The optimal data-fusion rule was first established for multiple-sensor detection systems in 1986. Most subsequent works have been focused on the corresponding implementation aspects. The probability of false alarm and the probability of miss detection used in this data-fusion rule are quite difficult to precisely enumerate in practice. Although the improved data-fusion implementation techniques are now available, most existing cooperative spectrum-sensing techniques are still based on the simple energy-detection algorithm, which is prone to failure in many scenarios. In this paper, we propose a novel adaptive cooperative spectrum-sensing scheme based on our recently proposed single-reception spectrum-sensing technique. We also found that the commonly-used sample-average estimator for the cumulative weights in the data-fusion rule becomes unreliable in time-varying environments. To overcome this drawback, we adopt a temporal discount factor, which is crucial to the probability estimators. New theoretical analysis to justify the advantage of our proposed new estimators over the conventional sample-average estimators and to determine the optimal numerical value of the proposed discount factor is presented. The Monte Carlo simulation results are also provided to demonstrate the superiority of our proposed adaptive cooperative spectrum-sensing method in both stationary and time-varying environments.

In this paper, an important statistical signal processing characteristic, namely Gaussianity or normality, is studied. In contrast to the existing Gaussianity measures, we propose a novel measure, which is based on Kullback-Leibler divergence (KLD) between the Gaussian probability density function (PDF) and the generalized Gaussian PDF incorporated with the skewness for the normality test. In our studies, conventional normality tests may often not be robust when they are employed for the non-Gaussian processes with symmetric PDFs. We call this new test as the KGGS test. Our proposed KGGS test is heuristically justified to be more robust than conventional tests for different PDFs, especially symmetric PDFs. A popular application of the normality test for QPSK signal detections is also presented to verify the effectiveness of our proposed technique and the simulation results demonstrate that our new KGGS test would outperform all others even for sparse data samples.

Source localization using acoustic sensor networks has been drawing a lot of research interest recently. In a sensor network, there are a large number of inexpensive sensors which are densely deployed in a region of interest (ROI). This dense deployment enables accurate intensity (energy) based target localization. The maximum-likelihood is the predominant objective which leads to a variety of source localization approaches. However, the investigation on the energy-based localization for multiple sources has been very rare. The corresponding robust and efficient algorithms are still being pursuit by researchers nowadays. In this paper, we would like to combat the energy-based multiple-source localization problem. We propose two new algorithms, namely alternating projection (AP) algorithm and expectation maximization (EM) algorithm, which can combat the energy-based localization problem for multiple sources.

The optimal data fusion rule for multiple sensor detection systems based on the Bayesian criterion has been derived by Chair and Varshney in 1986. However, most of the following works are focused on how to implement such a fusion rule, since the probability of false alarm and the probability of miss detection are hard to evaluate in practice. Till now, although more and more satisfactory data-fusion implementation schemes are available, most of the cooperative spectrum sensing techniques are based on the simple energy-detection algorithm, which only relies on the energy of the received signal. However, when noise is relatively large or the time-varying characteristics of the signal are conspicuous, the energy-detection spectrum sensing algorithm is more prone to fail. Thus, in this paper, we propose a new adaptive cooperative spectrum sensing scheme, which is based on a novel detection algorithm involving JB (Jarque-Bera) statistic. The ROC (receiver-operating characteristic) curves show that our new cooperative spectrum sensing scheme is more robust than that based on the energy-detection spectrum sensing algorithm. Besides, the performance comparison also implies that the optimal data-fusion rule in our new cooperative spectrum sensing scheme is superior to the commonly adopted “OR” and “AND” rules in the existing literature.

Source localization for wideband signals using acoustic sensor networks has drawn much research interest recently. The maximum-likelihood is the predominant objective for a wide variety of source localization approaches, and we have previously proposed an expectation-maximization (EM) algorithm to solve the source localization problem. In this paper, we tackle the source localization problem based on the realistic assumption that the sources are corrupted by spatially-non-white noise. We explore the respective limitations of our recently proposed algorithm, namely EM source localization algorithm, and design a new direction-of-arrival (DOA) estimation based (DEB) source localization algorithm. We also derive the Cramer-Rao lower bound (CRLB) analysis and the computational complexity study for the aforementioned source localization schemes. Through Monte Carlo simulations and our derived CRLB analysis, it is demonstrated that our proposed DEB algorithm significantly outperforms the previous EM method in terms of both source localization accuracy and computational complexity.

Source localization for wideband signals using near-field acoustic sensor networks has been drawing a lot of research interest recently. In this paper, we would like to tackle the source localization problem based on the realistic assumption where the sources are corrupted by spatially-non-white noise. We explore the respective limitations of our recently proposed algorithm, namely EM source localization algorithm, and design a new direction-of-arrival (DOA) estimation based (DEB) source localization algorithm. The simulation results demonstrate that our proposed DEB algorithm significantly outperforms the previous EM method.

In this paper, the DTV (digital television) spectrum sensing problem is studied, which plays a key role in the cognitive radio. In contrast to the existing higher-order-statistics (HOS) approach, we propose a novel robust spectrum-sensing method, which is based on the JB (Jarqur-Bera) statistic. In our studies, the existing detector may often not be robust when the sample size is small. Our proposed JB detector is heuristically justified to be superior for the simulated microphone signals as well as the real DTV signals. Moreover, the computational complexity analysis for our proposed new JB detector and the HOS detector is also presented. Ultimately, the normality test and the spectral analysis are provided to justify the advantage of our proposed spectrum sensing method.

In this paper, the DTV (digital television) spectrum sensing problem is studied, which plays a key role in the cognitive radio. In contrast to the existing higher-order-statistics (HOS) approach, we propose a novel robust spectrum-sensing method, which is based on the JB (Jarqur-Bera) statistic. In our new studies, the existing HOS spectrum sensing technique may often not be robust. Our proposed JB-statistic based detector has been justified to be superior for the simulated microphone signals as well as the real DTV signals.

In this paper, a fundamental but important statistical signal processing characteristic, namely the Gaussianity or normality, is studied. In contrast to the existing conventional Gaussianity measures, we propose a novelmeasure, which is based on Kullback-Leibler divergence (KLD) between the Gaussian probability density function (PDF) and the generalized Gaussian PDF incorporated with the skewness for the normality test. In our studies, conventional normality tests may often not be robust when they are employed for the non-Gaussian processes with symmetric PDFs. We call this new test as the KGGS test. Our proposed KGGS test is heuristically justified to be more robust than the conventional tests for different PDFs, especially for the symmetric PDFs.