Performance of Doppler estimation for acoustic sources with atmospheric scattering

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

2005

Self Cite

The nodes in a sensor network are often deployed in random locations. However, most applications require that the location and/or orientation of the nodes be known, so post-deployment algorithms for self-localization of the sensor nodes are important. We consider using a mobile access point (AP) for sensor node localization in a randomly deployed sensor network. We focus on the particular case in which the sensors measure the acoustic Doppler shift in a tone that is emitted from the mobile AP. In addition to the acoustic emission, the mobile AP broadcasts a radio signal that contains AP position, velocity, timing, and parameters of the acoustic signal. We demonstrate that atmospheric turbulence has a significant impact on the accuracy of sensor localization, degrading performance by as much as two orders of magnitude relative to an ideal, planewave propagation model. We present Cramer-Rao bounds (CRBs) for sensor localization accuracy and compare the performance of algorithms to the CRBs under "cloudy" and "sunny" weather conditions.

Section: Turbulence Model and Doppler Estimationmentioning

confidence: 99%

“…In [8], we have studied CRBs and algorithms for Doppler estimation in turbulence. Two algorithms for Doppler estimation are summarized here because they are used in this paper to perform sensor self-localization.…”

Section: Turbulence Model and Doppler Estimationmentioning

confidence: 99%

Sensor localization using acoustic doppler shift with a mobile access point

Kozick

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

2005

Self Cite

“…The CRBs are not improved by higher SNR (this is the high-SNR limit, which is valid when the SNR > 20 dB). We have shown in [5] that ignoring turbulent scattering leads to overly optimistic CRBs, so modeling the scattering is essential in order to get realistic performance bounds.…”

Section: Doppler and Sensor Localizationmentioning

confidence: 99%

“…Note from (11) that the scattering becomes more severe at higher source frequencies and larger ranges. Figure 2 shows the CRB on Doppler-shift (using the model in [5]) for a scenario that is characteristic of a helicopter serving as a mobile AP: processing bandwidth B = 80 Hz, bandwidth of scattered signal B v = 1 Hz, observation time T = 1.5 sec, and high signal-to-noise ratio (SNR). The CRBs in Figure 2 represent lower bounds on the standard deviation of Dopplershift estimates.…”

Section: Doppler and Sensor Localizationmentioning

confidence: 99%

“…We have analyzed the performance of acoustic Dopplershift estimation in [5], where we used a physics-based statistical model for the random scattering induced by turbulence in the atmosphere. CRBs are presented in [5] that illustrate the performance limits on Doppler-shift estimation as a function of the atmospheric conditions, the frequency of the source, and the range of the source. The turbulence scatters a fraction Ω ∈ [0, 1] of the tone energy emitted by the source into a narrowband, zero-mean random process.…”

Section: Doppler and Sensor Localizationmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Modal Sensor Localization Using a Mobile Access Point

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

Kozick²,

Tong³

We consider the problem of sensor node localization in a randomly deployed sensor network, using a mobile access point (AP). The mobile AP can be used to localize many sensors simultaneously in a broadcast mode, without a preestablished sensor network. We consider a multi-modal approach, combining radio and acoustics. The radio broadcasts timing, location information, and acoustic signal parameters. The acoustic emission may be used at the sensor to measure Doppler stretch, time delay, and angle of arrival. These measurements are individually sufficient to localize a sensor node, or they may be advantageously combined. We focus on the cases of Doppler and time delay. Sensor localization algorithms are developed, and performance analysis includes acoustic propagation effects caused by the turbulent atmosphere.

A Survey of Time Delay Estimation Performance Bounds

Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.

Kozick

Self Cite

1 In this paper we review performance bounds, as well as some current trends, in time delay estimation (TDE). Research over several decades reveals that a few key parameters determine TDE performance. The most basic are the signal-to-noise ratio (SNR), and the signal time-bandwidth (T B) product; larger values for each are desirable. The Cramér-Rao bound (CRB) reveals asymptotic maximum-likelihood estimation (MLE) behavior with respect to TB and SNR. At moderate to lower SNR, TDEs generally break down as ambiguities arise due to increased noise and the cross-correlation of the signal, causing the TDE to deviate (often quite sharply) away from the CRB. Because it is a local bound, the CRB does not indicate the threshold behavior, and ZivZakai and other bounds have been developed to handle this. When TD is measured between multiple sensors, the coherence between them can fundamentally limit the result, an effect that occurs in acoustics due to the turbulent atmosphere. We discuss modifications to the classical bounds that accommodate the coherence loss, and reveal a threshold coherence phenomenon. When communications and other signals are utilized for TDE, they may have significant nuisance parameters, including carrier uncertainty, unknown symbols, as well as effects due to an unknown channel. Recent TDE performance limits reveal the effect of these parameters for various signal models, including the impact of diversity channels on TDE.