Room reverberation introduces multipath components into an audio signal and causes problems for acoustic source localization and tracking. Existing tracking methods based on the extended Kalman filter (EKF) and sequential importance resampling based particle filter (SIR-PF) usually assume that a single source is constantly active in the tracking scene. Assuming that multiple talkers may appear alternatively during a conversation, this paper develops an extended Kalman particle filtering (EKPF) approach for nonconcurrent multiple acoustic tracking (NMAT). Essentially, an EKF is introduced to obtain an optimum importance sampling, by which the particles are drawn according to the current time-delay of arrival (TDOA) measurements as well as the previous position estimates. Hence, the proposed approach can quickly adapt to the sharp position change when the source switches and the tracking lag in SIR-PF can be avoided. Moreover, the amplitude of the * corresponding author Email addresses: xhzhong@ntu.edu.sg (Xionghu Zhong), James.Hopgood@ed.ac.uk (James R. Hopgood) Preprint submitted to Elsevier Signal ProcessingMay 25, 2013 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 TDOA measurement is investigated to formulate a measurement hypothesis prior. Such a prior is fused into the tracking algorithm to enhance the tracking accuracy. Both simulations and real audio lab experiments are organized to study the tracking performance. The results demonstrate that the proposed EKPF approaches outperforms the SIR-PF and EKF in a broad range of tracking scenarios. *Manuscript Click here to view linked References

Minimising the expected mean squared error is one of the fundamental metrics applied to adaptive waveform design for active sensing. Previously, only cost functions corresponding to a lower bound on the expected mean squared error have been expressed for optimisation. In this paper we express an exact cost function to optimise for minimum mean squared error adaptive waveform design (MMSE-AWD). This is expressed in a general form which can be applied to non-linear systems. Additionally, we provide a general example for how this method of MMSE-AWD can be applied to a system that estimates the state using a particle filter (PF). We make the case that there is a compelling reason to choose to use the PF (as opposed to alternatives such as the Unscented Kalman filter and extended Kalman filter), as our MMSE-AWD implementation can re-use the particles and particle weightings from the PF, simplifying the overall computation. Finally, we provide a numerical example, based on a simplified multiple-input-multiple-output radar system, which demonstrates that our MMSE-AWD method outperforms a simple non-adaptive radar, whose beam-pattern has a uniform angular spread, and also an existing approximate MMSE-AWD method.

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Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g. time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralised and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparision to the baseline non-offloading case, however, job completion rate is significantly improved, the efficiency score metric show the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms.

The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e. if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, nor in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only.