Zaib, Alam; Al-Naffouri, Tareq Y.(EURASIP Journal on Advances in Signal Processing, Springer Nature, 2014)[Article]
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
Omer, Muhammad; Quadeer, Ahmed A; Sharawi, Mohammad S; Al-Naffouri, Tareq Y.(EURASIP Journal on Advances in Signal Processing, Springer Nature, 2014-07)[Article]
This paper presents a robust method for two-dimensional (2D) impulsive acoustic source localization in a room environment using low sampling rates. The proposed method finds the time delay from the room impulse response (RIR) which makes it robust against room reverberations. We consider the RIR as a sparse phenomenon and apply a recently proposed sparse signal reconstruction technique called orthogonal clustering (OC) for its estimation from the sub-sampled received signal. The arrival time of the direct path signal at a pair of microphones is identified from the estimated RIR, and their difference yields the desired time delay estimate (TDE). Low sampling rates reduces the hardware and computational complexity and decreases the communication between the microphones and the centralized location. Simulation and experimental results of an actual hardware setup are presented to demonstrate the performance of the proposed technique.
Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.
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