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In this paper, we present a comprehensive investigation into improving Direction of Arrival (DOA) estimation for gamma-emitting isotopes using deep neural networks. The direction of arrival estimation is most valuable for Home Land Security (HLS) applications or increased safety in Decontamination and Decommissioning (D&D). Traditional methods, such as beamforming (BF), have limitations in accuracy and sensitivity to noise and background variations. In recent years, data-driven approaches utilizing deep neural networks, including Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) models, have shown promise in enhancing DOA estimation. By considering the full energy spectrum and augmenting recorded data, our neural network models outperform traditional BF methods and exhibit greater resilience in diverse background scenarios. The 2-layer CNN model, in particular, achieves up to 40% improvement in estimation accuracy. Our research provides a reliable and data-driven approach for precise DOA estimation with potential applications in nuclear security and safety in D&D. [ABSTRACT FROM AUTHOR]

Design teams are increasingly looking for design flows that can rapidly lead to high performance and low power implementation of DSP algorithms. Model-based design can satisfy this requirement, but it must be (1) coupled with efficient high-level synthesis support in order to provide good Quality of Results, and (2) controlled to derive the desired area/performance/throughput trade-off. We present a semi-automatic design flow for rapid high level synthesis-based hardware design space exploration starting from Simulink digital signal processing models. We illustrate our flow with a realistically complex signal processing algorithm for estimating the direction of arrival of a sound source. We show how one can start from a functionally validated fixed point model in Simulink and then go through a relatively simple design flow for hardware synthesis and automatic design space exploration, obtaining a very efficient hardware implementation that is competitive with the RTL implementation generated by another commercial model-based design tool. [ABSTRACT FROM PUBLISHER]

To enhance the passive direction-of-arrival (DOA) estimation performance of a volumetric array in high noise and strong interference background, a new DOA estimation method based on Fourier integral method (FIM) and inverse beamforming (IBF) is proposed. This method combines the two methods and thus obtains the interference cancelling capability of IBF and the noise suppression ability of FIM simultaneously. Furthermore, the proposed method is generalized to be feasible for arbitrary volumetric array and also achieves the same robustness as the conventional beamforming (CBF). With theoretical analysis and computer simulations, it is shown that with the proposed method, the volumetric array can obtain better DOA estimation results in low signal-to-ratio (SNR) condition as well as in the presence of interferences, when compared to the conventional CBF method. [ABSTRACT FROM PUBLISHER]

This paper proposes a linear estimation theory based direction of arrival (DOA) estimator to guarantee high-performance and computational efficiency. To do this, state-space system is derived from the linear prediction relation of the sinusoidal acoustic signal. Since it contains uncertain measurement matrix, the recently developed non-conservative robust Kalman filter (NCRKF) can be applied to compensate the performance degradation by the uncertain measurement matrix. However, unfortunately, the statistical information used in NCRKF scheme may not be precise in actual situation and it leads to the performance degradation. Therefore, in this paper, constrained NCRKF (CNCRKF) is presented to develop practical DOA estimator. It adopts constraint condition derived from the relation between target states to solve the performance degradation problem by the incorrect statistical information. The performance of the proposed solution is demonstrated by the computer simulation. [ABSTRACT FROM PUBLISHER]

In this paper, a new direction of arrival (DOA) estimation approach is addressed for the case of more sources than physical receiving antennas by considering a novel nonuniform array design. The new design utilizes the concept of minimum sparse rulers which are rulers having incomplete marks. The differences between marks in a sparse ruler cover all lags of the autocorrelation. In array processing, this set of differences can be used as a basis to construct a virtual uniform linear array having a higher number of antennas than the actual linear array. In order to attain the required rank condition of the observation matrix, the most recent spatial smoothing method is used. The MUSIC algorithm can then be applied leading to the desired high resolution result. It is also possible to compromise the resolution for a lower complexity level by exploiting the least-squares approach to generate the angular spectrum. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the direction of arrival (DOA) estimation problem under the coexistence of noncircular and circular signals. By exploiting the difference between the circularity of noncircular and circular signals, a method is proposed, which estimates the DOAs of noncircular and circular signals separately. The maximum number of detectable directions by the proposed method is twice that by the MUSIC method. Furthermore, since the proposed method resolves noncircular and circular signals based on the circularity difference rather than the DOA difference, the proposed method performs well regardless of the DOA separation between noncircular and circular signals. Simulation results illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In certain applications involving direction finding, a priori knowledge of a subset of the directions to be estimated is sometimes available. Existing knowledge-aided (KA) methods apply projection and polynomial rooting techniques to exploit this information in order to improve the estimation accuracy of the unknown signal directions. In this paper, a new strategy for incorporating prior knowledge is developed for situations with a low signal-to-noise ratio (SNR) and a limited data record based on the Unitary ESPRIT algorithm. The proposed KA-Unitary ESPRIT algorithm processes an enhanced covariance matrix estimate obtained by applying a shrinkage covariance estimator, which linearly combines the sample covariance matrix and an a priori known covariance matrix in an automatic fashion. Simulations show that the derived algorithm achieves significant performance gains in estimating the unknown sources and additionally provides a high robustness in the case of inaccurate prior knowledge. [ABSTRACT FROM PUBLISHER]

In this paper, a 3-D Luneburg Lens is employed for direction finding application. Using the special property of a Luneburg lens that every point on the surface of the Lens is the focal point of a plane wave incident from the opposite side, a number of detectors are mounted around the surface of the lens to estimate the direction of arrival (DOA) of a microwave signal. To demonstrate the proposed direction finding system, a Luneburg lens with five detectors mounted on its surface to receive the signal from −20° to 20° is measured at 5.6 GHz. The initial direction finding results using a correlation algorithm show that the estimated error is smaller than 2° within −15° to 15° incident angles. [ABSTRACT FROM PUBLISHER]

This paper presents an experimental platform for Multiple-Input Multiple-Output (MIMO) radar. Some detection techniques which have been recently proposed, such as Capon and the Generalized Likelihood Ratio Test (GLRT), will be tested in order to analyze their performance in real conditions. [ABSTRACT FROM PUBLISHER]