Dr. Hwa-Tung Ong Profile

Dr. Hwa-Tung Ong

at Defence Science and Technology Organisation

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Publications (3)

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Proceedings Article | 5 January 2004 Paper

Sensor registration using airlanes: maximum likelihood solution

Hwa-Tung Ong

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.502491

KEYWORDS: Sensors, Radar, Image registration, Geodesy, Monte Carlo methods, Signal to noise ratio, Data conversion, Error analysis, Radar sensor technology, Target detection

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In this contribution, the maximum likelihood estimation of sensor registration parameters, such as range, azimuth and elevation biases in radar measurements, using airlane information is proposed and studied. The motivation for using airlane information for sensor registration is that it is freely available as a source of reference and it provides an alternative to conventional techniques that rely on synchronised and correctly associated measurements from two or more sensors. In the paper, the problem is first formulated in terms of a measurement model that is a nonlinear function of the unknown target state and sensor parameters, plus sensor noise. A probabilistic model of the target state is developed based on airlane information. The maximum likelihood and also maximum a posteriori solutions are given. The Cramer-Rao lower bound is derived and simulation results are presented for the case of estimating the biases in radar range, azimuth and elevation measurements. The accuracy of the proposed method is compared against the Cramer-Rao lower bound and that of an existing two-sensor alignment method. It is concluded that sensor registration using airlane information is a feasible alternative to existing techniques.

Proceedings Article | 26 November 2001 Paper

Association and fusion of air target tracks with airlanes

Hwa-Tung Ong, Martin Oxenham

Proceedings Volume 4473, (2001) https://doi.org/10.1117/12.492804

KEYWORDS: Sensors, Target recognition, Information fusion, Knowledge management, Surveillance, Error analysis, Radar, Data fusion, Data processing, Device simulation

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Association of air targets with airlanes is a problem of interest in wide area surveillance because of its application to target identification, situation assessment and sensor registration. This problem has been previously considered for the single airlane scenario, under the assumption that the track state estimates are Gaussian distributed. In this paper, under the same assumptions, a recent solution based on statistical hypothesis tests is generalized and extended in three ways. First, the association test is generalized to the multiple airlane scenario. If the target can be associated with more than one airlane, the ambiguity is resolved by employing the test statistic of the association test as a discriminant. Secondly, a probabilistic state model based on airlane information is formulated for the corresponding airlane. Finally, the track data is fused with the associated airlane to improve target state estimates. Simulation results are presented for both unbiased and biased sensor measurements in terms of the probability of association for each airlane, and the root mean square error of fused and unfused target position and velocity estimates.

Proceedings Article | 27 July 1999 Paper

CFAR detector for target returns with unknown phase and frequency in unspecified non-Gaussian noise

Hwa-Tung Ong, Abdelhak Zoubir

Proceedings Volume 3720, (1999) https://doi.org/10.1117/12.357190

KEYWORDS: Sensors, Target detection, Signal detection, Chlorine, Interference (communication), Error analysis, Radar, Fourier transforms, Statistical methods, Statistical analysis

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In pulsed radar and sonar systems, the target return has unknown phase, and also unknown frequency, if the target is moving. In unspecified non-Gaussian noise, optimal detectors are unavailable, and current single sensor, noncoherent techniques rely on the frequency being known. When frequency estimates are substituted for the unknown frequency in these latter detectors, they fail completely because their thresholds do not take into account the uncertainty of the frequency estimator. In this contribution, we propose a detector based on the peak of the finite Fourier transform and the bootstrap. The bootstrap is a statistical method for estimating the sampling distribution of a statistic from the sample data itself. In this way, modeling assumptions about the noise and signal are relaxed. This advantage of using the bootstrap is seen from theoretical results and simulations presented. We demonstrate that a constant false alarm rate is achievable even for heavily skewed interference, while detection rates of 99% are possible for data sizes as low as 100 samples and -5 dB signal-to- noise ratio. Some asymptotic properties of the detector are given. In the simulations, we also present a comparison of the detector with the classical detectors based on least squares regression and based on uniform random phase. It is seen that our proposed method compares favorably with these methods in that, when the frequency is known, the proposed method is only slightly less powerful than the classical detectors for Gaussian noise. It continues to do well for heavy-tailed and skewed non-Gaussian noise such as t- distributed and Gaussian mixture noise. For unknown frequency, it is still able to obtain a high detection rate when the classical detectors fail completely.

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