This work studies the problem of tracking signal-emitting mobile targets using navigated mobile sensors based on signal reception. Since the mobile target's maneuver is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal's time of arrival (TOA). The mobile sensor controller acquires the TOA measurement information from both the mobile target and the mobile sensor for estimating their locations before directing the mobile sensor's movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming (SDP) relaxation, and apply a cubic function for mobile sensor navigation. We estimate the location of the mobile sensor and target jointly to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target.

In this work, we study the localization of mobile signal emitters using time of arrival (TOA) measurement and additional urban street information. Two algorithms are proposed to improve the localization performance by integrating street information with the TOA measurement. The first algorithm exhaustively searches of all possible road paths. For each possible path, the source location is estimated based a semidefinite programming (SDP) algorithm by minimizing the maximum error measurement between the observed propagation time and the modeled propagation time. Only location on a street that satisfies the minimum mean square error yields estimation output. To reduce complexity, our second joint optimization algorithm combines the two steps together and jointly optimizes the path selection and source location. Numerical results show that both algorithms can improve the localization performance. Our proposed joint optimization approach is more suitable for practical use because of lower complexity and good performance.

We investigate the problem of source localization based on measuring time difference of signal arrivals (TDOA) from the source emitter. Taking into account the colored measurement noise, we adopt a min-max principle to develop two lower complexity semidefinite relaxation algorithms that can be reliably solved using semidefinite programming. The reduction of algorithm complexity is achieved through a simple, but effective method to select a reference node among participating measurement nodes such that only selective time differences of signal arrival are exploited. Our estimation methods are insensitive to the source locations and can be used either as the final location estimate or as the initial point for more traditional search algorithms.

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate source localization based on the practical time of arrival (TOA) measurement model. Unlike most existing works that transform TOA measurement into time differences before processing, we consider the original measurement model and investigate three methods for direct source localization. We also derive the Cramér-Rao lower bound (CRLB) under the TOA model and establish its connection with the CRLB under the more commonly used time-difference of arrival (TDOA) signal model. We present results that illustrate the performance advantage of source localization based on the original TOA model over the commonly used TDOA pre-processing.

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the time-difference of arrivals (TDOA) measurement model. By adopting a min-max approximation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can be reliably and rapidly solved through semi-definite relaxation. Our approach hinges on the use of a reference sensor node which can be optimized according to the Cramer-Rao lower bound or selected heuristically. Our low complexity estimate can be used either as the final location estimation output or as the initial point for other traditional search algorithms.

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.

The queued-code based on low-density parity-check (LDPC) codes, which exploit both the near Shannon-limit performance LDPC codes and the instantaneous channel state information (CSI), can provide excellent performance in the block fading channels. The existing work utilized the conventional random puncturing and repetition operation, which may lead to considerable performance loss due to the changed intrinsic connections of parity-check matrix (PCM) for the receiver. In this paper, we present a reversed sequential puncturing (RSP) scheme and a construction method with modified dual-diagonal structure for the LDPC coded queued-code. The consecutive puncturing pattern in reversed direction can ensure the uniform row-weight distribution of the punctured PCM. The modified dual-diagonal form and the RSP scheme can ensure no more than one punctured symbol involved in each check-sum equation. The simulation results demonstrate that our proposed scheme can achieve noticeable performance gains.

A natural relationship between weighted bit-flipping (WBF) decoding and message-passing decoding is explored. This understanding can help us develop a dual WBF decoding algorithm from one type of message-passing decoding algorithm and vice versa. For min-sum decoding, one can find that its dual WBF algorithm is the algorithm proposed by Jiang et al. For belief-propagation (BP) decoding, we propose a new WBF algorithm and show its performance advantage. For some high-rate low-density parity-check (LDPC) codes of large row weight, it is shown that the WBF algorithm proposed by Liu and Pados performs extraordinarily well. However, its dual message- passing decoding does not work well. Furthermore, we propose a parallel implementation framework for various WBF algorithms. Compared to serial implementations, various WBF algorithms in their parallel form converge significantly faster and often perform better.