A passive wireless tracking system for indoor assistive monitoring

This paper presents a design concept and implementation of an indoor passive tracking system that utilises an array of Wi-Fi transceivers, and without any electronic device or tag attached to the object being tracked. Such tracking is made possible by exploiting the fundamental characteristic of signal attenuation due to object blocking, i.e. shadowing, that is prevalent in a typical wireless communication system

MIMO Receiver Structures with Integrated Channel Estimation and Tracking

This thesis looks at the problem of channel estimation and equalization in a multiple-input multiple-output (MIMO) dispersive fading environments. Two classes of MIMO receiver structure are proposed with integrated channel estimation and tracking. One is a symbol-by-symbol based receiver using a MIMO minimum mean square error (MMSE) decision feedback equalizer (DFE), and the other is a sequence-based receiver using a partitioned Viterbi algorithm (PVA) which approaches the performance of maximum likelihood sequence estimation (MLSE). A MIMO channel estimator capable of tracking the time and frequency selective channel impulse responses, known as the vector generalized recursive least squares (VGRLS) algorithm, is developed. It has comparable performance and a similar level of complexity as the optimum Kalman filter. However, it does not require any knowledge of the channel statistics to operate and as such it can be employed in a Rician fading channel readily. A reduced complexity form of the estimator, known as the vector generalized least mean squares (VGLMS) algorithm, is also developed. This is achieved by replacing the online recursive computation of the VGRLS algorithm's 'intermediate' Riccatti matrix with an offline pre-computed matrix. This reduces the complexity of the algorithm by an order of a magnitude, but at the expense of degraded performance. The estimators are integrated with the above-mentioned equalizers in a decision directed mode to form a receiver structure that can operate in continuously time-varying fading channels. Due to decision delays, the outputs from the equalizer are delayed and this then produces 'delayed' channel estimates. A simple polynomial-based channel prediction module is employed to provide up-to-date channel estimates required by the equalizers. However, simulation results show that the channel prediction module may be omitted for a very slowly fading channel where the channel responses do not vary much. In the case of the PVA- receiver, the zero-delay tentative decisions are used as feedback to the channel estimators with negligible loss

A reduced complexity channel estimation algorithm with equalization

The error rate performance of a previously developed reduced complexity channel estimator, known as the generalized least mean squares (GLMS) algorithm, is investigated in conjunction with a minimum-mean-square-error (MMSE) decision feedback equalizer (DFE). The channel estimator is based on the theory of polynomial prediction and Taylor series expansion of the underlying channel model in time domain. It is a simplification of a previously developed generalized recursive least squares (GRLS) estimator, achieved by replacing the online recursive computation of the 'intermediate’ matrix by an offline pre-computed matrix. Similar to the GRLS estimator, it is able to operate in Rayleigh or Rician fading environment without reconfiguration of the state transition matrix to accommodate the non-random mean components. Simulation results show that it is able to offer a trade-off between reduced complexity channel estimation and good system performance

Teaching information theory using a wireless optical transceiver platform

Information theory is important in many applications but many students find its ideas highly abstract and theoretical, making it a difficult and boring subject. Practical hands-on demonstrations can be used to complement computer simulation and modeling to enhance students' understanding of the underlying concepts. This paper presents a prototype platform for teaching information theory using a wireless optical transceiver system and MATLAB graphical user interface. Source coding and channel error-correction coding, in particular Huffman and Hamming codes, were studied

Vertical line-based descriptor for omnidirectional view image

Catadioptric omnidirectional view sensor has a convenient 360 degree field of view that favours various robotic applications. The distortion nature in omnidirectional view images allows the discovery of a new robust image feature in the form of vertical/central propagating lines. In this paper, we proposed an improvement to the existing vertical line detection algorithm using Haar wavelet transform under integral image environment. Subsequently, a new lightweight descriptor scheme is developed using the same Haar wavelet response that complies with the nature of line features

A parallel root-finding method for omnidirectional image unwrapping

The panoramic unwrapping of catadioptric omnidirectional view (COV) sensors have mostly relied on a precomputed mapping look-up table due to an expensive computational load that generally has its bottleneck occur at solving a sextic polynomial. However, this approach causes a limitation to the viewpoint dynamics as runtime modifications to the mapping values are not allowed in the implementation. In this paper, a parallel root-finding technique using Compute Unified Device Architecture (CUDA) platform is proposed. The proposed method enables on-the-fly computation of the mapping look-up table thus facilitate in a real-time viewpoint adjustable panoramic unwrapping. Experimental results showed that the proposed implementation incurred minimum computational load, and performed at 10.3 times and 2.3 times the speed of a current generation central processing unit (CPU) respectively on a single-core and multi-core environment