Kalman Channel Research Articles

Block-Based Kalman Channel Tracking for LEO Satellite Communication With Massive MIMO

Low Earth orbit (LEO) satellite communication (SATCOM) is one of the most promising systems to be incorporated in the future 5G and beyond wireless networks. The combination of massive multi-input multi-output (mMIMO) with LEO SATCOM can effectively enhance the communication capacity and improve the system throughput. However, the channel acquisition and tracking are challenging due to the severe Doppler shift and long propagation delay under the LEO SATCOM channel’s time-varying propagation environment. In this work, we propose a block-based (BB) Kalman channel tracking scheme for LEO SATCOM with mMIMO. Firstly the mMIMO LEO SATCOM channel model with Doppler shift and propagation delay is proposed. To reduce the tracking times in one data frame, the BB Kalman channel tracking scheme is proposed under LEO SATCOM environment. The optimal block length problem is also studied. Simulation results prove the feasibility of the proposed scheme and the block length selection criteria.

NEW TECHNOLOGIES AND APPLICATIONS OF MOTION TRACKING IN VIRTUAL REALITY SYSTEMS

This article is centered around the sort of virtual reality (VR) – MoCap – motion capture. VR is utilized as a part of the planning and design procedure of the working environment. Firstly, the work environment is designed and before actual building the various conditions are simulated. Product of this simulation by VR is the working environment/ building. VR is utilized too in the medical treatments. This paper investigates a way to deal with physical rehabilitation using best in class advances in virtual reality and motion tracking. As of present, there has been a boom in the utilization of the Kalman channel in Virtual/ Augmented Reality. This paper presents a brief introduction to the Kalman filter and the development of the utilization of the filter in VR motion tracking.

Vehicle Surveillance and Tracking using Background Segmentation

A significant initial step for video investigation is Background Subtraction and it is utilized to find the objects of enthusiasm for additional prerequisites. Foundation deduction approach is a general technique for movement recognition strategy, which proficiently utilizes the distinction of the current picture and the foundation picture to recognize moving articles. Here the proposed calculation is known as Mixture of Gaussian (MOG) process. This goes under a quality investigation calculation for pictures, which could be handled in the recordings and casings. A methodology is utilized alongside the Kalman channel for outline by outline identification. At that point the MOG is utilized naturally to gauges the quantity of blend parts required to display the pixels foundation shading dissemination. Here executes the foundation concealment for static and dynamic foundation pictures without utilizing any reference foundation pictures, and furthermore smother the clamor out of sight picture's shadows. Kalman channel is a channel that contains strategies portrayed by inferior computational expense and depends on a strong factual model, on a heartiness level. At long last, the fragmented foundation picture is acquired with acceptable execution. At that point the key of this technique is the instatement and update of foundation picture and recognition of moving article, which is likewise exact.

Multiuser Detection and Channel Estimation for CDMA System under Flat Fading Channels

For a level blurring code-division multiple access (CDMA), the numerous entrance framework is done by speech signal, and it shows the real framework by different conditions like comprising of state condition, multiplicative commotion condition , the nonlinear estimation condition, and an ideal separating calculation and these are used for joint recursive channel estimation and multi users separation. It ought to be focused on that the new calculation is pertinent to issues with a similar sort of nonlinear framework which shows that related commotions. Recreations process demonstrates that the new calculation performs are superior to the expanded Kalman channel estimation in faster merging rate and lower in bit mistake rate.

Joint Kalman Channel Estimation and Turbo Equalization for MIMO OFDM Systems over Fast Fading Channels

Joint Kalman Channel Estimation and Turbo Equalization for MIMO OFDM Systems over Fast Fading Channels

GUI Based Execution of Discourse Upgrade System

Discourse, being a key method for correspondence, has been inserted in different applications. In numerous unavoidable circumstances, we are rendered vulnerable attempting to conclude the understandability of the discourse and this is the place Speech improving strategy i.e. evacuation of undesirable foundation commotion, comes into picture. In this paper, an endeavor has been made towards contemplating Speech Enhancement methods, for example, Spectral Subtraction, Minimum Mean Square Error (MMSE), Kalman and Wiener channel. In view of our perceptions and investigation of different execution parameters, we finish up which of the strategies is most reasonable for discourse improvement. The usage of the code for different channels is finished utilizing.

New OSTBC for Blind Channel Estimation and Tracking in MIMO-OFDM Systems

Applying orthogonal space time block coding (OSTBC) to multiple-input multiple-output (MIMO) systems helps reduce receiver complexity. However, this approach has been applied only to flat fading channels, as when the channel is a frequency selective fading MIMO channel, OSTBC cannot be used directly since its orthogonal propriety may be lost. Furthermore, the MIMO channel is not always known. To deal with this problem, many techniques were proposed to estimate the channel using a training sequence. Unfortunately, these techniques reduce the useful spectral bandwidth. This paper proposes OSTBC for blind channel estimation and data detection in the case of a MIMO frequency selective channel. The aim of this new OSTBC is twofold: to solve the ambiguity of channel estimation and to reduce the complexity of the detector. By exploiting the well-known technique of orthogonal frequency division multiplexing (OFDM), the frequency selective fading MIMO channel is split into a set of flat fading subchannels. Moreover, to accommodate the fact that a MIMO channel can be time varying, the steady state Kalman channel estimator (SS-KCE) is extended to track the channel’s fast variations. The performance of the proposed blind algorithm is related by the adequate choice of the number of subcarriers and it is compared with other existing approaches by means of Monte Carlo simulations.

Novel Detectors for Massive MU-MIMO Communications

The massive multi-user multiple-input multiple-output (MU-MIMO) can be adapted to meet the increasing requirement of data throughput services in wireless transmission systems. The combination of the Single Carrier Frequency Division Multiple Access (SC-FDMA) with the massive MU-MIMO technology can be employed for the uplink transmission to meet the requirement of data rates and error performance. In this paper, for practical implementation, we integrate multi-user detection, Kalman channel estimation and turbo equalization and propose turbo detectors to solve the problem of huge computation load of matrix inversion at receiver side. The proposed low-complexity detectors are based on Neumann series expansion, successive over-relaxation (SOR), and symmetric successive over-relaxation (SSOR) principles. Simulation examples in the uplink scenario are given for the proposed low-complexity turbo detectors to demonstrate the comparisons and effectiveness of the proposed schemes.

Speech enhancement using discrete kalmanfilter algorithm

Enhancement of speech deals with the noise reduction which is mostly essential in many applications like teleconferencing sys tem. The nearness of added substance foundation noise degenerate the discourse in correspondence condition. In the event that the commotion is developing slower than discourse which implies noise is more stationary than speech .Estimation of noise ends up noticeably s impler when delays happened in speech. In this work discrete Kalman channel calculation which gauges the inward condition of a direct powe r-ful framework.

Analysing Multi Sensor Fusion with Distinctive Approach

Information companionship is a central issue in multitarget-multisensor following. It involves selecting the most possible cooperation between sensor estimations and target tracks from a huge set of conceivable outcomes. With N sensors and n focuses in the location extent of every sensor, even with immaculate recognition there are (n!) ways which renders infeasible an answer by immediate reckoning even in unobtrusively estimated requisitions. We use apriori calculation to for taking care of the ideal information affiliation issue in a conveyed manner; which are an influential apparatus for encoding the measurable conditions of a set of irregular variables and are generally utilized as a part of numerous requisitions .We then foresee the position and speed of target utilizing the visualized information within placing it into the Kalman channel. Keywords: Association, Fusion, Co-relation Estimation, Combat Management System, Kalman Filter

Pilot Design for Vector State-Scalar Observation Kalman Channel Estimators in Doubly-Selective MIMO-OFDM Systems

Vector state-scalar observation (VSSO) Kalman channel estimators for doubly-selective OFDM systems (DS-OFDM) result in complexity savings of 90% without any loss of performance, as compared to vector state-vector observation (VSVO) Kalman estimators. In this letter, we present the VSSO Kalman channel estimator for doubly-selective multiple-input multiple-output OFDM (DS-MIMO-OFDM) systems. Unlike the VSSO estimator in a DS-OFDM system, where all the pilot symbols had the same value, the pilot symbols need to be designed in a specific way to enable the feasibility of a VSSO estimator for a DS-MIMO-OFDM system. We derive a sufficient condition called as VSSO Kalman-filter condition (VSSO-KFC) that needs to be satisfied by the pilot-pattern design for the feasibility of the VSSO Kalman estimator. We comment, and compare, the proposed pilot pattern with that of Barhumi's and Dai's pilot pattern. A new scheme is introduced, based on the proposed pilot pattern and VSSO Kalman filter, that increases the spectral efficiency compared to a conventional system.

VSSO Kalman-usage of Barhumi's pilot pattern for channel estimation in MIMO-OFDM systems

Vector state-scalar observation (VSSO) and vector state-vector observation (VSVO) are two types of Kalman channel estimators used in orthogonal frequency division multiplexing (OFDM) systems. VSSO Kalman channel estimators result in over 90% complexity savings, compared to VSVO Kalman channel estimators, without any loss of performance. Thus far, for frequency-selective channels, VSSO Kalman channel estimators have been proposed only for OFDM systems and not for MIMO-OFDM systems. VSVO Kalman channel estimators have been proposed for MIMO-OFDM systems over frequency-selective channels. Proposed is a VSSO Kalman channel estimator for MIMO-OFDM systems using Barhumi's pilot pattern. It will be seen that the proposed VSSO Kalman channel estimator has much better performance than the method of Barhumi's least-squares method (literature method) and this is achieved with only a moderate increase (two times) in complexity. Similarly, the proposed VSSO Kalman channel estimator has the same performance as the VSVO Kalman channel estimator method (literature method) and this is achieved at about (1/60)th of the complexity associated with the VSVO method.

A Linear Optimal Transversal Filter for Time-Varying Receiver Channels

Discrete linear optimal and unbiased transversal filters are proposed for onboard receiver channels to estimate continuous/discrete time-varying state-space signal models. The solutions are first found in the batch form. For optimal filtering, the mean square initial state function is determined a posteriori by the discrete algebraic Riccati equation. The unbiased filter is also represented in the iterative Kalman-like form with no requirements for noise and initial state. The filtering error bound is found in the three-sigma sense via the noise power gain (NPG). A fast iterative algorithm for NPG is also supplied. A comparison with the Kalman channel is given employing the harmonic two-state space model.

Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

We derive and analyze two modified Kalman channel estimators (KCE) for time-varying, flat, spatially correlated MIMO channels in systems employing orthogonal space-time block codes: the steady-state KCE, which is less complex than the KCE, and the fading memory KCE, which is more robust to model mismatch.

Equivalence of VSSO and VSVO Kalman Channel Estimators in Doubly-Selective OFDM Systems—A Theoretical Perspective

Vector state-vector observation (VSVO) , and vector state-scalar observation (VSSO) are two types of Kalman estimators that can be used for channel estimation in doubly-selective OFDM systems over wide-sense stationary uncorrelated-scattering (WSSUS) channels. It was shown in that the VSSO Kalman channel estimator resulted in over 90% complexity savings compared to the VSVO Kalman channel estimator and this was achieved without any loss of performance. This inevitably raises the question whether the VSSO and VSVO Kalman channel estimators are computationally equivalent? We attempt to answer this question in this letter and theoretically show the equivalence of the VSSO and VSVO Kalman channel estimator for WSSUS channels.

Antenna Selection and Power Control in MIMO Systems with Continuously Varying Channels

Existing limited feedback methods for Multiple- Input Multiple-Output (MIMO) systems focus largely on block fading conditions whereas, in practice, channels change continuously over time. In this paper, we investigate adaptive transmission over a continuously varying channel (CVC). A combination of Kalman channel tracking, truncated channel inversion (TCI) antenna selection, and power control, is used to achieve bit error rates that are lower than those obtained with existing schemes. Using Kalman filtering for pilot-based channel tracking as well as decision-directed channel tracking, our scheme achieves about 3.6 dB gain over the eigen-mode antenna selection scheme and 1.5 dB gain over the unitary precoding scheme, when operating at a BER of 10-5.

Parallel Kalman filtering for optimal symbol-by-symbol estimation in an equalization context

In this paper, the equalization is placed in an estimation framework where the unknown state to be estimated is a finite sequence of transmitted symbols. A Network of Kalman Filters (NKF) has been suggested for this purpose which is based on modeling the a posteriori symbol state probability density function (pdf) by a Weighted Gaussian Sum (WGS). As the theoretical number of Gaussian terms is increasing dramatically through iterations, several variations on the NKF are presented here while seeking a compromise between complexity and optimal equalization in terms of bit error rate (BER) performance. The suggested trade-off solution consists in merging and pruning the NKF at the beginning of each symbol sequence prediction step. To deal with nonstationary channel equalization, blind hybrid channel/symbol estimation algorithms based on a Kalman (or RLS) channel identification are shown to have a better BER performance and a more stable convergence behavior, compared to the Augmented Network of Kalman Filters (ANKF) and to the Blind Bayesian Equalizer (BBE) developed in (IEEE Trans. Commun. 42 (1994) 1019). Finally, the structure of the NKF is shown to be a kind of Recurrent Radial Basis Function Network (RRBFN) of a reduced size and its performance is compared to that of RBF-based equalizers (IEEE Trans. Neural Networks 4 (1993) 570).

Performance of adaptive MC-CDFMA detectors in rapidly fading rayleigh channels

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct sequence spread spectrum. Different approaches have been adopted which do not assume a perfectly known channel. We examine the forward-link performance of decision-directed adaptive detection schemes, with and without explicit channel estimation, for MC-CDMA systems operating in fast fading channels. We analyze theoretically the impact of channel estimation errors by first considering a simpler system employing a threshold orthogonality restoring combining (TORC) detector with a Kalman channel estimator. We show that the performance deteriorates significantly as the channel fading rate increases and that the fading rate affects the selection of system parameters. We examine the performance of more realistic schemes based on the minimum mean square error (MMSE) criterion using least mean square (LMS) and recursive least square (RLS) adaptation. We present a discussion which compares the decision-directed and pilot-aided approaches and explores the tradeoffs between channel estimation overhead and performance. We find that there is a fading rate range where each method provides a good tradeoff between performance and overhead. We conclude that the MMSE per carrier decision-directed detector with RLS estimation combines good performance in low to moderate fading rates, robustness in parameter variations, and relatively low complexity and overhead. For higher fading rates, however, only pilot-symbol-aided detectors are appropriate.

Joint blind and semi-blind detection and channel estimation for space–time trellis coded modulation over fast faded channels

A method for decoding of space–time trellis coded modulation (STTCM) without a need to transmit separate training sequences is developed. The novel technique uses only a single initial estimate to acquire and track multiple-input multiple-output (MIMO) channels while performing STTCM detection. The technique is akin to blind trellis search techniques (per-survival processing, PSP) and adaptive Viterbi. The solution consists of the deployment of a bank of Kalman filters. The bank of Kalman filters is coupled with Viterbi-type decoders, which produce tentative decisions based on Kalman channel predictions. In return the bank of Kalman filters use the tentative decisions to update and track the MIMO channels corresponding to a number of tracked hypotheses. The proposed technique is applicable to space–time single carrier systems in rapidly fading environments, and also to space–frequency coded MIMO-OFDM.

Performance of an array receiver with a Kalman channel predictor for fast Rayleigh flat fading environments

We develop an approach for using an antenna array for tracking fast Rayleigh flat fading channels and suppressing cochannel interference. The fast flat fading process is assumed to be a general autoregressive (AR) process in order to characterize temporal variation of channels and evaluate its effect on the receiver structure and performance. The optimal array receiver structure that minimizes the probability of error for BPSK signals is derived, which includes a Kalman filter to predict the fading channels. A simple integral expression for the probability of error is also derived for the optimal receiver. In particular, we analyze the case with identical shaping filters. An irreducible probability of error is shown to exist due to the prediction error of multiple channels. Another interesting observation from the study is that the diversity gain with m antenna elements in the presence of k interferences is usually greater than (m-k), even in the presence of channel prediction error. Simulations are carried out to verify the theoretical analysis.