The issue of asset tracking in dense environ-ments where the performance of the Global Positioning System (GPS) becomes unavailable or unreliable is addressed. The proposed solution uses a low-profile array of antenna elements (sensors) mounted on a finite conducting ground. A compact-size sensor array of six electrically-small dual-band omnidirec-tional spiral antenna elements was designed as a front end of a tracker to operate in the 402 and 837 MHz spectrum bands. For the lower band, a three-element superposition method is applied to support estimation of the Angle of Arrival (AOA), whereas all six sensors are employed for the higher band. A low complexity and accurate AOA determination algorithm is proposed, the Projection Vector (PV), and this is combined with the array mentioned. Orthogonal Frequency Division Multiplexing (OFDM) is integrated with the PV technique to increase the estimation resolution. The system was found to be suitable for installation on the roof of vehicles to localize the position of assets. The proposed system was tested for tracking of non-stationary sources, and then two scenarios were investi-gated using propagation modeling software: outdoor to out-door and outdoor to indoor. The results confirm that the pro-posed tracking system works efficiently with a single snapshot.

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.

Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M–MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers.

This manuscript firstly proposes a reduced size, low-complexity Angle of Arrival (AoA) approach, called Reduced Uniform Projection Matrix (RUPM). The RUPM method applies a Uniform Sampling Matrix (USM) criterion to sample certain columns from the obtained covariance matrix in order to efficiently find the directions of the incident signals on an antenna array. The USM methodology is applied to reduce the dependency between the adjacent sampled columns within a covariance matrix; then, the sampled matrix is used to construct the projection matrix. The size of the obtained projection matrix is reduced to minimise the computational complexity in the searching grid stage. A theoretical analysis is presented to demonstrate that the USM methodology can increase the Degrees of Freedom (DOFs) with the same aperture size and number of sampled columns compared to the classical sampling criterion. Then, a polynomial root is constructed as an alternative efficient computational solution of the UPM method in a one-dimensional (1D) array spectrum peak searching problem. It is found that this distribution increases the number of produced nulls and enhances noise immunity. The advantage of the RUPM method is that it is appropriate to apply for any array configuration while the Root-UPM offers better estimation accuracy with less execution time under a uniform linear array condition. A computer simulation based on various scenarios is performed to demonstrate the theoretical claims. The proposed direction-finding methods are compared with several AoA methods in terms of the required execution time, Signal-to-Noise Ratio (SNR) and different numbers of data measurements. The results verify that the new methods can achieve significantly better performance with reduced computational demands.

Several works show that the linear Angle of Arrival (AoA) methods such as Projection Matrix (PM) have low computational complexity compared to the subspace methods. Although the PM method is classified as a subspace method, it does not need decomposition of the measured matrix. This work investigates the effect of the sampled columns within the covariance matrix on the projection matrix construction. To the authors’ knowledge, this investigation has not been addressed in the literature. Unlike the subspace methods such as Multiple Signal Classification (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Minimum Norm, Propagator, etc., which have to use a specific number of columns, we demonstrate this aspect is not applicable in the PM method. To this end, the projection matrix is formed based on a various number of sampled columns to estimate the arrival angles. A theoretical analysis is accomplished to illustrate the relationship between the number of the sampled columns and the degrees of freedom (DOFs). The analysis shows that with the same aperture size, the DOFs can be increased by increasing only the number of sampled columns in the projection matrix calculation step. An intensive Monte Carlo simulation for different scenarios is presented to validate the theoretical claims. The estimation accuracy of the PM method, based on the proposed selected sampling methodology outperforms all the other techniques with less complexity compared to the Capon and MUSIC methods. The estimation accuracy is evaluated in terms of Root Mean Square Error (RMSE) and the Probability of Successful Detection (PSD). The results are presented and discussed.

Direction of arrival (DOA) estimation is currently an active research topic playing a fundamental role in many applications including electromagnetic, tracking systems and signal processing applications. This paper evaluates the performance estimation of Pisarenko Harmonic Decomposition (PHD) algorithm and then compares it with multiple signal classification (MUSIC) method based on narrowband signal conditions. The main purpose of this evaluation is to investigate the effect of the spurious peaks (spurious angles) that appears on the pseudospectrum of the PHD on the estimation precision. The principle working in addition to mathematical model of the DOA model and PHD method are presented. An exhaustively Monte Carlo simulation is performed to explain this problem. A useful recommendation is provided to overcome this problem.