Computationally efficient distributed minimum wilcoxon norm

In the fields related to digital signal processing and communication, as system identification, noise cancellation, channel equalization, and beam forming Adaptive filters play an important role. In practical applications, the computational complexity of an adaptive filter is an important consideration. As it describes system reliability, swiftness to real time environment least mean squares (LMS) algorithm is widely used because of its low computational complexity (O (N)) and simplicity in implementation. The least squares algorithms, having general form as recursive least squares (RLS), conjugate gradient (CG) and Euclidean direction search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, for their high computational complexity (O (N2)) makes them unsuitable for many real-time applications. Therefore controlling of computational complexity is obtained by partial update (PU) method for adaptive filters. A partial update method is implemented to reduce the adaptive algorithm complexity by updating a fraction of the weight vector instead of the entire weight vector. An analysis of different PU adaptive filter algorithms is necessary, sufficient so meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. System is related to the partial update adaptive filter, but has distinct performance. It can be viewed as a PU adaptive filter, in that machine the deficient-length adaptive filter also updates part of the weight vector. However, it updates some part of the weight vector in every iteration. While the partial update adaptive filter updates a different part of the weight vector for each iteration

Improving Detection of Dim Targets: Optimization of a Moment-based Detection Algorithm

Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the ability to detect low signal to noise ratio (SNR) targets without acceptance of a high false alarm rate. TBD methods exploit both the temporal and spatial information simultaneously to make detection of low SNR targets possible, but at the cost of computation time. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallellizable. The MBD algorithm was found to detect targets comparably with leading TBD methods in 1000th the time