一种基于分步式滤波的数据融合算法

本文提出了一种基于分步式滤波的多传感器动态系统数据融合算法.在由多传感器组成的分布式动态系统中,当对目标状态的所有观测值到来时,首先基于系统先前信息对该时刻目标状态进行预测估计,利用Kalman滤波器和各局部观测值依次对该时刻目标状态的估计值进行更新,从而得到该时刻目标状态基于全局信息的融合估计值.文中详细推证了融合算法的具体形式,并与传统的集中式数据融合算法在计算复杂度上进行了比较,计算机仿真表明该算法与传统的集中式算法对目标状态具有相同的估计精确度.     

基于有理数倍采样的异步数据融合算法研究

本文研究了一类具有不同采样率的分布式多传感器动态系统的数据融合问题,针对一类采样率呈有理数倍关系的动态系统,提出一种基于多源异步采样数据的新融合算法.新算法首先是将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行映射统一;其次,以对目标状态下一时刻的预测值与目标在该时刻状态的估计值之差为基础,建立起描述该融合周期内各个观测点处的目标状态向量之间的动态模型;然后,以该时刻目标状态基于全局信息的估计值为条件,结合建立的新模型和传统的Kalman滤波器,利用本周期内按序到达的各传感器观测值,依次对各个观测点处目标的状态进行估计和更新;最后,在顺序得到本周期内各个观测点处目标估计值的同时,也将获得下一时刻目标状态基于全局信息的估计值或预测估计值.文中在给出新算法基本思想的同时,也较为详细地对融合算法进行了推导,并通过计算机仿真的方法,将新算法与基于时间校准的算法在估计精确度上进行了比较,从而验证了新算法的有效性.     

一类多速率动态系统的异步数据融合算法

针对不同传感器以不同采样率、异步对同一目标进行观测的一类线性时不变动态系统,给出了一种有效的状态融合估计方法.利用该方法进行状态估计,首先根据多尺度系统理论,针对每一个传感器分别建立起相应的系统模型;然后利用Kalman滤波和有反馈分布式融合结构进行数据融合并给出状态估计.该方法避免了插值以及状态和观测的扩维,具有较好的实时性.理论分析和仿真结果均表明,融合估计结果在估计误差方差最小意义下,优于最高采样率的传感器Kalman滤波的结果,融合算法是有效的.     

多传感器单模型动态系统多尺度数据融合

利用多尺度分析的思想,将基于模型的动态系统分析方法与基于统计特性的多尺度信号变换方法相结合,建立起目标状态基于多源观测信息的多尺度数据融合估计新算法,在每个尺度上获得目标状态基于全体细尺度上传感器测量信息的融合估计值,此算法可有效地应用于拥有不同采样速率的多传感器动态系统的数据融合估计.     

基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计

本文将强跟踪滤波理论与多传感器数据融合技术相结合,提出基于强跟踪滤波器的多传感器状态与参数联合估计新算法;对拥有相同采样率的分布式多传感器单模型非线性动态系统,应用强跟踪滤波器,得到目标状态基于全局信息融合估计结果,并利用计算机仿真结果对算法的有效性进行了验证;这些工作初步解决了Kalman滤波中由于模型的不确定性而造成估计误差值偏大情况下的状态融合估计问题,从而丰富和发展了多源信息融合理论.     

一类多速率传感器动态系统的建模与估计

研究了一类时变线性动态系统的多速率多传感器数据融合状态估计问题.首先,在不同传感器以不同采样率对同一目标进行观测的情况下,提出了一种多速率建模方法,该方法可将多采样率的融合估计问题转化为同采样率的状态估计问题.随后,利用Kalman滤波对目标状态进行了在线估计,并利用有反馈分布式融合结构对上述估计进行了有机融合,从而获得了目标状态的最优融合估计值.该方法不需要对状态或观测进行扩维,计算量适当,保证了算法的实时性.以景象匹配辅助GPS/INS组合导航为例,在两种采样关系下,分别做了仿真,仿真结果验证了算法的有效性.     

一种基于异步数据融合的传感器管理方法

针对多传感器系统存在观测数据过多,数据融合中心处理负担过重的问题,在引入一种基于传感器观测数据序贯处理的最优异步融合算法的基础上,提出一种基于目标协方差控制的传感器选择算法对异步观测数据进行优化组合,从而实现以最小的计算量达到所要求的目标状态估计精度。最后的仿真结果证明了该算法的有效性和可行性。     

基于异步多传感器采样量测的最优状态融合估计

针对一类异步多传感器采样系统,在对量测方程形式统一描述的基础上,给出以采样量测顺序为基准的序贯滤波算法的基本思想;为了避免现有右同步提升技术所可能引发的系统非因果这一重要问题,选择将融合周期内所有异步采样量测进行左同步提升,并发展了一种集中式最优状态融合估计算法,同时从理论上严格证明了基于左、右同步提升技术的融合估计算法在精度上的等价性;将三种融合估计算法对两个多速率采样系统进行仿真比较,在验证三种算法估计精度相同的同时,还对三种算法计算代价进行了分析和比较.相关结论对异步多传感器各最优状态估计融合算法的实际应用具有一定指导意义.     

基于BLUE的雷达/红外异步融合算法研究

多传感器融合是提高态势感知能力的重要手段。为提高探测能力,将雷达和红外传感器组网,各传感器独立工作,在统一调度下,完成探测、跟踪、识别任务。研究该系统的雷达/红外数据融合算法,针对传感器异步探测特点,采用观测驱动的融合跟踪方法：雷达探测到目标时,采用基于状态预测的改进BLUE（Best Linear Unbiased Estimation）滤波,通过方位预测的辅助,减小测角误差非线性效应,提高跟踪性能;红外探测到目标时,基于方位预测和斜距观测,构造新的转换量测模型,实现基于不完备观测的修正BLUE滤波器。理论分析和仿真证明：所述雷达/红外数据融合方法,在不同传感器布局下都具备更优的综合性能,其设计思想可解决其他类似的多传感器融合问题,有较好的应用推广潜力。     

一类多速率多传感器系统的状态融合估计算法

基于不同传感器以不同采样率对同一目标状态进行观测的多传感器单模型动态系统,该文提出了一种状态融合估计算法。不同传感器之间采样率之比可以是正有理数。该算法不仅具有好的实时性,而且在线性最小方差意义下是最优的。进一步可以证明:融合多个传感器获得的最高采样率下状态的估计值优于单传感器的估计结果,而减少任何一个传感器的信息所获得的估计值的误差协方差都将增大。仿真结果验证了算法的可行性与有效性。     

Optimal signal reconstruction in noisy filter bank systems:multirate Kalman synthesis filtering approach

A multirate Kalman synthesis filter is proposed in this paper to replace the conventional synthesis filters in a noisy filter bank system to achieve optimal reconstruction of the input signal. Based on an equivalent block representation of subband signals, a state-space model is introduced for an M-band filter bank system with subband noises. The composite effect of the input signal, analysis filter bank, decimators, and interpolators is represented by a multirate state-space model. The input signal is embedded in the state vector, and the corrupting noises in subband paths are generally considered as additive noises. Hence, the signal reconstruction problem in the M-band filter bank systems with subband noises becomes a state estimation procedure in the resultant multirate state-space model. The multirate Kalman filtering algorithm is then derived according to the multirate state-space model to achieve optimal signal reconstruction in noisy filter bank systems. Based on the optimal state estimation theory, the proposed multirate Kalman synthesis filter provides the minimum-variance reconstruction of the input signal. Two numerical examples are also included. The simulation results indicate that the performance improvement of signal reconstruction in noisy filter bank systems is remarkable     

ORCA-MRT: an optimization-based approach for fair scheduling in multirate TDMA wireless networks

This paper presents an optimization-based approach to solve the wireless fair scheduling problem under a multirate time division multiple access (TDMA)-based medium access control (MAC) framework. By formulating the fair scheduling problem as an assignment problem, the authors propose the optimal radio channel allocation for multirate transmission (ORCA-MRT) algorithm for fair bandwidth allocation in wireless data networks that support MRT at the radio link level. The key feature of ORCA-MRT is that while allocating transmission rate to each flow fairly, it keeps the interaccess delay bounded under a certain limit. The authors investigate the performance of the proposed ORCA-MRT scheduler in comparison to another recently proposed multirate fair scheduling algorithm. They also propose two channel prediction models and perform extensive simulations to investigate the performance of ORCA-MRT for different system parameters such as channel state correlation, number of flows, etc.     

基于自适应Kalman滤波的二维有噪子带信号恢复

基于子带信号的多通道表示（multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组（Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面（object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。     

Implementation of a Multirate Speech Digitizer

In this paper, implementation of a compact and efficient multirate speech digitizer with variable transmission rates of 2.4, 4.8, 9.6, and 14.96 kbits/s is presented. The multirate algorithm has been made based on the residual-excited linear prediction (RELP) vocoder with a transmission rate of 9.6 kbits/s. The residual encoder employed in the RELP vocoder uses hybrid companding delta modulation (HCDM). This HCDM is also used as a 14.96 kbit/s coder. If the residual in the RELP system is down-sampled before encoding, a 4.8 kbit/s coder can be realized. If the residual encoder is not used, a 2.4 kbit/s linear predictive coder (LPC) can be realized by incorporating a pitch extractor. In the 4.8 and 9.6 kbit/s coders the pitch-implanted residual excitation method has been used to generate the excitation signal to the synthesis filter. The multirate speech digitizer algorithm has been implemented using 2900 series bit-slice microprocessors. The external memory is composed of 2K RAM's and 2K ROM's. The system design is a two-bus structure with a 204 ns cycle time. With efficient hardware and software design, the multirate speech digitizer requires almost the same hardware complexity as compared with the conventional 2.4 kblt/s LPC vocoder.     

Variable-length code construction for incoherent optical CDMA systems

The purpose of this study is to investigate the multirate transmission in fiber-optic code-division multiple-access (CDMA) networks. In this article, we propose a variable-length code construction for any existing optical orthogonal code to implement a multirate optical CDMA system (called as the multirate code system). For comparison, a multirate system where the lower-rate user sends each symbol twice is implemented and is called as the repeat code system. The repetition as an error-detection code in an ARQ scheme in the repeat code system is also investigated. Moreover, a parallel approach for the optical CDMA systems, which is proposed by Marić et al., is also compared with other systems proposed in this study. Theoretical analysis shows that the bit error probability of the proposed multirate code system is smaller than other systems, especially when the number of lower-rate users is large. Moreover, if there is at least one lower-rate user in the system, the multirate code system accommodates more users than other systems when the error probability of system is set below 10⁻⁹.     

Optimal filtering for multirate systems

For a multirate system where the output sampling is slower than the input updating, this brief aims at designing filters for fast state estimation in the H/sub 2/ and H/sub /spl infin// settings. Because of the multirate nature, linear matrix inequality solutions to the design problems involve a nonconvex constraint, which is numerically tackled by the product reduction algorithm. Finally, a design example is given and the effectiveness of the approach is illustrated.     

Alias-Component Matrices of Multirate Systems

We consider a multirate system, which is a generalization of linear time-invariant systems. Such a system is invariant to a certain shift in the input sequence. In particular, assume that p and q are coprime. A multirate system with the property that a delay of mq samples in its input sequence results in a delay of mp samples in its output sequence is called an (mp, mq)-periodic system. This multirate system can be obtained by cascading an upsampler, followed by a linear periodically time-varying (LPTV) kernel system, then followed by a downsampler. Here, we study the alias-component matrices of multirate systems. We show that they can be obtained from the alias-component matrices of their LPTV kernels by some row and column additions. An example shows the use of the method to design rate changers for a specified frequency band swap.     

Q-learning-based multirate transmission control scheme for RRM in multimedia WCDMA systems

In this paper, a Q-learning-based multirate transmission control (Q-MRTC) scheme for radio resource management in multimedia wide-band code-division multiple access (WCDMA) communication systems is proposed. The multirate transmission control problem is modeled as a Markov decision process where the transmission cost is defined in terms of the quality-of-service (QoS) parameters for enhancing spectrum utilization subject to QoS constraint. We adopt a real-time reinforcement learning algorithm, called Q-learning, to accurately estimate the transmission cost for the MRTC. In the meantime, we successfully employ the feature extraction method and radial basis function network (RBFN) for the Q-function that maps the original state space into a feature vector that represents the resultant interference profile. The state space and memory-storage requirement are then reduced and the convergence property of the Q-learning algorithm is improved. Simulation results show that the Q-MRTC for a multimedia WCDMA system can achieve higher system throughput by an amount of 80% and better users' satisfaction than the interference-based MRTC scheme, while the QoS requirements are guaranteed. Also, compared to the table-lookup method, the storage requirement is reduced by 41%.     

Modified Unscented Kalman Filter for a Multirate INS/GPS Integrated Navigation System

Instead of the extended Kalman filter, the unscented Kalman filter (UKF) has been used in nonlinear systems without initial accurate state estimates over the last decade because the UKF is robust against large initial estimation errors. However, in a multirate integrated system, such as an inertial navigation system (INS)/Global Positioning System (GPS) integrated navigation system, it is difficult to implement a UKF‐based navigation algorithm in a low‐grade or mid‐grade microcontroller, owing to a large computational burden. To overcome this problem, this letter proposes a modified UKF that has a reduced computational burden based on the basic idea that the change of probability distribution for the state variables between measurement updates is small in a multirate INS/GPS integrated navigation filter. The performance of the modified UKF is verified through numerical simulations.     

Optimal reconstruction of ARMA signals with decimated samples under corrupting noise by use of multirate Kalman filter

A multirate modeling theory of the ARMA stochastic signals is derived from a state-space viewpoint in this work. Its application to the signal reconstruction problem for the recovery of the complete ARMA signal from its noise-corrupted, missing-sample sequence is then developed in detail. The proposed estimation-interpolation problem can be resolved by using the multirate optimal state estimation scheme of this work. Theoretically, the multirate Kalman reconstruction filters derived in this paper produce the minimum variance estimation and interpolation of the original complete, clean ARMA signal. Practically, the numerical examples show that the multirate Kalman reconstruction filters illustrate good estimation/interpolation performances, not only for synthetic ARMA sequences but also for human speech signals.