Particle filters for positioning, navigation, and tracking

A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars     

A fast convergence algorithm for sparse-tap adaptive FIR filters identifying an unknown number of dispersive regions

This paper proposes a fast convergence algorithm for sparse-tap adaptive finite impulse response (FIR) filters to identify an unknown number of multiple dispersive regions. Coefficient values and tap-positions of the adaptive filter are simultaneously controlled. A constrained region for new-tap positions is selected from equisize subgroups of all possible tap-positions, and it hops from one subgroup to another to cover multiple dispersive regions. The hopping order and the stay time for each subgroup are adaptively determined based on the absolute coefficient values. Simulation results with colored signals show that the proposed algorithm saves more than 80% in the convergence time over the full-tap NLMS and 50% over the STWQ. Tracking capability of the proposed algorithm exhibits its superior characteristics. These characteristics are confirmed by hardware evaluations with a telephone network simulator.     

一种未知信源数的高分辨DOA估计算法

提出了一种未知信源数的高分辨 DOA 估计算法。该算法在未知信源数的情况下,利用线性预测(LP)法或 Pisarenko 法与 ASPECT 技术相结合来实现高分辨谱估计。该算法在消除 LP 法或 Pisarenko 法 DOA 估计中存在的伪峰的同时可判断出入射信源数,明显减小 DOA 估计算法的运算量,并可提高谱分辨力。计算机仿真结果证明了新算法理论的正确性和有效性。     

Rotation-aware correlation filters for robust visual tracking

Recent years have witnessed several modified discriminative correlation filter (DCF) models exhibiting excellent performance in visual tracking. A fundamental drawback to these methods is that rotation of the target is not well addressed which leads to model deterioration. In this paper, we propose a novel rotation-aware correlation filter to address the issue. Specifically, samples used for training of the modified DCF model are rectified when rotation occurs, rotation angle is effectively calculated using phase correlation after transforming the search patch from Cartesian coordinates to the Log-polar coordinates, and an adaptive selection mechanism is further adopted to choose between a rectified target patch and a rectangular patch. Moreover, we extend the proposed approach for robust tracking by introducing a simple yet effective Kalman filter prediction strategy. Extensive experiments on five standard benchmarks show that the proposed method achieves superior performance against state-of-the-art methods while running in real-time on single CPU.     

基于空间正则化约束的支持向量相关滤波器目标跟踪方法

基于支持向量相关滤波器(Support Correlation Filters,SCF)的目标跟踪方法存在严重的样本边界不连续问题,因此模型判别能力受到严重限制。本文将空间正则化项引入到SCF中,提出了基于空间正则化约束的支持向量相关滤波器(Spatially Regularized SCF,SRSCF)模型。相比于SCF,SRSCF不仅可以借助更大的图像区域进行模型学习,同时也能缓解样本的边界不连续问题对模型学习的负面影响,由此得到判别能力更强的模型。此外,本文提出了一种ADMM(Alternating Direction Method of Multiplier)算法求解SRSCF模型,其中每个子问题具有解析解。实验结果表明,相较于SCF,SRSCF能够有效地提升跟踪精度,同时仅增加较少的计算开销。     

采用混合t分布粒子滤波器的视觉跟踪

由于目标数量的变化,观测数据的岐义性和目标间的遮挡,多目标视觉跟踪问题面临多种困难.基于目标分布的有限t分布混合模型提出了一种混合t分布粒子滤波器以实现多目标跟踪.在算法中,每个被跟踪目标指派一个独立的粒子滤波器,显式处理当新目标出现在场景中时对应粒子滤波器的初始化,当被跟踪目标消失时,对应粒子滤波器的删除.混合t分布...     

On-line subspace algorithms for tracking moving sources

Proposes a class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors. The proposed methods estimate the DOA using only linear operations on the data, and can hence be implemented in a very efficient manner. Furthermore, these methods can accommodate more general noise models than the spatially white noise model commonly used in the literature. Large sample expressions are derived for the variance of the estimates obtained by using the proposed techniques. A comparative statistical study is performed in which comparisons against MUSIC are considered. It is found that usually MUSIC offers slightly more accurate DOA estimates at the cost of an increased computational burden and a more restrictive noise model. The paper includes simulation results lending support to the theoretical results obtained     

Multi-frame co-saliency spatio-temporal regularization correlation filters for object tracking

The spatial regularization weight of the correlation filter is not related to the object content and the model degradation in the tracking process. To solve this problem, a new multi-frame co-saliency spatio-temporal regularization correlation filters (MCSRCF) is proposed for visual object tracking. To the best our knowledge, this is the first application of co-saliency regularization to CF-based tracking. In MCSRCF, grayscale features, directional gradient histogram (HOG) features and CNN features are extracted to improve the tracking precision of the tracker. Secondly, the three-dimensional spatial saliency and semantic saliency are introduced to obtain the initial weight of the spatial regularization with object content information. Then, the heterogeneous saliency fusion method is exploited to add a co-saliency spatial regularization term to the objective function to make the spatial penalty weight learn the change of the object region. In additional, the temporal saliency regularization is introduced to learn the information between adjacent frames, which reduces the overfitting effect caused by inaccurate samples. A variety of evaluations are conducted on public benchmarks, and the experimental results show that the proposed tracker achieves good robustness against many state-of-the-art trackers in various complex scenarios.     

Noise sources and calculation techniques for switched capacitor filters

The noise response of switched capacitor networks (SCNs) is reviewed with emphasis on simplifying approximations suitable for SPICE noise simulation. The techniques developed cover all op-amp noise sources, as well as capacitor switching noise. The close agreement between predicted and measured noise responses for several monolithic SCNs bears out the validity of these simulation techniques.     

Occam filters for stochastic sources with application to digitalimages

An Occam filter employs lossy data compression to separate signal from noise. Previously, it was shown that Occam filters can filter random noise from deterministic signals. Here, we show that Occam filters can also separate two stochastic sources, depending on their relative compressibility. We also compare the performance of Occam filters and wavelet-based denoising on digital images     

Particle filters for state estimation of jump Markov linear systems

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods     

Nonlinear observer-based adaptive tracking control for inductionmotors with unknown load

In this paper, we propose a nonlinear observer-based adaptive controller for induction motors with unknown load. With the use of the skew-symmetric property of induction motors, a two-stage design technique is applied to construct an observer-based controller for velocity tracking control. To demonstrate the effectiveness of the proposed scheme, a voltage-control type of drive system is set up to perform the task of velocity tracking. The main computing facility consists of two personal computers, PC 486 and PC 286, of which one is to perform the calculation of the control law and the other is to provide the function of pulse width modulation (PWM) and to generate the gating pulses. Satisfactory experimental results are shown in the paper     

Conditional filters for image sequence-based tracking--application to point tracking.

In this paper, a new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence-based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model that is considered for point tracking combines a state equation relying on the optical flow constraint and measurements provided by a matching technique. Based on this, two point trackers are derived. The first one is a linear tracker well suited to image sequences exhibiting global-dominant motion. This filter is determined through the use of a new estimator, called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a conditional particle filter. It allows tracking of points whose motion may be only locally described. These conditional trackers significantly improve results in some general situations. In particular, they allow for dealing with noisy sequences, abrupt changes of trajectories, occlusions, and cluttered background.     

MLSE for correlated diversity sources and unknown time-varyingfrequency-selective Rayleigh-fading channels

Yu and Pasupathy (see ibid., vol.43, p. 1534-44, 1995) derived a maximum-likelihood sequence estimation (MLSE) receiver structure for unknown time varying frequency-selective Rayleigh-fading channels and uncorrelated diversity sources. This receiver design is extended to the case of correlated diversity sources. Correlated diversity sources typically arise with space diversity, where constraints on antenna volume require that diversity antennae be placed too closely together. Analytic and simulated bit-error rate (BER) curves are presented for receivers which exploit and ignore the correlation. In the former case, we find a small BER improvement that reduces with decreasing correlation. However, for a fixed receiver complexity, superior performance is achieved when the correlation is ignored     

Application of multiresolution spatial filters to long-axis tracking

A method was developed for automatically tracking the long axis of thin objects which have nonuniform width and arbitrary orientation in a two-dimensional image space. This method is used to determine the length of isolated contractile smooth muscle cells, but has applications in other medical areas such as angiographic imaging. Pattern recognition techniques that determine object size and orientation are used to identify the long axis of an imaged object from its responses to difference of Gaussian and orientation filters. This method needs no a priori knowledge of object location, adapts to varying image magnification, requires little human interaction, and yields reproducible results.     

基于模拟退火的粒子滤波在目标跟踪中的应用

针对目标跟踪中的机动目标跟踪,提出了一种基于模拟退火（SA,simulated annealing）的粒子滤波（PF,particle filter）跟踪算法（SAPF）。通过采用SA方法改进重要抽样密度函数,在状态转移基础上增加扰动量,依据Metropolis准则解决粒子的贫乏问题,扩展粒子的局部搜索范围;以降温设计...     

基于DSP利用粒子滤波算法实现目标跟踪

以TMS320DM643数字信号处理器(DSP)为核心,构建了高性能的视频处理平台,并利用该硬件平台实现粒子滤波(PF)算法完成对目标的跟踪。介绍了视频处理平台的基本结构和核心器件,并阐述了DSP实现运动目标跟踪的软件设计。实验表明,该系统能够实时跟踪目标,且相对于传统的图像采集卡与计算机结合的处理系统,具有体积小、低功耗、模块化和移动性好的特点,容易集成到各种移动设备中实现自主导航、安防监控等功能。     

Universal compression of memoryless sources over unknown alphabets

It has long been known that the compression redundancy of independent and identically distributed (i.i.d.) strings increases to infinity as the alphabet size grows. It is also apparent that any string can be described by separately conveying its symbols, and its pattern-the order in which the symbols appear. Concentrating on the latter, we show that the patterns of i.i.d. strings over all, including infinite and even unknown, alphabets, can be compressed with diminishing redundancy, both in block and sequentially, and that the compression can be performed in linear time. To establish these results, we show that the number of patterns is the Bell number, that the number of patterns with a given number of symbols is the Stirling number of the second kind, and that the redundancy of patterns can be bounded using results of Hardy and Ramanujan on the number of integer partitions. The results also imply an asymptotically optimal solution for the Good-Turing probability-estimation problem.     

Simple RC active filters using controlled sources

The letter outlines a method of synthetising RC active filters by a cascade of simple second-order stages isolated by voltage-or current-controlled current sources of low gain. Second-order stages are described having bandpass, lowpass and highpass responses and transmission zeros at finite frequencies. All the active circuits are based on simple parallel or series LC resonant circuits.