Direct Position Determination Research Articles

An enhanced direct position determination of non-circular sources via sparse Bayesian inference and grid refinement strategy

In this study, an off-grid sparse Bayesian inference (OGSBI) based direct position determination (DPD) algorithm is investigated. Existing SBI-based DPD algorithms are confronted with the challenge of excessive computational loads and lack of consideration for non-circular (NC) signals. To address these limitations, we present an enhanced OGSBI-based DPD algorithm for multiple non-circular sources. By utilizing the conjugate information of the NC signals, we expand the dimensionality of the data matrix to achieve a significant improvement in the localization performance. Additionally, a grid refinement strategy is developed to alleviate the computational loads, which involves an initial search to determine the approximate source locations, followed by fine localization using a denser grid. Moreover, the computational complexity and Cramér-Rao lower bound are derived to provide a comprehensive analysis of the proposed algorithm. Numerical simulations demonstrate the superiority of the proposed algorithm in terms of both localization accuracy and computational efficiency.

Enhancing Direct Position Determination in Distributed Base Station Systems Through Time-Varying Quantization Design

Enhancing Direct Position Determination in Distributed Base Station Systems Through Time-Varying Quantization Design

Direct Position Determination With a Moving Extended Nested Array by Spatial Sparsity

Direct Position Determination With a Moving Extended Nested Array by Spatial Sparsity

An Iterative Direct Position Determination Approach Based on Doppler Frequency Shifts

An Iterative Direct Position Determination Approach Based on Doppler Frequency Shifts

Self-Position Awareness Based on Cascade Direct Localization Over Multiple Source Data

The global positioning system (GPS), which provides ubiquitous location-awareness with a constellation of satellites, has become an instrumental function of multiple mass-market applications. Satellite signals, however, may not be capable of penetrating obstacles in harsh environments (e.g., urban canyons, tree canopies, and flyovers). Hence, GPS may not provide adequate localization accuracy for applications like autonomous vehicles. Resorting to data fusion of heterogeneous signals emanating from multiple anchors, we advocate a self-localization method that provides accurate estimates of the vehicle position. To be more specific, several heterogenous emitters whose positions are known are used as anchors to determine the vehicle's position based on the weighted direct position determination (DPD) method that eliminates nonhomogeneity among different emitters. However, the weighted DPD method requires an exhaustive search of the parameter search space and is thus time-consuming. To reduce the computational burden, we propose a weighted cascade compensation estimator (WCCE) that is tailored for real-time tracking and self-localization. The proposed WCCE outperforms traditional DPD methods in terms of computational complexity while achieving nearly comparable localization accuracy. The effectiveness of the proposed method is corroborated by extensive simulated examples.

A Direct Position Determination Method Using Distributed UAVs With Synchronization Error

A Direct Position Determination Method Using Distributed UAVs With Synchronization Error

Direct Position Determination based on Passive Synthetic Aperture for Coherent Receivers

Direct Position Determination based on Passive Synthetic Aperture for Coherent Receivers

Direct Position Determination of Non-Circular Signals for Distributed Antenna Arrays: Optimal Weight and Polynomial Rooting Approach

Direct Position Determination of Non-Circular Signals for Distributed Antenna Arrays: Optimal Weight and Polynomial Rooting Approach

A Low-Complexity Direct Position Determination Method Based on the Diffusion Strategy in Wireless Sensor Array Networks

A Low-Complexity Direct Position Determination Method Based on the Diffusion Strategy in Wireless Sensor Array Networks

Sparse Direct Position Determination Based on TDOA Information in Correlation-Domain

The sparse direct position determination (DPD) method requires reconstructing the emitter position with prior knowledge. However, in non-cooperative localization scenarios, it is difficult to reconstruct the transmitted signal with the unknown signal form and propagation model. In this paper, a sparse DPD method based on time-difference-of-arrival (TDOA) information in correlation-domain is proposed. Different from the traditional sparse DPD method, the received signal is converted into correlation-domain, and the proposed dictionary matrix is generated by the quantized delay difference, which solves the pseudo-positioning problem. Compared to the conventional multi-signal classification (MUSIC) method, multi-frequency fusion (MFF) method, and two-step positioning algorithm, the proposed algorithm achieves higher positioning accuracy. The feasibility of the algorithm has been verified by both simulation and real-world measured tests.

Passive Direct Position Determination Based on KL Transform and Feature Matching

Passive Direct Position Determination Based on KL Transform and Feature Matching

Optimal linear array orientation design for 3D direct position determination via semi-Definite relaxation

This paper presents an optimization strategy for array orientations in a three-dimensional (3D) direct position determination (DPD) system. Specifically, we consider a scenario in which uniform linear arrays (ULAs) are used to locate emitters, and we seek to optimize the array orientations in terms of localization accuracy. The E-optimality criterion, which minimizes the spectral norm of the Cramér-Rao lower bound (CRLB), is exploited to formulate this problem. As the objective function is non-convex with bilinear structures, we leverage semi-definite relaxation (SDR) to transform it into a convex semi-definite programming (SDP) problem by substituting the bilinear terms with a matrix variable. In addition, we tackle the array orientation configuration problem in the context of multi-emitter scenarios and develop an SDR solution for large-scale antenna array systems. Simulation results validate that the ULAs with array orientations designed by the proposed SDR-based method have near-optimal localization performance.

Joint Direct Estimation of Emitter Location and Antenna Beam Direction Based on Baseband Reconstruction

This paper addresses the problem of locating a stationary noncooperative emitter using multiple moving receivers. A signal baseband reconstruction based algorithm is proposed to locate the emitter directly from the received signals, without assuming static delay and Doppler shift in any observing interval. Besides the information of time-differences-of arrival (TDOA) and frequency-differences-of-arrival (FDOA), the received signal strength (RSS) information is further utilized when the source emits signals in free space with an omni antenna. In a more complex scenario where the emitter adopts a directional antenna, an iterative algorithm is proposed to jointly estimate the emitter location and its antenna beam direction, where a traditional direct position determination (DPD) method is used to initiate the iteration. The Cramér-Rao lower bounds (CRLBs) of the emitter position and the beam direction are derived. Three numerical cases are included to investigate the performance of the proposed algorithms. The signal baseband reconstruction based DPD algorithm shows improved applicability when the assumption of static time delay and Doppler shift is inappropriate and performs similarly with the classical DPD methods when the assumption holds. In case of free space propagation, it shows that the localization accuracy can be improved when RSS is further utilized. When the transmitting beam is directional, however, simply ignoring the directivity characteristics would lead to nonnegligible localization biases. By jointly estimating the emitter location and antenna beam direction, the RSS information can be effectively utilized to improve the localization accuracy, and a much more accurate estimation of beam direction can be obtained simultaneously.

Multi-TDOA Estimation and Source Direct Position Determination Based on Parallel Factor Analysis

In this paper, source localization exploiting time difference of arrival (TDOA) information is discussed, and a parallel factor (PARAFAC) analysis based method for multi-TDOA estimation and direct position determination (DPD) is proposed. Firstly, signals from the radiation source are received by multiple antennas through synchronous sensing. Then, the data from multiple antennas is fused by extracting the time delay matrix to construct a PARAFAC model. Thereafter, the time delay matrix is obtained by fast iterative decomposition using the trilinear alternating least square (TALS) algorithm. In the case of no multipath or weak multipath, where the typical scenario is the antennas being deployed on the airborne platform, the multi-TDOA estimation can be obtained simultaneously according to the time delay matrix to improve the processing speed. In the case of multipath, such as the antennas being located on the ground, the DPD cost function directly related to the source position can be established based on the time delay matrix, and the source position estimation can be achieved through grid search. Compared with other DPD methods, the proposed DPD method has better positioning performance and lower complexity. The effectiveness and superiority of the proposed methods are verified by both simulations and actual scenario tests.

Direct Position Determination Using Compressive Sensing Measurements Without Reconstruction

This paper proposes a direct position determination (DPD) method for stationary emitters with compressed signal measurements. Employing Hadamard matrix properties, the method estimates the source location directly in the compressing measurement domain, without the step of signal reconstruction and time-differences-of-arrival (TDOA) parameter extraction. In case of free space propagation, an augmented DPD method is proposed to further utilize the position related received signal strength (RSS) information. In addition, the Cramér-Rao lower bounds (CRLBs) for emitter position of both DPD methods are derived for performance evaluation.

A Computationally Efficient Direct Position Determination Algorithm Based on OFDM System

Based on the orthogonal frequency division multiplexing (OFDM) system, a computationally attractive implementation for the traditional single source maximum likelihood (SML) direct position determination (DPD) algorithm is proposed. The proposed algorithm implements fast Fourier transform (FFT) to the received frequency-domain signals after deconvolution. Then, the cost function for all points can be obtained from the same FFT output set. As a consequence, some repeated calculation in the traditional SML DPD algorithm is avoided. Simulation results demonstrate that the proposed modified SML DPD algorithm can achieve a comparable performance to traditional SML-DPD while significantly reducing the computational complexity.

Direct position determination algorithm for non‐circular sources in the presence of mutual coupling and its theoretical performance analysis

AbstractThis article proposes a direct position determination (DPD) algorithm for non‐circular sources observed by a moving array using the self‐calibration technique in the presence of mutual coupling. The method first utilises the symmetric Toeplitz property of uniform linear array matrices with mutual coupling and cyclic Toeplitz property of uniform circular array coupling matrix, realising the decoupled estimations of target position parameters and sensor error parameters. Then the position parameters of multiple non‐circular are directly determined based on the subspace data fusion criterion in a decoupled manner, where the subspaces are obtained using the extended array data model with the non‐circular properties of the sources. This results in a significant improvement in the accuracy of the target position estimation and the number of distinguishable sources compared to the traditional mutual coupling calibration algorithm. In addition, the theoretical mean square error expression for the position estimations of the proposed algorithm under the influence of finite sampling is derived based on the matrix perturbation analysis theory, and the corresponding Cramér‐Rao bound is given. Finally, the correctness of the theoretical derivation and the superiority of the method is verified by simulation experiments.

A Direct Position Determination Method under Unknown Multi-Perturbation with Moving Distributed Arrays

Distributed array manifold perturbation, which includes synchronization errors, amplitude-phase errors, and path attenuation, has seriously degraded the accuracy of existing direct position determination (DPD) methods. In this paper, a DPD method under unknown multi-perturbation with moving distributed coprime arrays is advocated for. Firstly, by means of array position interchange, the integrated signals received from distributed arrays can be fused, which contributes to multi-position fusion. Subsequently, by resorting to the orthogonality between the noise subspace and steering vector received via distributed arrays, a quadratic optimization problem is constructed. Finally, we realize multi-parameter decoupling and achieve localization regardless of unknown perturbations. The superiority of the advocated method is substantiated from simulation examples.

Experimental Study on the Target–Receiver Formation Problem with the Exploitation of Coherent and Non-Coherent Bearing Information

Localization of emitting sources is a fundamental task in sonar applications. One of the most important factors that affect the localization performance is the sensor–target geometry. The sensor formation problem is usually addressed in related work assuming that the target is static and the location is known to a certain degree, but this is not the case for many underwater surveillance problems. In this paper, we deal with the target–receiver formation problem from a different perspective, and propose to investigate the effect of target–receiver geometry on localization performance by exploiting the spatial spectrum of the direct position determination (DPD) methods. For a given multi-array system, the transformation of geometrical patterns can be explicitly demonstrated as the target moves along the track. Meaningful characteristics of the DPD methods are obtained from the experimental results, where coherent and non-coherent bearing information is used and compared. The feasibility of the DPD approaches in the ocean environments is also investigated by comparing with a matched filter processing (MFP)-based multi-array processor in order to validate the credibility of the results in this paper.

A Modest Power Consumption Maximum Likelihood Direct Position Determination Approach for Multiple Targets With Moving Sensor Arrays

Target localization is widely applied in wireless sensor networks (WSNs). In this article, we tackle the problem of position estimation for multiple stationary targets using Doppler frequency shifts and angles by moving sensor arrays. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on Pincus’ theorem and the importance sampling (IS) concept, we propose a novel noniterative ML DPD method, and the circular mean is used for superior position estimation performance; meanwhile, a concrete criterion is also proposed for the choice of the importance function. 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. In addition, we present the closed-form Cramér–Rao lower bound (CRLB) for the DPD with moving sensor arrays and stationary targets. 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 CRLB, even in the adverse conditions of low signal-to-noise ratio (SNR) levels.