New algorithm for multitarget angle tracking

Dual polarization antennas (DPAs) are being developed to provide robust GNSS spoof detection and interference mitigation. These antennas show great potential in determining the azimuthal direction of arrival (DOA) of incoming GNSS signals with just a single GNSS patch antenna. With satellite signal DOA, we can test for and detect non-genuine or spoofed signal. Under nominal conditions, it can provide static heading with as few as one GNSS satellite. The Stanford DPA prototypes have demonstrated an ability to find DOA from both genuine and spoofed signals [1][2][3]. The DPA provides right hand circularly polarized (RHCP) and left hand circularly polarized (LHCP) signal components that can be used to determine signal DOA, detect spoofing, or mitigate interference through directional nulls. Improved versions of the Stanford DPA have the ability to support high rate measurements, operate in dynamic situations and improved spoof detection algorithm that can utilize other detection metrics. In this paper, we examine several means to improve performance and spoof detection with a DPA. It first discusses the design of the improved Stanford printed circuit board (PCB) based DPA [1] [4] to make is suitable flight trials. The design was modified to support high dynamics which means supporting a faster DOA determination capability than our first generation PCB DPA. Additionally, this DPA PCB supports a processing chains using a commercial off the shelf (COTS) GNSS receiver. Next, the paper examines the dynamic performance of the COTS GNSS DPA in flight test. It quantifies the factors that affect DOA performance such as satellite used, elevation angle and other factors. Accurate knowledge and incorporation of these factors can improve our DOA based spoof detection algorithm. It also examines performance in different flights conditions such as nominal and interfered. We will examine the resulting performance of the signal for spoof detection using algorithms developed to utilize DOA along with other detection metrics, specifically pseudo range residuals (prr). Finally, we examine the improvement in spoof detection if we are able to resolve the 180-degree ambiguity of DPA DOA measurements. Our analysis shows that resolving the ambiguity can greatly strengthen our confidence of spoof detection.

In this paper, we propose a maximum-likelihood online diarization method based on a probabilistic spatial dictionary. This dictionary consists of the given probability distribution of spatial features for each possible direction of arrival (DOA) of source signals. Recently, we have developed an online, noise-robust diarization method by utilizing this dictionary as spatial prior information. In this method, DOA estimation is first performed frame-wise based on the dictionary, and subsequently diarization is performed. Although the DOA estimation is performed optimally in the maximum-likelihood sense, the diarization is performed suboptimally based on some heuristics. In contrast, the proposed method performs DOA estimation and diarization jointly and optimally in the maximum-likelihood sense. This is realized by introducing a categorical mixture model (CMM), which has source-wise DOA information and diarization information as unknown parameters. We conducted an experiment on a real-world meeting dataset, and confirmed that the proposed method reduced a diarization error rate by absolute 2.7% compared to the above conventional method.

In this paper, the direction of arrival (DOA) and adaptive antenna array beamforming for the smart antenna applications are realized by using Minimum Variance Distortionless Response (MVDR) algorithm of DOA estimation. The MVDR supported by joint approximate diagonalization of eigenmatrices (JADE) algorithm is used to estimate the direction of arrivals of coherent signals in non-coherent groups. The generalized steering vectors using JADE algorithm are first estimated, and then the spectrum methods are realized to estimate the DOA of each signal. Root mean square error (RMSE) values are computed in different scenarios for the proposed method as well as JADE-MUSIC and the corresponding results are compared. Although both methods resolved the signals correctly, simulation results show that JADE based MVDR has better performances in the cases of low number of antenna elements and snapshots. The DOAs obtained are then processed using LMS adaptive beamforming algorithm to steer the main lobe of the radiation pattern to desired angles (signals) and the nulls to undesired angles (signals). The simulation results also reveal that adaptive beamforming is successfully done by annihilating the effects of undesired signals significantly.

In most wireless communication systems, two-dimensional directions of arrival (DOA) of multipath signals need to be found for spatial selective transmission. In this paper, we propose a new algorithm to estimate 2-D DOA of multiple narrow-band signals. A DOA cyclic matrix is constructed whose eigenvalues and eigenvectors can be simultaneously used to extract 2-D DOA without 2-D searches. By exploiting the temporal property of cyclostationarity, the signal detection capability is significantly improves. Besides, based on the decorrelation model for mobile terminal signals, the algorithm can be effectively extended to the coherent case without spatial smoothing and the loss of array aperture. Simulation results are given to illustrate the performance of the new algorithm.

Adaptive beamforming in antenna arrays aims at adjusting the weighted linear combination of the output signals provided by the antennas so that the power of the received signals at dominant paths is maximized at the same time that the power of interference and noise signals is minimized. The weight vectors, each one associated with one received signal can be directly obtained if the direction of arrival (DOA) of the corresponding signal has already been estimated. The process of DOA estimation involves the prediction of the angle of arrival by means of monitoring the output produced by the antennas in the array, given that the number of antennas is higher than the number of signals to be detected. Even though signal subspace techniques have made a good job in DOA estimation, they present some important drawbacks that are alleviated here using a supervised learning approach, in the form of a multiclass LS-SVM classification problem. The main contribution of this paper is twofold: a step-by-step description of the complete set of algebraic manipulation for data preprocessing and for the synthesis of the classification device, and an analysis of the effect in performance when relevant parameters vary in a given operational interval.

In this paper, an idea from the quasi-maximum-likelihood (QML) algorithm has been applied to estimation of the direction of arrival (DOA) and parameters of the polynomial phase signals (PPS) impinging on the uniform linear array. The algorithm uses a search over a set of possible DOA values. For considered value, the DOA is compensated and resulted signal parameters are estimated using the original QML approach. In this way, several sets of potential estimates are obtained. Optimal DOA and PPS parameters belong to set that maximizes the proposed cost function. In the proposed approach, parameters are estimated using 2-D search over DOA and window widths used for calculation of the short Fourier transform in the QML that is an important advantage with respect to existing strategies like, for example, the polynomial beamformer requiring multi-dimensional or metaheuristic search strategies. Numerical validation of the proposed approach for both mono- and multi-component signals is provided in the paper. Both far- and near-field scenarios are considered.

It is interesting to determine the direction of arrival (DOA) of signals in the presence of impulsive noise for monostatic multiple-input multiple-output (MIMO) radar. Inspired by the robust statistics property of M-estimation theory and maximum likelihood estimation theory, a Cauchy score cost function is proposed to suppress the outliers, leading to robust DOA estimation. Firstly, the Cauchy score cost function for residual fitting error matrix of the radar data is derived. Then, an alternating convex optimisation algorithm based on the complex-valued Newton gradient descent is tailored to acquire an efficient solution for the Cauchy score cost function. Finally, using the signal subspace matrix obtained by the alternating convex optimisation algorithm, ESPRIT algorithm is adopted to provide a closed-form DOA estimations. Simulation results are presented to demonstrate the effectiveness of the proposed method.

We present a cost-effective hybrid analog digital system to estimate the Direction of Arrival (DoA) of WiFi signals. The processing in the analog domain is based on simple well-known RADAR amplitude monopulse antenna techniques. Then, using the received signal strength indicator (RSSI) delivered by a commercial MiMo WiFi cards, the DoA is estimated using the so-called digital monopulse function. Due to the hybrid analog digital architecture, the digital processing is extremely simple, so that DoA estimation is performed without using IQ data from specific hardware. The simplicity and robustness of the proposed hybrid analog digital MiMo architecture is demonstrated for the ISM 2.45 GHz WiFi band. Also, the limitations with respect to multipath effects are studied in detail. As a proof of concept, an array of two MiMo WiFi DoA monopulse readers is distributed to localize the two-dimensional position of WiFi devices. This cost-effective hybrid solution can be applied to all WiFi standards and other Internet of Things narrowband radio protocols, such as Bluetooth Low Energy or Zigbee.

With the dramatic advances in mobile communication technologies, the problem of estimating the direction of arrival (DOA) of signals generated by several sources becomes even more important. High‐resolution estimation methods based on eigenvalue decomposition include the minimum‐norm method and the MUSIC method. Since the peak of the angular spectrum must be scanned over all directions, the computational effort is enormous. In this paper, a DOA estimation method using a database is proposed. In this method, a database is constructed in which the eigenvector corresponding to the minimum eigenvalue is the key and the DOA is the associated value. The DOA is estimated by seeking in the database the data with the key closest to the eigenvector obtained from the observation signals. In the proposed method, DOA estimation can be achieved by only a regional search of the database, and hence the process is extremely fast. Finally, the effectiveness of the proposed method is demonstrated by simulation. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(3): 48–56, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20251

Independent vector analysis (IVA) minimizes an objective function to estimate separation filters that separate mixture signals into individual source signals. Unfortunately, IVA often suffers from the well-known block permutation problem. To mitigate that problem, the use of geometry knowledge has been studied, but two crucial issues remain: the necessity of non-differential processes outside the minimization and of high-level geometrical clues such as the directions of arrival (DOAs) of the source signals. This paper thus presents a novel IVA method whose objective function has a differentiable max-directivity beamforming normalization (MDBN) term. This term uses geometry knowledge from only a low-level geometrical clue (the positions of the microphone array) via the traditional beamforming (BF) concept that each separation filter should have a maximum gain for each specific DOA across all frequency bins. Thus, our overall objective function can be minimized by gradient descent, and the MDBN term encourages the separation filters to focus on specific directions, which implicitly estimates the most reasonable DOAs of the source signals at each iteration. Therefore, our method uses geometry knowledge while avoiding the above two issues and estimates good separation filters mitigating the permutation problem. Several experiments show that our method outperforms the conventional BF and IVA methods.

An algorithm is proposed for estimating the directions of arrival (DOAs) of multipath signals received from multiple users on the uplink of a code-division multiple-access system. The correlation matrices of the received signal before and after code-matched filtering are used to provide unique estimates even when the number of required DOAs exceeds the number of antenna array elements. This scenario is well known to cause conventional direction-finding algorithms (such as MUSIC) to fail. Both intersymbol interference (ISI) and multiple-access interference (MAI) are modeled exactly, and so the algorithm performs much better than those which model ISI and MAI as Gaussian noise.

This article proposes a direction of arrivals (DOAs) estimator based on magnitude-only samples with partly calibrated sensors array. First, the difference of DOAs (DDOAs) is obtained from magnitude-only array output. Then, the reference DOA is obtained from perfectly calibrated subarray via combination search. After that, DOAs can be estimated by the reference DOA and DDOAs. For partly calibrated sensors array, the proposed DOA estimator is robust to interchannel mismatch in time. Besides, the complexity of channel calibration is unvaried when array aperture increases. Numerical examples illustrate the advantages of the proposed estimator.

The compressive array method, where a compression matrix is designed to reduce the dimension of the received signal vector, is an effective solution to obtain high estimation performance with low system complexity. While sparse arrays are often used to obtain higher degrees of freedom (DOFs), in this paper, an orthogonal dipole sparse array structure exploiting compressive measurements is proposed to estimate the direction of arrival (DOA) and polarization signal parameters jointly. Based on the proposed structure, we also propose an estimation algorithm using the compressed sensing (CS) method, where the DOAs are accurately estimated by the CS algorithm and the polarization parameters are obtained via the least‐square method exploiting the previously estimated DOAs. Furthermore, the performance of the estimation of DOA and polarization parameters is explicitly discussed through the Cramér‐Rao bound (CRB). The CRB expression for elevation angle and auxiliary polarization angle is derived to reveal the limit of estimation performance mathematically. The difference between the results given in this paper and the CRB results of other polarized reception structures is mainly due to the use of the compression matrix. Simulation results verify that, compared with the uncompressed structure, the proposed structure can achieve higher estimated performance with a given number of channels.

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

An algorithm to improve direction of arrival (DOA) estimation accuracy with an extended sensor array in the presence of multiple coherent signal sources is proposed. The algorithm uses virtual element theory to extend the sensor array, estimates virtual element information via linear prediction and expands the array aperture in practical sense; the sparsity of the target orientation in angle space is exploited to establish an over-complete dictionary and a reception model for the array signal in sparse space; the received array data is preprocessed using singular value decomposition (SVD) method and target DOA estimation is realized by calculating the best atoms. The algorithm improves DOA estimation accuracy by extended array and uses SVD to control computational complexity effectively, which ensures the accuracy and efficiency. Computer simulation shows that the proposed algorithm is able to accurately estimate the DOA for both single-target and closely spaced multi-target cases in low signal-to-noise ratio environments, and also has excellent DOA estimation performance in the presence of multiple coherent signal sources.