Detection and estimation with fixed lag for abruptly changing systems

Journal ArticleThe problem of state estimation and system structure detection for discrete-time stochastic systems with parameters which may switch among a finite set of values is considered. The switchings are modeled by a semi-Markov, or Markov, chain with known transition statistics. A fixed time delay (lag) is allowed in estimation (smoothing) and detection. The optimal solutions require geometrically increasing computations and storage with time. Suboptimal solutions are proposed to alleviate this problem and simulation results are presented to illustrate the effectiveness of the proposed algorithms and the advantages of introducing a delay in processing of the observations

Linear Prediction Error Method For Blind Identification Of Time-Varying Channels: Theoretical Results

Blind channel estimation for SIMO time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability are investigated. Cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. The focus of the paper is on certain theoretical issues

Wireless Communications and Networking Innovative Signal Transmission and Detection Techniques for Next Generation Cellular CDMA Systems EURASIP Journal on Wireless Communications and Networking Innovative Signal Transmission and Detection Techniques for

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