Linear algebra (numerical analysis) code length error statistics optimum maximum likelihood decoder auto cross-correlation correlation methods Codes twin tree structured cross-correlation hierarchy code division multiple access multistage detector matrix algebra matched filters codes Combinatorial mathematics Multiple access communication binary Hadamard iterative methods set theory filtering methods in signal processing interference net level uniquely decodable codes overloaded synchronous CDMA subset cardinality intergroup cross-correlation uncoded synchronous overloaded code division multiple access average bit error rate k orthogonal subsets recursive method Interpolation and function approximation (numerical analysis) Radio links and equipment noiseless transmission filtering theory radiofrequency interference Other topics in statistics

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Image enhancement with Gaussian filtering in time-domain microwave imaging system for breast cancer detection

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ELECTRONICS LETTERS 2016年第5期52卷 342-343页

作者： Kwon, S. Lee, H. Lee, S. Ewha Womans Univ Dept Elect Engn 11-1 Daehyun Dong Seoul 120750 South Korea

A time-domain microwave imaging system with improved image quality is presented for early detection of breast cancer. The time-domain system has the advantage of short scan time compared with the frequency-domain system but suffers from low signal-to-noise ratio (SNR) due to high attenuation of the RF signal within the breast tissue. signal averaging of repeated measurements can improve the SNR, but this method also smears the signal peaks due to the signal-averaged jitter, which adversely affects the reconstructed image quality. An image enhancement method using Gaussian band-pass filtering (BPF) is proposed for a time-domain imaging system. The results from the proposed method were compared with those from the deconvolution method, which is typically used to remove the effects of signal-averaged timing jitter in short pulse measurements. Simulation results with a 3D hemispherical homogeneous breast model containing tumour cells revealed improvement of the reconstructed image quality with the proposed method in the presence of timing jitter. The signal-to-mean ratio was increased from 6.46 to 14.15 with BPF, whereas deconvolution resulted in an increase to 9.36. Simulation results with a non-homogeneous realistic breast model also showed image enhancement with the proposed method.

关键词： medical image processing Mathematical analysis time-domain analysis Patient diagnostic methods and instrumentation time-domain microwave imaging system breast cancer detection Gaussian bandpass filtering image enhancement method Probability theory, stochastic processes, and statistics tumour cells Biology and medical computing breast cancer health physics Computer vision and image processing techniques breast tissue image reconstruction Other topics in statistics improved image quality signal-averaged timing jitter Biomedical measurement and imaging Medical and biomedical uses of fields, radiations, and radioactivity Optical, image and video signal processing band-pass filters filtering methods in signal processing BPF 3D hemispherical homogeneous breast model image enhancement Gaussian processes

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Two-stage constant false alarm rate detection for distributed multiple-input-multiple-output radar

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IET RADAR SONAR AND NAVIGATION 2016年第2期10卷 264-271页

作者： Hu, Qinzhen Su, Hongtao Zhou, Shenghua Liu, Ziwei Yang, Yang Liu, Jun Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

In this study, the authors consider a two-stage constant false alarm rate (TS-CFAR) detection problem in a distributed multiple-input-multiple-output radar. This study is motivated by the fact that the clutter plus noise power and the signal to clutter plus noise ratio may be both unknown in practice. Two TS-CFAR detectors are proposed, that is, TS generalised likelihood ratio test detector and TS adaptive matched filter detector. Each of the proposed detectors has two thresholds: the first one called local threshold controls the communication rate and the second one called global threshold adjusts the global probability of false alarm. It is proved that both of the proposed detectors have CFAR property. Numerical results illustrate that the two proposed detectors have good detection performance at a low communication rate.

关键词： adaptive filters global probability global threshold distributed multiple-input-multiple-output radar probability matched filters Radar equipment, systems and applications TS-CFAR detection problem signal detection clutter plus noise power filtering methods in signal processing TS adaptive matched filter detector two-stage constant false alarm rate detection problem TS generalised likelihood ratio test detector local threshold signal to clutter plus noise ratio radar detection MIMO radar Other topics in statistics

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Accurate range profile alignment method based on minimum entropy for inverse synthetic aperture radar image formation

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IET RADAR SONAR AND NAVIGATION 2016年第4期10卷 663-671页

作者： Wang, Rui Zeng, Tao Hu, Cheng Yang, Jian Long, Teng Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Accurate range profile alignment is an essential step for achieving the subsequent azimuth coherent processing for inverse synthetic aperture radar image formation. In this study, an iterative method based on minimum entropy is first proposed for accurate range profile alignment, which constructs a series of local quadratic curves to gradually approach the extremum of the entropy of range profiles. The accuracy of range profile alignment depends on whether the iteration could be convergent to the optimal extremum. Unfortunately, the entropy of misaligned range profiles usually has numerous local extrema. The low-pass profile filtering is able to smooth the entropy surface and eliminate the local extrema. Nevertheless, the detailed features of range profiles are also lost due to the filtering so that the alignment accuracy could be reduced. Finally, a circulation cascade processing by appropriately combining the proposed iterative method and the low-pass filtering is presented to make the range profile alignment both convergent and accurate. Simulations and real data are used to validate the performance of the proposed method on iteration convergence and alignment accuracy.

关键词： local extrema elimination Radar theory convergence of numerical methods range profile alignment method circulation cascade processing inverse synthetic aperture radar image formation radar imaging subsequent azimuth coherent processing Optical, image and video signal processing Radar equipment, systems and applications minimum entropy methods iterative methods local quadratic curve filtering methods in signal processing entropy surface smooth minimum entropy method synthetic aperture radar iteration convergence method image filtering Interpolation and function approximation (numerical analysis) low-pass filters low-pass profile filtering

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Video stabilisation based on modelling of motion imaging

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IET IMAGE processing 2016年第3期10卷 177-188页

作者： Yu, Jialin Xiang, Ke Wang, Xuanyin Cao, Songxiao Zhang, Yang Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou 310003 Zhejiang Peoples R China

This study proposes a novel digital video stabilisation scheme based on modelling of motion imaging (MI). The modelling of MI eliminates the speed motion as a result of a moving car, which is ignored in other models such as rotation + translation model, and estimates movement parameters of the background in video sequences captured from cameras mounted on moving cars. The authors first analyse the MI to understand the principle of the effects of car motion on MI, and select the matching method according to the proposed model. Then, they employ symmetric points to remove the speed motion. Finally, unwanted motion vector is stimulated by employing adaptive step-length filter, and the boundary compensating approach is employed to suppress the image jitter effectively. Their major contribution is the elimination of the effect of carrier's speed in motion estimation. Other contributions include new robust block matching approach and adaptive-step selection for motion filtering. They conduct experiments on real videos and artificial data. Experiments on real videos show that the proposed model can remove the effect of car motion, whereas the experiments on artificial data are conducted for theoretical analysis.

关键词： adaptive step-length filter image jitter suppression filtering methods in signal processing MI modelling image filtering image capture video signal processing Image recognition video camera adaptive-step selection Computer vision and image processing techniques robust block matching approach moving car motion estimation carrier speed effect elimination image matching image sequences adaptive filters video sequence capture boundary compensating approach motion imaging modelling Video signal processing image denoising stability motion filtering digital video stabilisation scheme

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State estimation for networked systems with a Markov plant in the presence of missing and quantised measurements

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IET CONTROL THEORY AND APPLICATIONS 2016年第5期10卷 599-606页

作者： Niu, Yingjun Dong, Wei Ji, Yindong Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

This study is concerned with the problem of state estimation for networked systems with a Markov plant considering the measurement uncertainties. The measurements suffer from both the randomly occurring missing phenomenon and the quantisation effects. Taking into account the statistical knowledge of the quantised measurements, an approximate minimum mean square error estimate algorithms is derived based on Gaussian assumption, which is referred to as interacting multiple model Monte Carlo (IMMMC) algorithm. A quantised measurement expectation calculated by Monte Carlo sampling method is embedded into the Kalman filter under the IMM framework. A simulation example is provided demonstrating that IMMMC is computationally appealing and presents better estimate performance than the previous algorithms. Moreover, IMMMC has better mode following ability and can clearly distinguish the occurrence of measurements missing.

关键词： Gaussian assumption quantised measurement expectation interacting multiple model Monte Carlo algorithm Gaussian processes IMMMC algorithm measurement uncertainties approximate minimum mean square error estimate algorithms Monte Carlo methods Simulation, modelling and identification Markov processes Multivariable control systems measurement uncertainty mean square error methods filtering methods in signal processing signal processing theory networked control systems Monte Carlo sampling method networked systems Kalman filters statistical knowledge Interpolation and function approximation (numerical analysis) sampling methods Markov plant state estimation Kalman filter

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Probability hypothesis density filter with adaptive estimation of target birth intensity

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IET RADAR SONAR AND NAVIGATION 2016年第5期10卷 901-911页

作者： Zhu, Youqing Zhou, Shilin Zou, Huanxin Ji, Kefeng Natl Univ Def Technol Coll Elect Sci & Engn Dept Informat Engn Changsha 410073 Hunan Peoples R China

Target birth intensity, which plays a role similar to track initialisation, is an important part of the probability hypothesis density (PHD) filter. Inmost papers, the intensity is always known as a priori, but it is too restrictive for real application. Besides, existing algorithms for the birth intensity estimation only consider the measured component of the target state (e.g. position), but the unmeasured component (e.g. velocity) is viewed as a priori or modelled by a simple distribution. As a result, they are not efficient enough to represent the initial states of newborn targets. To overcome that an adaptive estimation method combining with the single-point and two-point difference track initialisation algorithms is proposed. The target birth intensity is approximated by a Gaussian-mixture (GM) form whose means are equal to the locations and velocities of the candidate newborn targets, so that the estimated intensity can be closer to the truth. On the basis of that both the GM and sequential Monte Carlo implementations of the extended PHD filter are presented in this study. Experiment results show that the proposed method can effectively estimate the target birth intensity and has a better performance in the simulated scenarios.

关键词： estimated intensity filtering methods in signal processing birth intensity estimation newborn targets probability hypothesis density filter Gaussian-mixture adaptive estimation method Radar equipment, systems and applications Gaussian processes target tracking GM Monte Carlo methods sequential Monte Carlo filtering theory PHD filter target birth intensity

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Histogram-based cost aggregation strategy with joint bilateral filtering for stereo matching

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IET COMPUTER VISION 2016年第3期10卷 173-181页

作者： Fu, Limei Peng, Guohua Song, Weijie Northwestern Polytech Univ Sch Sci Xian 710072 Peoples R China

The edge-aware bilateral filter has been demonstrated to be effective for preserving depth edges, and disparity maps obtained from Fast Bilateral Stereo (FBS) have enhanced the efficiency of algorithm and the robustness to noise. However, they also lead to a non-perfect localisation of discontinuities. To overcome this issue, a new bilateral filtering based cost aggregation utilising colour statistical classification and similarity measurement within annular blocks is proposed in this study. We have adopted the similarity of histograms evaluated by Earth Mover Distance (EMD) to obtain the raw matching cost in the raised annular block, since histograms are very effective and efficient in capturing the distribution characteristics of visual features. For the weights aggregation, the spatial weight is assumed to be a constant. The colour weight is calculated by using a cluster-mean-value strategy, which is implemented by the local colour histogram. It improves the accuracy in the discontinuous areas. Computation redundancy is reduced by disparity candidate selection using the local minimal relevancy in the corresponding annular blocks. We use the efficiency and accuracy to demonstrate the performance of our proposed method. Experimental results have shown that the proposed method reduces the mismatch at depth discontinuous and the computation complexity significantly.

关键词： cluster-mean-value strategy filtering theory Other topics in statistics image matching histogram-based cost aggregation strategy Image recognition statistical analysis similarity measurement stereo matching Earth mover distance edge-aware bilateral filter computation complexity Computer vision and image processing techniques filtering methods in signal processing image enhancement disparity candidate selection stereo image processing image colour analysis colour histogram statistical classification bilateral filtering based cost aggregation strategy

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