Joint Maximum Likelihood Algorithm Research Articles

SiPM joint likelihood reception for mobile LED-optical communication under pointing errors.

Light-emitting diode (LED)-optical communication is a novel spectrum communication with wide field of view (FOV), light weight, and long-distance free-space capabilities. Due to atmospheric turbulence attenuation and pointing errors caused by long-distance communication, this Letter proposes a multi-pixel channel joint maximum likelihood (JML) reception method using a highly sensitive silicon photomultiplier (SiPM). To evaluate the performance of the SiPM under mobile terminal jittering communication, we analyze the effect of optical transmitting power, pointing errors, and signal-to-noise ratio (SNR) gain on optical communication by comparing JML with signal channel using the maximum likelihood (ML) algorithm. Both simulation analysis and experimental results demonstrate that the proposed JML algorithm to process signals received from SiPM multi-pixel channels can effectively mitigate the impact of pointing errors on the bit error rate (BER) of optical communications by two orders of magnitude at large jitter radians and SNR.

RSS-Based Self-Localization Framework for Future Wireless Networks

The localization is an important asset in all existing and emerging wireless networking solutions since it can extend the radio environmental awareness and assist in providing better network operation. The received signal strength (RSS) based non-Bayesian transmitter localization is especially interesting due to the inherent presence of the RSS observations in all commercial radio devices and the instantaneous estimations without the need for extensive training/learning phases. The existing RSS based localization solutions neglect the problems arising from the inherent sensing network topology uncertainty. The sensors, usually assumed to have a-priori known positions, are often a subject of a previous estimation which propagates errors in the transmitter localization procedure, and, hence, results in significant transmitter localization performance degradation. This paper presents a recently developed generic RSS based joint transmitter/sensors localization framework, founded on the assumption of uncertain topology information. The derived joint maximum likelihood (JML) algorithm simultaneously estimates the transmitter and uncertain sensor positions providing twofold gains: improving the transmitter localization and reducing the network topology uncertainty. The paper broadly evaluates the JML algorithm, emphasizing the substantial localization gains originating from the joint transmitter/sensor position estimation. The results prove up to 85 % sensor position uncertainty reduction with the general system model with multiple transmitters locations and multiple previous estimations of the sensors positions. The paper also derives the theoretical lower bounds of the joint estimation framework, and proves the convergence of the JML algorithm. The presented joint estimation framework is applicable to a variety of wireless networking applications. It can provide self-awareness in future wireless networks and cope with the environment and topology dynamism in wireless ad-hoc networks.

A joint maximum likelihood algorithm for simultaneous track correlation and sensorbias estimation

In this study a joint maximum likelihood (JML) algorithm was developed to solve problems regarding interdependent and contradictory relationships between track correlation and sensor bias estimation in multi-sensor information fusion systems. First, the relationships between track correlation and sensor bias estimation of a multi-sensor system were analyzed. Then, based on these relationships, a JML function of the track correlation and sensor bias estimation was developed, while an iterative two-step optimization procedure was adopted to solve the JML function. In addition, transformation of sensor bias from Cartesian coordinates to polar coordinates and a complete design of track quality and ambiguity processing were provided. Finally, several Monte Carlo simulations were built to test the effect of target density and different sensor bias in the JML algorithm. Simulation results showed that the JML algorithm presented in this paper had a higher correct correlation rate and more accurate sensor bias estimation than traditional methods, demonstrating that the JML algorithm had good performance.