Systolic square root covariance Kalman filtering

The square root covariance Kalman filter (SRCF) and the square root covariance Kalman predictor (SRCP) are derived from a least squares viewpoint. A new systolic array architecture is presented which is suitable for implementing both forms of the filter. The systolic SRCF is found to be comparable with other architectures in the literature in terms of size, speed and processor utilization. The SRCP is faster than any comparable architecture withO(2n) timesteps between measurements.     

Systolic array for all-nearest-neighbours problem

The all-nearest-neighbours (ANN) problem is a fundamental problem in computational geometry. In the letter a new two-dimensional triangular systolic array with mesh-connected cells is proposed for the ANN problem. This array can process a queue of ANN problems at a throughput of one ANN problem per time period, with an efficiency of 100%.     

Systolic arrays for recursive digital filtering

Two classes of systolic array for the implementation of recursive digital filters are presented, which overcome some of the limitations of earlier designs. The trade-off resulting from varying the degree of pipelining is discussed.     

Systolic convolution array

A systolic convolution array is presented, which has the minimum possible number of register stages, fixed fan-out, fan-in and response time, and which accepts a single input stream at the clock rate.     

Systolic array for recursive least-squares minimisation

A systolic array for performing recursive least-squares minimisation is described. It performs an orthogonal triangularisation of the data matrix using a sequence of Givens rotations, and generates the required residual without having to solve the associated triangular linear system by back-substitution. Since the back-substitution process may be ill conditioned and numerically unstable, the reliability and robustness of the method is greatly improved as a result, whilst the amount of circuitry and computation is reduced.     

Modified Kalman filtering

A modified Kalman filtering algorithm is described. The key point of the new algorithm is a model mismatch function, which accounts for deviation of the model from the ideal condition of orthogonality between the innovations process and past observations     

Approximate Kalman filtering for multiply perturbed systems

An approximate Kalman filtering algorithm for multiply perturbed systems is proposed based on an approximate solution to the Riccati equation. This new algorithm not only greatly reduces the computational complexity, but also lends itself to solving a parameter optimization problem.     

Systolic array for VLSI implementation of realization techniques

A technique for implementing the realization algorithm from input-output data of the system directly in hardware is proposed. The proposed technique results in low cost, special purpose devices which can quickly solve realization problems. The systolic arrays for VLSI implementation of the different steps of the realization algorithm are developed.     

Kalman filtering for power estimation in mobile communications

In wireless cellular communications, accurate local mean (shadow) power estimation performed at a mobile station is important for use in power control, handoff, and adaptive transmission. Window-based weighted sample average shadow power estimators are commonly used due to their simplicity. In practice, the performance of these estimators degrades severely when the window size deviates beyond a certain range. The optimal window size for window-based estimators is hard to determine and track in practice due to the continuously changing fading environment. Based on a first-order autoregressive model of the shadow process, we propose a scalar Kalman-filter-based approach for improved local mean power estimation, with only slightly increased computational complexity. Our analysis and experiments show promising results.     

Motion estimation for video compression using Kalman filtering

Motion estimation plays an important role for the compression of video signals. This paper presents a new block-based motion estimation method using Kalman filtering. The new method utilizes the predicted motion and measured motion to obtain an optimal estimate of motion vector. The autoregressive models are employed to fit the motion correlation between neighboring blocks and then achieve predicted motion information. The measured motion information is obtained by the conventional block-based fast search schemes. Several algorithms based on either one- or two dimensional models using either nonadaptive or adaptive Kalman filters are developed. The analysis of computational complexity and the simulation results indicate that the proposed method achieves significant savings on computation along with smoother motion vector fields and similar picture quality, when compared to the conventional full search algorithm     

A Kalman filtering technique for certain Markov chains

In this paper an efficient decoupling Kalman filtering technique is applied to certain Markov chains with finite-dimensional stationary state-transition matrices. For optimal estimates of a Markov chain with ann-dimensional stationary statetransition matrix, the resultant computational algorithm consists ofn-1 simple one-dimensional recursive formulas.This research was supported by the ONR Contract N00014-88-K-0127 and the State of Texas grant TATP 2892.     

Space-time coding and Kalman filtering for time-selective fadingchannels

This paper proposes a novel decoding scheme for Alamouti's (see IEEE J. Select. Areas Commun., vol.16, p.1451-1458, 1998) space-time (ST) coded transmissions over time-selective fading channels that arise due to Doppler shifts and carrier frequency offsets. Modeling the time-selective channels as random processes, we employ Kalman filtering for channel tracking in order to enable ST decoding with diversity gains. Computer simulations confirm that the proposed scheme exhibits robustness to time-selectivity with a few training symbols     

Harmonic analysis via Kalman filtering technique

A recursive computer method for harmonic analysis of a noisy periodic signal is presented. It is shown that with a suitable model for harmonic coefficients, a simple application of Kalman filtering technique can yield minimum variance estimates of the coefficients. The proposed method is illustrated with the help of simulation results.     

Adaptive Kalman filtering using stochastic approximation

A Kalman filter requires an exact knowledge of the noise covariance matrices to determine the optimal gain Kop for the filtering equations. In the absence of such prior information, an adaptive technique must be used. An approach based on stochastic approximation is presented. The steady-state gain is obtained by using a recursive algorithm that satisfies the innovations theorem.     

Kalman filtering of traffic fluctuations for real-time network management

Discrete- time stochastic models of sequential traffic records in telephone networks are considered. They appear to be well suited for a use of the Kaiman filter. A version of the filtering algorithm is adopted to deal with traffic fluctuations. The overall approach is tested against the real data gathered by the supervisor of the French toll network. The adaptive traffic estimator is inserted into a simulator of a testbed network operated in the urban zone of Paris. The traffic estimations provided by the filter are used to decide an overflow hierarchy.     

Estimation and identification for 2-D block Kalman filtering

The development of a recursive identification and estimation procedure for two-dimensional block Kalman filtering is discussed. The recursive identification scheme can be used online to update the image model parameters at each iteration based on the local statistics within a block of the observed noisy image. The covariance matrix of the driving noise can also be estimated at each iteration of this algorithm. A recursive procedure for computing the parameters of the higher order models is given. Simulation results are also provided     

卡尔曼滤波在目标跟踪中的研究与应用

卡尔曼滤波算法是现阶段雷达信号处理中最常用的跟踪算法,结合雷达跟踪的空中目标的实际情况,针对目标运动模型中的线性运动和非线性运动模型,分别设计了两种模型,并利用马尔可夫状态转移矩阵实现交互多模算法。最后对交互多模型卡尔曼滤波算法进行了Matlab仿真及结果分析。     

Kalman filtering for enhanced landmine detection using quadrupole resonance

Quadrupole resonance (QR) is a novel technology recently applied to landmine detection. The detection process is specific to the chemistry of the explosive, and therefore is less susceptible to the types of false alarms experienced by metal detectors and ground-penetrating radars. Although QR is vulnerable to radio-frequency interference (RFI) when the sensor is deployed in the field, adaptive RFI mitigation can remove most of the RFI. In this paper, advanced signal processing algorithms applied to the postmitigation signal are studied to enhance explosive detection. A new Kalman filtering strategy is proposed to estimate and detect the QR signal in the postmitigation signal. The results using both simulated data and experimental data show that the proposed algorithm can provide robust landmine detection performance.