Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

Optimal filtering for networked systems with Markovian communication delays

Abstract: This paper is concerned with the optimal filter problems for networked systems with random transmission delays, while the delay process is modeled as a multi-state Markov chain which incorporates the data losses naturally. By defining a delay-free observation sequence, the optimal filter problems are transformed into the ones of the standard Markov jumping parameter measurement system. We first present an optimal Kalman filter, which is with time-varying, path-dependent filter gains, and the number of the paths grows exponentially in time delay. Thus an alternative optimal Markov jump linear filter is presented, in which the filter gains just depend on the present value of the Markov chain, and as a result, the obtained filter is again a Markov jump linear system. It can be shown that the proposed Markov jump linear filter converges to the constant-gain filter under appropriate assumptions.

Error surfaces of recursive adaptive filters

Abstract: For an adaptive filter with N adjustable coefficients or weights, the "error surface" is a plot, in N+ I dimensions, of the mean-squared error versus the N coefficient values. If the adaptive filter is nonrecursive, the error surface is a quadratic function of the coefficients. With recursive adaptive filters, the error surface is not quadratic and may even have local minima. In this correspondence we discuss the nature of the recursive error surface and give examples of conditions under which local minima may exist. We conclude with a discussion of the effects of the nonquadratic error surface on gradient-search algorithms for recursive adaptive filters.

Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search

Abstract: The aim of this paper is to propose an evolutionary particle filter based upon improved cuckoo search algorithm which will overcome the sample impoverishment problem of generic particle filter. In our proposed method, improved cuckoo search (ICS) algorithm is embedded into particle filter (PF) framework. Improved cuckoo search algorithm uses levy flight for generating new particles in the solution and introduced randomness in samples by abandoning a fraction of these particles. The second important contribution in this article is introduction of new way for tackling scaling and rotational error in object tracking. Performance of proposed improved cuckoo particle filter is investigated and evaluated on synthetic and standard video sequences and compared with the generic particle filter and particle swarm optimization based particle filter. We show that object tracking using improved cuckoo particle filter provides more reliable and efficient tracking results than generic particle filter and PSO-particle filter. The proposed technique works for real time video objects tracking.