Design of sparse FIR filters with low group delay

The aim of the work is to present the method for designing sparse FIR filters with very low group delay and approximately linear-phase in the passband. Significant reduction of the group delay, e.g. several times in relation to the linear phase filter, may cause the occurrence of undesirable overshoot in the magnitude frequency response. The method proposed in this work consists of two stages. In the first stage, FIR filter with low group delay is designed using minimax constrained optimization that provides overshoot elimination. In the second stage, the same process is applied iteratively to reach sparse solution. Design examples demonstrate the effectiveness of the proposed method

P and M class phasor measurement unit algorithms using adaptive cascaded filters

The new standard C37.118.1 lays down strict performance limits for phasor measurement units (PMUs) under steady-state and dynamic conditions. Reference algorithms are also presented for the P (performance) and M (measurement) class PMUs. In this paper, the performance of these algorithms is analysed during some key signal scenarios, particularly those of off-nominal frequency, frequency ramps, and harmonic contamination. While it is found that total vector error (TVE) accuracy is relatively easy to achieve, the reference algorithm is not able to achieve a useful ROCOF (rate of change of frequency) accuracy. Instead, this paper presents alternative algorithms for P and M class PMUs which use adaptive filtering techniques in real time at up to 10 kHz sample rates, allowing consistent accuracy to be maintained across a ±33% frequency range. ROCOF errors can be reduced by factors of >40 for P class and >100 for M class devices

Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms

This paper considers three problems in sparse filter design, the first involving a weighted least-squares constraint on the frequency response, the second a constraint on mean squared error in estimation, and the third a constraint on signal-to-noise ratio in detection. The three problems are unified under a single framework based on sparsity maximization under a quadratic performance constraint. Efficient and exact solutions are developed for specific cases in which the matrix in the quadratic constraint is diagonal, block-diagonal, banded, or has low condition number. For the more difficult general case, a low-complexity algorithm based on backward greedy selection is described with emphasis on its efficient implementation. Examples in wireless channel equalization and minimum-variance distortionless-response beamforming show that the backward selection algorithm yields optimally sparse designs in many instances while also highlighting the benefits of sparse design.Texas Instruments Leadership University Consortium Progra

Design of Sparse Uniform Linear Array Beamformer using Modified FRM Structure for Varied Applications

This paper presents a method to generate antenna patterns for a Uniform Linear Array (ULA) having narrow beamwidth and low sidelobe levels (SLL) using the recently proposed Modified FRM (ModFRM) architecture. This allows it to direct the beams to specific ground cells for communications while mitigating inter-cell interference. The sharpness of the beam pattern defines the spatial discriminating performance of a ULA beamformer, while the SLL dictates the interference and noise suppression capabilities. Typically, a conventional ULA beamforming will demand high computational complexity and a large number of sensors to satisfy these requirements. Hence to reduce the system cost, using the ModFRM technique a sparse array is developed. With this strategy, the total number of sensors is drastically reduced compared to conventional ULA beamformers. The designed beamformers can be used in applications with stringent requirements where cost and size are concerned