Sparse Bayesian Inference-Based Direct Off-Grid Position Determination in Multipath Environments

Ke-gang, Hao; Wan, Qun

doi:10.1109/lwc.2021.3057502

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Signal fitting methods are proposed in [11] and achieve better results than multiple signal classification (MUSIC). Nevertheless, these methods only consider the ideal position scenario and often exhibit poor performance under multipath environments [12]. Position determination in the presence of multipath signals has attracted much attention [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Cao,

Li,

Tang

et al. 2023

Sensors

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A vehicle’s position can be estimated with array receiving signal data without the help of satellite navigation. However, traditional array self-position determination methods are faced with the risk of failure under multipath environments. To deal with this problem, an array signal subspace fitting method is proposed for suppressing the multipath effect. Firstly, all signal incidence angles are estimated with enhanced spatial smoothing and root multiple signal classification (Root-MUSIC). Then, non-line-of-sight (NLOS) components are distinguished from multipath signals using a K-means clustering algorithm. Finally, the signal subspace fitting (SSF) function with a P matrix is established to reduce the NLOS components in multipath signals. Meanwhile, based on the initial clustering estimation, the search area can be significantly reduced, which can lead to less computational complexity. Compared with the C-matrix, oblique projection, initial signal fitting (ISF), multiple signal classification (MUSIC) and signal subspace fitting (SSF), the simulated experiments indicate that the proposed method has better NLOS component suppression performance, less computational complexity and more accurate positioning precision. A numerical analysis shows that the complexity of the proposed method has been reduced by at least 7.64dB. A cumulative distribution function (CDF) analysis demonstrates that the estimation accuracy of the proposed method is increased by 3.10dB compared with the clustering algorithm and 11.77dB compared with MUSIC, ISF and SSF under multipath environments.

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Section: Introductionmentioning

confidence: 99%

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Cao,

Li,

Tang

et al. 2023

Sensors

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“…As we all know, wireless position determination is a significant research work about signal processing, which has developed rapidly in recent years. It has been widely used in many fields including communication, radar, military, astronomy, and so on [1][2][3][4]. Research on wireless location can be divided into two categories: traditional two-step location technology and direct position determination technology (DPD).…”

Section: Introductionmentioning

confidence: 99%

Weighted Direct Position Determination via the Dimension Reduction Method for Noncircular Signals

Shi

Zhang

2021

Mathematical Problems in Engineering

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This work studies the direct position determination (DPD) of noncircular (NC) signals with multiple arrays. Existing DPD algorithms of NC sources ignore the impact of path propagation loss on the performance of the algorithms. In practice, the signal-to-noise ratios (SNRs) of different observation stations are often different and unstable when the NC signal of the same radiation target strikes different observation locations. Besides, NC features of the target signals are applied not only to extend the virtual array manifold but also to bring high-dimensional search. For the sake of addressing the above problems, this study develops a DPD method of NC sources for multiple arrays combing weighted subspace data fusion (SDF) and dimension reduction (RD) search. First, NC features of the target signals are applied to extend the virtual array manifold. Second, we assign a weight to balance the error and obtain higher location accuracy with better robustness. Then, the RD method is used to eliminate the high computational complexity caused by the NC phase search dimension. Finally, the weighted fusion cost function is constructed by using the eigenvalues of the received signal covariance matrixes. It is verified by simulation that the proposed algorithm can effectively improve the location performance, get better robustness, and distinguish more targets compared with two-step location technology and SDF technology. In addition, without losing the estimation performance, the proposed algorithm can significantly reduce the complexity caused by the NC phase search dimension.

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“…Different from the conventional two-step approach, a new technology called direct position determination (DPD) estimates the emitter position from received signals straightforwardly [12,13,14,15,16,17,18,19,20]. The DPD technology uses the data collected by multiple receivers to formulate a cost function that depends on the location of the emitter.…”

Section: Introductionmentioning

confidence: 99%

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

Wang¹,

Sun²,

Wan³

et al. 2021

Preprint

Self Cite

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Emitter localization is widely applied in the military and civilian fields. In this paper, we tackle the problem of position estimation for multiple stationary emitters using Doppler frequency shifts and angles by moving receivers. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on the Pincus' theorem and importance sampling (IS) concept, we propose a novel non-iterative ML DPD method. The proposed method transforms the original multidimensional grid search into random variables generation with multiple low-dimensional pseudo-probability density functions (PDF), and the circular mean is used for superior position estimation performance. The computational complexity of the proposed method is modest, and the off-grid problem that most existing DPD techniques face is significantly alleviated. Moreover, it can be implemented in parallel separately. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the Cramér-Rao lower bound (CRLB), even in the adverse conditions of low signal-to-noise ratios (SNR) levels.

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Sparse Bayesian Inference-Based Direct Off-Grid Position Determination in Multipath Environments

Cited by 11 publications

References 10 publications

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Self-Position Determination Based on Array Signal Subspace Fitting under Multipath Environments

Weighted Direct Position Determination via the Dimension Reduction Method for Noncircular Signals

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

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