DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques.

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“…The estimated DoAs are denoted as

and

, respectively. The two targets are said to be successfully resolved when the following equation is satisfied [ 66 , 67 , 68 ]

…”

Section: Simulation Resultsmentioning

confidence: 99%

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Hamid

Wyne

Butt

2023

Sensors

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“…e training sets and validation sets of the three RBF networks are the same as those of the proposed method. e spread of the training sets is searched in the range of [1,10], and the desired MSE is searched in the range of [0, 5] to obtain the optimal performance of the validation sets.…”

Section: Effect Of the Snr And Snapshot Numbermentioning

confidence: 99%

“…Direction-of-arrival (DOA) estimation, as one of the crucial technologies in antenna array systems, has been widely applied in many fields, such as sonar, seismology, radar, and mobile communication [1][2][3]. e multiple signal classification (MUSIC) [4,5] and estimation of signal parameter via rotational invariance technique (ESPRIT) [6,7] are two classic conventional one-dimensional (1D) DOA estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Chen

Joo

2021

International Journal of Antennas and Propagation

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This paper presents a novel efficient high-resolution two-dimensional direction-of-arrival (2D DOA) estimation method for uniform circular arrays (UCA) using convolutional neural networks. The proposed 2D DOA neural network in the single source scenario consists of two levels. At the first level, a classification network is used to classify the observation region into two subregions (0°, 180°) and (180°, 360°) according to the azimuth angle degree. The second level consists of two parallel DOA networks, which correspond to the two subregions, respectively. The input of the 2D DOA neural network is the preprocessed UCA covariance matrix, and its outputs are the estimated elevation angle to be modified by postprocessing and the estimated azimuth angle. The purpose of the postprocessing is to enhance the proposed method’s robustness to the incident signal frequency. Moreover, in the inevitable array imperfections scenario, we also achieve 2D DOA estimation via transfer learning. Besides, although the proposed 2D DOA neural network can only process one source at a time, we adopt a simple strategy that enables the proposed method to estimate the 2D DOA of multiple sources in turn. Finally, comprehensive simulations demonstrate that the proposed method is effective in computation speed, accuracy, and robustness to the incident signal frequency and that transfer learning could significantly reduce the amount of required training data in the case of array imperfections.

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“…It is a challenge to estimate the time-delay for the bandwidth limitation of AM radio signal; hence, estimating the direction-of-arrival (DOA) of scattered wave is crucial to the AM radio-based PBR [6]. In our previous research, we developed a weak signal enhancement approach [7] and proposed a range-Doppler domain array signal processing (RD-ASP) method [8,9], which can estimate the DOA of scattered wave with high resolution and suppress the clutter simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

et al. 2021

International Journal of Antennas and Propagation

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In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.

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DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

Cited by 5 publications

References 30 publications

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Novel Approach to 2D DOA Estimation for Uniform Circular Arrays Using Convolutional Neural Networks

Track-Before-Detect Procedures in AM Radio-Based Passive Radar

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