Wan Qun

Influence of Random Carrier Phase on True Cramer-Rao Lower Bound for Time Delay Estimation

The true Cramer-Rao lower bound (CRB) for the time delay estimation has been obtained for narrowband signals with the carrier phase as a deterministic parameter already. However, the carrier phase is usually a random parameter in noncoherent receiver in the applications such as radar, sonar and communication systems. And accuracy of the time delay estimation is affected by this nuisance carrier phase. In this paper, the true Cramer-Rao lower bound for the time delay estimation is derived and analyzed in the presence of a random carrier phase. The new bound is tighter than the one obtained under the condition that the carrier phase is not random. We show that this relation indicates the influence of the random carrier phase on the true CRB, and the penalty resulting from this random carrier phase increases severely with decreasing signal-to-noise ratio. The explanation about this influence is also given from the point of information theory. Simulations are provided to support the theoretical results.

Multidimensional scaling algorithm of localization with hybrid TOA and AOA measurements

Recently, multidimensional scaling (MDS) algorithms have been verified to be robust for the emitter localization. However, the traditional MDS algorithms are based on single one kind of measurement. In this paper, a novel hybrid MDS localization algorithm based on both angle-of-arrival (AOA) and time-of-arrival (TOA) measurements is proposed. In the condition that one of the sensors can obtain the AOA of the emitter, this algorithm utilizes AOA measurement to improve the traditional MDS localization based on TOA measurements. The simulation results demonstrated the improvement of this algorithm.

Robust location algorithm for NLOS environments

Abstract One of the main problems facing accurate location in wireless communication systems is non-line-of-sight (NLOS) propagation. Traditional location algorithms are based on classical techniques under minimizing a least-squares objective function and it loses optimality when the NLOS error distribution deviates from Gaussian distribution. An effective location algorithm based on a robust objective function is proposed to mitigate NLOS errors. The proposed method does not require the prior knowledge of the NLOS error distribution and can give a closed-form solution. A comparison is performed in different NLOS environments between the proposed algorithm and two additional ones (LS method and Chans method with an NLOS correction). The proposed algorithm clearly outperforms the other two.

Joint DOA and time delay estimation method for space-time coherent distributed signals based on search

Abstract Under dense urban fading environment, performance of joint multi-path parameter estimation method based on traditional point signal model degrades seriously. In this paper, a new space and time signal model based on multipath distribution function is given after new space and time manifold is reconstructed. Then joint spacetime signal subspace is obtained by converting acquired channel from time domain to frequency domain. Then space and time spectrum is formulated by the space sub-matrix and time sub-matrix taken out of joint space-time signal subspace, and parameters are estimated by searching the minimum eigenvalues of the space matrix and the time matrix. Lastly, A space and time parameters matching process is performed by using the orthogonal property between joint noise subspace and the space-time manifold. In contrast with tradition MUSIC, the algorithm we present here only need two 1-dimension searching and was not sensitive to difierent distribution function.

A novel imaging algorithm with random phase error mitigation for the FMCW SC-SAR system

In this paper, millimeter-wave frequency modulated continuous wave (FMCW) imaging of foreign object debris (FOD) which is potential hazard on airport runway is studied with the help of Spotlight Circular Synthetic Aperture Radar (SC-SAR) system. First, the signal model of FMCW SC-SAR is analyzed. Then, a novel imaging algorithm with random phase error mitigation is developed by virtual of the advanced Range-Doppler method and phase gradient autofocus (PGA). Finally, the experimental results are presented to show the validity of our proposed imaging method for the FMCW SC-SAR system with random phase error.

Direction of arrival estimation and identification of mixed circular and non-circularity sources

Most of existing array signals processing methods are based on the homogeneous circular or non-circular sources. However, mixed circular and non-circular sources are frequently encountered in increasingly complex situations. Since the useful properties of the mixed source model are not utilized efficiently, there is significant performance space between the methods based on homogeneous source model and the theoretical bound. In this paper, an enhanced direction-of-arrival(DOA) estimation and identification method for mixed circular and non-circular sources is proposed. Simulation results show that it can improve the resolution capability and the number of mixed sources can be larger than the number of array elements.

Acoustic Localization in Multi-path Aware Environments

A novel acoustic source localization approach utilizing multi-path information in a reverberant environment is proposed. Making use of multi-path information, virtual sensors can be constructed. In this case, time difference of arrival (TDOA) caused by multi-path can be used for localization, and a closed-form solution to the acoustic source location can be obtained by only two sensors.

Multidimensional scaling-based passive emitter localization from TOA measurements with sensor position uncertainties

If the sensor positions are not precise, the localization accuracy is significantly degraded. This paper derives a weighted multidimensional scaling estimator for estimating the position of a stationary emitter with sensor position uncertainties using time of arrival measurements. It is an effective and closed-form solution. Simulation results show that the proposed estimator can achieve the Cramer-Rao lower bound performance over small noise region and it performs better than the two-step weighted least squares approach and traditional multidimensional scaling estimator ignoring sensor positions uncertainty.

Squared distance matrix completion through Nystrom approximation

In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large area. The Nystrom algorithm has overcome the classical multidimensional scaling in a low and moderate signal to noise ratio, in addition it performs well as the number of missing entries increase. The plotted figures show admissible consequences for the proposed algorithm.

Sources localization via multi-dimesional scaling based on subspace method

There is no doubt that the issue of estimate locations for unknown sources has been mattering a lot of debate in recent years, in the field of communication, Radar, Navigation, wireless sensor networks, ...etc. In this paper, the multidimensional scaling (MDS) based on subspace method was applied to solve the addressed issue in the presence of distance information that acquired from TOA measurements. The Simulation results show the significant consequences compared with Cramer-Rao lower bound (CRLB) as well as the modified version of classical Multi-dimensional scaling, it became close to the lower bound of variance when the signal to noise ratio increased.