For real-time acoustic source localization applications, one of the primary challenges is the considerable growth in computational complexity associated with the emergence of ever larger, active or passive, distributed sensor networks. These sensors rely heavily on battery-operated system components to achieve highly functional automation in signal and information processing. In order to keep communication requirements minimal, it is desirable to perform as much processing on the receiver platforms as possible. However, the complexity of the calculations needed to achieve accurate source localization increases dramatically with the size of sensor arrays, resulting in substantial growth of computational requirements that cannot be readily met with standard hardware. One option to meet this challenge builds upon the emergence of digital optical-core devices. The objective of this work was to explore the implementation of key building block algorithms used in underwater source localization on the optical-core digital processing platform recently introduced by Lenslet Inc. This demonstration of considerably faster signal processing capability should be of substantial significance to the design and innovation of future generations of distributed sensor networks.

Acoustic source localization by means of distributed sensor networks requires very accurate time delay estimation. Also, due to phenomenon like reverberation or environmental additive noise, the intrasensor distance cannot be made very large without reducing the coherence between the signals whose mutual delay has to be estimated. The use of passive sensor arrays for estimating the position of a generic acoustic source represents an old and well-investigated area. Time delay estimation techniques have been applied extensively to this area. Many of these techniques are specific to the geometrical configuration adopted for array placement thus imposing heavy restrictions on the choice of sensor configuration. For example, in the area of naval surveillance, much attention has focused on adaptive beam-forming, primarily in the context of rigid-geometry towed arrays [

A distributed sensor network detecting a submarine lurking underwater.

The Center for Engineering Science Advanced Research (CESAR) at the Oak Ridge National Laboratory is involved in the development and demonstration of exciting unconventional technologies for Distributed Sensor Signal (DSS) processing. The CESAR efforts in the area of DSS processing are driven by the emergence of powerful new processors such as the IBM CELL [

This paper begins with a presentation of the key concepts of threat-detection algorithms such as TDOA estimation via sensor data correlation in both time and frequency domains. A brief overview of the EnLight device is also presented along with the above mentioned fundamental concepts. Next, the implementation of TDOA calculations on the EnLight platform is presented with the aid of numerical simulation and actual optical hardware runs. The paper concludes by highlighting the major accomplishments of this research in terms of computational speedup and numerical accuracy achieved via the deployment of optical processing technology in a distributed sensor framework. This paper omits discussions of the statistical nature and hypothesis testing associated with target detection decision. The theory assumes that the received signals are cross-correlated for an estimation of the TDOA which provides a starting point for target-tracking in time, velocity, and space. The algorithm is designed for a single sound source localization using a distributed array of acoustic sensors. Conventional TDOA estimation procedures are used. The major focus of this paper is the time-domain implementation of TDOA estimation although the frequency domain analysis is briefly discussed. The frequency domain counterpart of the analysis, complete with matched filter bank simulation for active sonar platforms detecting both target range and velocity via Doppler-sensitive waveform synthesis and generation, is presented in previous publications by the authors [

Locating/tracking an acoustic target involves the estimation of mutual time delays between the direct-path wavefront arrivals at the sensors. Using an array of multiple sensors, the TDOAs of the received signals are measured. The TDOAs are proportional to the differences in sensor-source range, called range differences. In order to reduce analytical and computational complexity, it is common practice to make a number of critical assumptions for TDOA calculations. Far-field geometry is usually assumed concerning the location of the target, which is justified by the nominally small aperture of the sensor array. This, in turn, allows the use of the plane wave approximation in the design of TDOA algorithms. For intrasensor spacing, a regular grid is considered with a grid resolution in excess of

Explicitly accounting for uncertainties in model parameters and sensor measurements has been found critical in many areas of science and engineering. Here, the source localization problem could be addressed by adapting the recently developed Nonlinear Optimization Generalized Adjustments (NOGA) methodology [

Methodologies for threat source localization.

A signal

Research efforts at Oak Ridge National Laboratory include the feasibility demonstration of high-precision computations for grand challenge scientific problems using the novel, Lenslet-developed,

The EnLight optical device. The architecture of this device provides a strong rationale for using it in matrix-based applications.

In mobile target detection schemes, such as active sonar systems, the accurate estimation of TDOA by filtering through severely noisy data is crucial for tracking and target parameter (such as velocity) estimation. To benchmark the EnLight performance, three computer codes were written, one using the Intel Visual FORTRAN-95 compiler, one using the

The distributed sensor-net and target coordinates.

For assessing the accuracy of the EnLight computations, a very simple model is considered. It is assumed that the target emits a periodic pulsed signal with unit nominal amplitude. Pulse duration is 1 SI (Sample Interval) and interpulse period is 25 SIs. The size of one sampling interval is 0.08 seconds. Noise and interference are taken as Gaussian processes with varying power levels (typically up to unity). Each sensor stores sequences of measured signal samples. Sequence lengths can range from 1 K to 80 K samples. The signature from the threat source becomes harder to distinguish as the noise and interference level rises. This contributes to the rationale for using correlation techniques in the source localization process.

The simulation comprised of two approaches. For the first scenario, calculations were done in the frequency domain and the cross-power spectrum for each pair of sensors was computed from the corresponding finite-length data sequences following the methodology described in (

(a) TDOA magnitude (in units of sampling intervals) versus sensor pairs (ordered lexicographically) for 7 active sensors. Exact results are in blue; sensor-inferred results (computed using 64-bit floating-point FORTRAN) are in brown,

(a) TDOA magnitude (in units of sampling intervals) versus sensor pairs (ordered lexicographically) for 7 active sensors. Exact results are in blue; sensor-inferred results (computed using Intel Visual FORTRAN) are in brown,

For the time domain analysis, the cross-correlation

The details of the matrix memory and vector register loading scheme for the EnLight processing board.

The EnLight processor is ideal for implementing large time series correlation calculations in terms of matrix-vector multiplication operations. The processor works as a matrix-vector multiplier in which a complete MVM operation is performed for each machine cycle (

Hierarchical structure of the EnLight software interface.

(a) The correlation function obtained via MATLAB simulation (blue) and

The correlation functions

Comparison of the magnitudes of the cross-correlation functions obtained via MATLAB simulations and hardware runs for sensor pairs (1,2), (1,3), (1,4), (1,5), (1,6), (1,7).

MATLAB | 79.1978 | 95.5889 | 80.3471 | 100.9231 | 123.0550 | 181.2274 |

65.0842 | 103.5966 | 85.9405 | 91.9592 | 137.1272 | 185.1162 | |

17.82% | 8.38% | 6.96% | 8.88% | 11.44% | 2.15% |

We have presented an example case where the correlation lags (Figures

Distributed sensors with optical computing platforms as onboard devices present an attractive alternative to conventional dedicated sensor arrays. Future advances in DSS signal processing for improved target detection, tracking, and classification in highly noise-corrupted environments can be realized through the development of distributed systems that combine superior sensors and highly efficient computational nodes consisting of optical-core devices such as the EnLight platform. Emerging classes of distributed sensors for naval target detection algorithms employ data/information fusion of diverse transmit waveforms such as Constant Frequency (CF), Linear Frequency Modulation (LFM), and Sinusoidal Frequency Modulation (SFM) [

The authors acknowledge helpful discussions with Michael Wardlaw (Office of Naval Research), Aviram Sariel (Lenslet), Shimon Levit (Weizmann Institute and Lenslet), and Jeffrey Vetter (ORNL). Primary funding for this work was provided by the Office of Naval Research. Additional support was provided by the ORNL Laboratory Directed Research and Development (LDRD) program. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the US Department of Energy under contract number DE-AC05-00OR22725.