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A model based approach is developed to track and trace multiple incoherent sound sources in 3D space in real time. This technology is capable of handling continuous, random, transient, impulsive, narrowband and broadband sounds over a wide frequency range (20 to 20,000 Hz). The premise of this technology is that the sound field is generated by point sources located in a free field. To locate these sound sources, iterative triangulations are used based on the signals measured by a microphone array. These signals are preprocessed through de-noising techniques to enhance signal to noise ratios (SNR). Unlike the conventional beamforming, the present technology enables one to pinpoint the exact locations of multiple incoherent sound sources simultaneously by using the Cartesian coordinates, including sources behind measurement microphones. In other words, the microphone array need not face a test object, which is required in the beamforming.

This paper aims to develop some enhanced identification algorithms for real time characterization of battery dynamics. The core of such a system is advanced system identification techniques that provide fast tracking capability to update battery cell's individual models in real-time operation. Due to inevitable measurement noises on voltage and current observations, the identification algorithms must perform under both input and output noises, leading to more challenging issues than standard identification problems. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares identification methods will encounter identification bias. This paper devises modified model structure and algorithms to resolve these issues. System identifiabihty, algorithm convergence, identification bias, and bias correction mechanisms are established.