Unambiguous Estimation Research Articles

Radar-Based UAV Swarm Surveillance Based on a Two-Stage Wave Path Difference Estimation Method

The numerous applications of unmanned aerial vehicles (UAVs) have caused them to receive growing interest worldwide. However, misused UAVs have caused significant public safety and privacy concerns. Therefore, developing an anti-UAV surveillance system is of paramount importance. In this paper, we propose a radar-based UAV swarm surveillance approach. A frequency estimation-based approach is used to obtain a coarse wave path difference estimate. Then, by combining the coarse estimate from the first stage with an estimate from the phase comparison method, a fine and unambiguous estimate of the wave path difference is derived. Through the solution of a system of nonlinear equations regarding the measured range and wave path difference, the proposed method is able to provide an unambiguous three-dimensional (3D) position estimate for a given UAV swarm. Once such 3D positions have been collected throughout an observation period, system tracks of the UAV swarm can be formed. With these tracks, a target object map (TOM) can be generated and handed over to an interceptor to counter the UAV swarm. Theoretical analyses of the accuracy of the estimation results and the restrictions of the proposed method are presented in detail. Several simulations are also carried out to evaluate the performance of the proposed method.

DOA estimation based on density-tapered thinned array

In this paper, several classical DOA estimation methods are applied to density-tapered thinned array. The density-tapered thinned array has the advantage that there is no grating lobe in the whole angle scanning range, and the unambiguous angle estimation of the target is obtained. The experimental results show that the classical DOA estimation method can be applied directly to the density-tapered thinned array to obtain satisfactory results, which lays a foundation for the practical application of the density-tapered thinned array. In addition, the effects of sparsity and number of elements on DOA estimation are analyzed, and the advantages of density-tapered thinned array over uniform arrays are verified.

Evaluation of pore size distribution via fluid-fluid displacement porosimetry: The viscous bias

Fluid-fluid displacement porosimetry (FFDP) is a method for determining the pore size distribution of a porous medium from viscous fluid-fluid immiscible displacement (drainage) results, using a simplistic representation of the pore structure by cylindrical parallel tubes. The method leads to unambiguous estimates when the porous medium microstructure is strongly anisotropic and reasonably akin to this representation. The information obtained from this technique is however less straightforward to interpret for more complex microstructures. On the basis of synthetic data obtained from pore network simulations, it is evidenced, in particular, that this method leads to a shift in the distribution toward unrealistic small pore sizes. The shift, that may be very significant, is due to the viscous pressure drop in the displacing fluid. It is shown that significant improvement is achieved when the characterization of the pore size distribution is considered as an inverse optimization problem. The latter is solved using an optimization method presented in a previous work that was restricted to situations where viscous effects are unimportant.

2D Direction of Arrival Estimation Using Uniform Circular Arrays With Radiation Pattern Reconfigurable Antennas

This paper focuses on direction of arrival (DoA) estimation using adaptive arrays that consist of radiation pattern reconfigurable antenna (RPRA) elements for 2D direction finding (i.e., azimuth and elevation DoA estimation). In particular, uniform circular arrays (UCAs) are explored that use RPRA elements with two different elevation radiation pattern states to achieve unambiguous estimates over all possible incident angles. Theoretical cardioid-type directional patterns are investigated to determine the pattern states that minimize overall DoA estimation error, and the performance of 3-, 4-, and 5-element RPRA UCAs is compared with baseline theoretical arrays composed of isotropic elements. The results demonstrate that RPRA UCAs with optimized cardioid patterns can achieve similar accuracy to the baseline arrays, but with fewer antennas/front-ends. For example, a 4-element RPRA UCA can achieve approximately the same root mean square error (RMSE) as a 6-element uniform spherical array composed of isotropic elements. Furthermore, unambiguous estimates can be achieved over all incident angles with electrically large UCA radii using optimized RPRA elements, which can further improve accuracy and increase bandwidth. To demonstrate the feasibility of the technique, a practical RPRA was designed at 6 GHz with a pattern that approximates the optimized cardioid, and the performance meets or exceeds the theoretical pattern performance for RMSEs less than 3°.

2D-DOD and 2D-DOA Estimation Using Sparse L-Shaped EMVS-MIMO Radar

It is well-known that EMVS-MIMO radar is an emerging technique that it allows for 2D-DOD and 2D-DOA estimation. Unfortunately, existing array geometries on the EMVS-MIMO radar can rarely reach a good compromise between the estimation accuracy and the computational burden. This paper is aimed at proposing an L-shaped sparse array topology for a bistatic EMVS-MIMO radar, whose inter-element distance is much larger than half-wavelength. A fast algorithm proposed herein to estimate the 2D-DOD and 2D-DOA. Firstly, the direction cosine estimates are obtained via the rotational invariance properties of the sparse subarrays, which are ambiguous yet exhibit high-resolution. Thereafter, the direction cosine estimates are achieved via the vector cross-product of the normalized Poynting vectors, which are unambiguous but have low-resolution. The unambiguous high-resolution direction cosine estimates are determined by combining the previous results, following which the 2D-DOD and 2D-DOA can be easily recovered. It is shown that the proposed framework can obtain a better accuracy of estimation than the other existing methods. Moreover, it is more flexible than the current sparse array methodologies. Finally, the theoretical derivations have been validated by simulation results.

An Unambiguous Doa Estimation Algorithm by a Large-Space Separated Nested Array for Reducing Mutual Coupling

An Unambiguous Doa Estimation Algorithm by a Large-Space Separated Nested Array for Reducing Mutual Coupling

A high-precision 2D DOA estimation algorithm by a large-space three-parallel array for reducing mutual coupling

In this paper, a high-precision two-dimensional (2D) direction of arrival (DOA) estimation algorithm by three parallel uniform linear arrays (ULAs) is proposed. Both the internal spacing of each sub-array and the spacing between two adjacent sub-arrays can exceed the half-wavelength of incident signal. An extended propagator method (PM) algorithm is introduced to obtain a multiple extended propagator matrix by processing the covariance matrix of received signals. Then, by using the extended propagator matrix, unambiguous and high-precision 2D DOA estimation can be obtained without extra pairing process. For the proposed array construction, mutual coupling effect can be ignored as long as the element interval is large enough. The extended PM algorithm based on the proposed array has far higher precision than the existing similar algorithms, which can be proved by some simulation experiments.

Subpulse Processing for Unambiguous Doppler Estimation in Pulse-Doppler Noise Radars

The Doppler dilemma, an inherent dichotomy between range and velocity estimation in pulse-Doppler radars, has been the subject of many works in literature. Classical approaches for solving its intrinsic ambiguities usually rely on the subdivision of the coherent processing interval into multiple bursts of different pulse repetition frequency (PRF). Besides some additional complexity related to the PRF selection and the extraction of the unambiguous measurements, these techniques reduce the coherent gain, introducing losses that, in some cases, can be in the order of several decibels. Within this context, this article presents a novel velocity estimation scheme, based on a subpulse processing architecture and random waveforms. It is shown that, with the proper selection of system parameters, the subpulse-to-subpulse phase shift can be evaluated and used to extend the unambiguous measurements as far as needed. The algorithm performance has been evaluated through statistical simulations, and its complexity addressed with a real-time implementation. The proposed architecture was also verified considering different noise waveforms, including sidelobe optimized ones. The results have shown that, besides extending the unambiguous measurements, and improving the Doppler tolerance, the subpulse processing has also increased the system robustness to the Doppler mismatch effects, reducing sidelobes, and thus, extending its applicability.

Performance Evaluation and Optimization of MIMO Radars Using Biomimetic Antenna Arrays

The improvement of the angular resolution of radar sensors is one of the crucial goals of current radar research. A promising approach to achieve this goal is inspired by the ears of a fly called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ormia ochracea</i> . The working principle was adapted for antennas, and the so-called biomimetic antenna arrays (BMAAs) aroused the interest of several research groups. In this work, BMAAs are incorporated into multiple-input multiple-output (MIMO) arrays, a very common approach of improving the angular resolution, to gain more degrees of freedom in array design. The MIMO BMAAs are modeled utilizing the effective biomimetic antenna distance, a fundamentally new measure introduced in this article to translate the special biomimetic phase progression into a spatial quantity. We present straightforward antenna configurations but also describe how a genetic algorithm can be utilized to optimize both antenna positions and BMAA parameters. The proposed arrays show various beneficial effects such as a wider angular range for unambiguous angle estimation or a narrower beamwidth. The impact of MIMO BMAAs on the angular resolution is thoroughly analyzed both theoretically and by radar measurements in the range of 77 GHz. The measurements confirm the modeling method very well and show a significant increase in the angular separability.

Wien’s Displacement Law and Blackbody Radiation Quartiles

The conventional approach to teaching blackbody radiation in introductory modern physics courses relies on an oversimplified explanation of Wien’s displacement law, which does not uphold students’ conceptual understanding of Planck’s law and can lead to well-known misconceptions. Thus, there is a need to clarify the pedagogical role of this concept, while trying to make it more useful for making unambiguous estimates of spectral distributions of blackbodies. This necessarily requires the use of displacement laws, which are independent from the spectral scale of choice.

Super Resolution Wide Aperture Automotive Radar

State-of-the-art automotive radars have an angular resolution of 1°, which is insufficient for estimating the objects shape and boundaries at long distance. In this paper, we design a novel automotive radar with 1m aperture, which is an order of magnitude larger aperture than state-of-the-art automotive radars, and thus achieves super high angular resolution of 0.1°. The radar is designed to overcomes the major technical challenges of a wide aperture radar, which are: non-robust phase coherency over a wide aperture, spanning a wide aperture with sparse antenna elements, simultaneous non-interfering transmissions from a relatively large number of transmit antennas with unambiguous target speed estimation, and high complexity of near-field beamforming. We demonstrate the super high resolution performance of the radar in automotive scenarios, and show that it attains comparable results to a high resolution LIDAR at short range, and outperforms the LIDAR at long ranges. The wide aperture radar has the potential to enable autonomous driving at higher speed, and also to increase the operation of autonomous driving to more regions and climates.

Estimation of the Radial Scale Length and Vertical Scale Height of the Galactic Thin Disk from Cepheids

Up-to-date data on 2214 classical Cepheids have been used. The model of an exponential matter density distribution in the Galactic thin disk has been applied. New estimates of the radial disk scale length $h_R,$ the distance of the Sun from the symmetry plane $z_\odot$, and the vertical disk scale height $h_z$ have been obtained. Based on samples of 1087 Cepheids younger than 120 Myr and 1127 Cepheids older than 120 Myr, we have found $h_R=2.30\pm0.09$ kpc and $h_R=1.96\pm0.12$ kpc, respectively. It has turned out that an unambiguous estimate of $h_R$ cannot be obtained from their overall radial distribution. However, we have constructed the radial distribution of 806 Cepheids of different ages at the time of their birth from which we have found $h_R=2.36\pm0.24$ kpc. We show that $h_z$ depends strongly on the age of stars. For example, from 705 Cepheids younger than 120 Myr with heliocentric distances $r<6$ kpc we have found $z_\odot=-17\pm4$ pc and $h_z=75\pm5$ pc. From 393 Cepheids older than 120 Myr at $r<6$ kpc, we have found $z_\odot=-39\pm11$ pc and $h_z=131\pm10$ pc.

Estimating Remaining Carbon Budgets Using Temperature Responses Informed by CMIP6

A remaining carbon budget (RCB) estimates how much CO2 we can emit and still reach a specific temperature target. The RCB concept is attractive since it easily communicates to the public and policymakers, but RCBs are also subject to uncertainties. The expected warming levels for a given carbon budget has a wide uncertainty range, which increases with less ambitious targets, i.e., with higher CO2 emissions and temperatures. Leading causes of RCB uncertainty are the future non-CO2 emissions, Earth system feedbacks, and the spread in the climate sensitivity among climate models. The latter is investigated in this paper, using a simple carbon cycle model and emulators of the temperature responses of the Earth System Models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Driving 41 CMIP6 emulators with 127 different emission scenarios for the 21st century, we find almost perfect linear relationship between maximum global surface air temperature and cumulative carbon emissions, allowing unambiguous estimates of RCB for each CMIP6 model. The range of these estimates over the model ensemble is a measure of the uncertainty in the RCB arising from the range in climate sensitivity over this ensemble, and it is suggested that observational constraints imposed on the transient climate response in the model ensemble can reduce uncertainty in RCB estimates.

Joint Doppler Frequency and Direction of Arrival Estimation for TDM MIMO Automotive Radars

This paper considers a novel approach to joint estimation of the velocity (or Doppler frequency) and Direction of Arrival (DOA) of targets by time division multiplexing (TDM) multiple-input multiple-output (MIMO) radars in automotive applications. The TDM MIMO radars create orthogonal probing signals (in time domain) by allocating a transmitter to a separate time slot. In this paper, we consider a standard TDM MIMO radar to be the one where transmitters are activated sequentially according to their natural spatial order. The drawback of the standard TDM MIMO approach is the coupling of velocity and DOA information of the targets. The coupling reduces the unambiguous estimation interval of the Doppler frequencies of the targets by the number of transmit antennas being multiplexed. In this paper, we propose a novel cost function to jointly estimate both the Doppler frequency and the DOA of each target by reinstating the reduced unambiguous Doppler spectrum interval. Our approach is generally applicable to solve the Doppler ambiguity problems associated with any colocated standard TDM MIMO radar. To confirm this, we present the simulation results that evaluate the performances of two widely deployed TDM MIMO radar antenna configurations: non-overlapped and overlapped arrays. Moreover, we derive the analytical expressions to study the statistical performance of the proposed method. The simulation examples are presented to verify the performance of the proposed method as well as the accuracy of the analytical expressions.

A Propagator Method for Bistatic Coprime EMVS-MIMO Radar

In this paper, a novel two-dimensional (2D) direction-of-departure (DOD) and 2D direction-of-arrival (DOA) estimate algorithm is proposed for bistatic multiple-input multiple-output (MIMO) radar system equipped with coprime electromagnetic vector sensors (EMVS) arrays. Firstly, we construct the propagator to obtain the signal subspace. Then, the ambiguous angles are estimated by using rotation invariant technique. Based on the characteristic of coprime array, the unambiguous angles estimation is achieved. Finally, all azimuth angles estimation is followed via vector cross product. Compared to the existing uniform linear array, coprime MIMO radar is occupying large array aperture, and the proposed algorithm does not need to obtain signal subspace by eigendecomposition. In contrast to the state-of-the-art algorithms, the proposed algorithm shows better estimation performance and simpler computation performance. The proposed algorithm’s effectiveness is proved by simulation results.

Artificial intelligence and quasar absorption system modelling; application to fundamental constants at high redshift

ABSTRACT We have developed a new fully automated Artificial Intelligence (AI)-based method for deriving optimal models of complex absorption systems. The AI structure is built around VPFIT, a well-developed and extensively tested nonlinear least-squares code. The new method forms a sophisticated parallelized system, eliminating human decision-making and hence bias. Here, we describe the workings of such a system and apply it to synthetic spectra, in doing so establishing recommended methodologies for future analyses of Very Large Telescope (VLT) and Extremely Large Telescope (ELT) data. One important result is that modelling line broadening for high-redshift absorption components should include both thermal and turbulent components. Failing to do so means it is easy to derive the wrong model and hence incorrect parameter estimates. One topical application of our method concerns searches for spatial or temporal variations in fundamental constants. This subject is one of the key science drivers for the European Southern Observatory’s ESPRESSO spectrograph on the VLT and for the HIRES spectrograph on the ELT. The quality of new data demands completely objective and reproducible methods. The Monte Carlo aspects of the new method described here reveal that model non-uniqueness can be significant, indicating that it is unrealistic to expect to derive an unambiguous estimate of the fine structure constant α from one or a very small number of measurements. No matter how optimal the modelling method, it is a fundamental requirement to use a large sample of measurements to meaningfully constrain temporal or spatial α variation.

Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays.

Angle-of-arrival (AoA) estimation in localized hybrid arrays suffers from phase ambiguity owing to its localized structure and vulnerability to noise. In this letter, we propose a novel phase shift design, allowing each subarray to exploit difference beam steering in two potential AoA directions. This enables the calibration of cross-correlations and an enhanced phase offset estimation between adjacent subarrays. We propose two unambiguous AoA estimation schemes based on the even and odd ratios of the number of antennas per subarray N to the number of different phase shifts per symbol K (i.e., ), respectively. The simulation results show that the proposed approach greatly improves the estimation accuracy as compared to the state of the art when the ratio is even.

Sparse Nested Arrays With Spatially Spread Square Acoustic Vector Sensors for High-Accuracy Underdetermined Direction Finding

In acoustic sensing systems, acoustic vector sensor (also known as vector hydrophone for underwater applications) arrays are widely used. Most of the acoustic vector sensor array signal processing methods presume the minimum spacing between two adjacent sensors or sensor components to be within a half-wavelength in order to avoid azimuth-elevation angle estimation aliasing. This would limit the effective array aperture, thereby reducing the potential estimation accuracy. Furthermore, they are unapplicable to the underdetermined scenarios, where the number of sources exceeds that of sensor components. Exploiting the recently proposed nested array concept, we present a new type of nested array, termed as Sparse Nested spatially spread Square Acoustic Vector sensor Array (SNSAVA) to realize underdetermined 2-D direction finding with increased estimation accuracy. In SNSAVA, interspacing of two sensors and two components of a sensor can be spread to be much higher than a half-wavelength so that the effective array aperture will be significantly extended. An unambiguous angle estimation method is further derived to make this fully sparse array configuration practically feasible. Performance studies focused on underdetermined high-accuracy azimuth-elevation angle estimation are provided via numerical examples. The estimation performance for the SNSAVA is also compared with that of the nested acoustic vector sensor arrays, proposed in [33], and with the Cramér-Rao bound.

Применение теории решетчатых упаковок в задаче высокоточного абсолютного местоопределения по ионосферосвободным измерениям в ГНСС с кодовы м разделением

The paper considers the use of the lattice packing theory for Integer Precise Point Positioning (Integer PPP) with the errors usually not exceeding 1–3 cm based on GNSS signals with code division multiple access (CDMA). Positioning is carried out by processing ionosphere-free linear combinations of code and phase measurements with ambiguity resolution employing satellite corrections. The main issue of PPP algorithms is overcoming the rank deficiency problem of the linear equation system obtained by linearization of nonlinear mathematical models of measurements. Nowadays Float PPP is quite well developed, where rank deficiency is tackled by combining systematic biases in measurement models with integer carrier phase ambiguities. As a result, the number of unknowns is reduced to the rank of design matrix, which allows unambiguous estimation of precise user coordinates and values of new variables generated by the performed combinations. However, under such conditions the information about integer nature of carrier phase ambiguities is lost, and this leads to a significant increase in convergence time to obtain user coordinates estimates with the errors of 1–3 cm. It is possible to involve the information on the integer nature of phase ambiguities into processing by applying ambiguity resolution algorithms. Though, as a result of the conducted combinations, the integer nature is destroyed, which makes it impossible to apply these algorithms. In Integer PPP rank deficiency is overcome by projecting the state space of the initial linear equation system onto a so-called S-space, whose dimension is equal to the rank of this system. The orientation of the S-space and the direction of projecting are chosen so that the variables of the initial system corresponding to user coordinates are not changed during the projecting and the projections of integer variables remain integer. This makes it possible to estimate precise user coordinates involving information on the integer nature of phase ambiguities. In the literature on Integer PPP based on CDMA GNSS signals processing the description of the S-space orientation with the desired properties is given, but there is no description of the method to determine this orientation. This paper based on the notions of the lattice packing theory considers an algorithm for determining the S-space with the desired properties. It is shown that there exists an infinite set of such S-spaces connected by unimodular transformations, and a technique is proposed to enable selection from this set the S-space, which requires minimal computational cost. The use of the lattice packing theory to the Integer PPP network solution with CDMA GNSS signals will be considered in the following publication of the authors.

Joint 2D-DOA and polarization estimation for sparse nonuniform rectangular array composed of spatially spread electromagnetic vector sensor

In this paper, a sparse nonuniform rectangular array based on spatially spread electromagnetic vector sensor (SNRA-SSEMVS) is introduced, and a method for estimating 2D-direction of arrival (DOA) and polarization is devised. Firstly, according to the special structure of the sparse nonuniform rectangular array (SNRA), a set of accurate but ambiguous direction-cosine estimates can be obtained. Then the steering vector of spatially spread electromagnetic vector sensor (SSEMVS) can be extracted from the array manifold to obtain the coarse but unambiguous direction-cosine estimates. Finally, the disambiguation approach can be used to get the final accurate estimates of 2D-DOA and polarization. Compared with some existing methods, the SNRA configuration extends the spatial aperture and refines the parameters estimation accuracy without adding any redundant antennas, as well as reduces the mutual coupling effect. Moreover, the proposed algorithm resolves multiple sources without the priori knowledge of signal information, suffers no ambiguity in the estimation of the Poynting vector, and pairs the x-axis direction cosine with the y-axis direction cosine automatically. Simulation results are given to verify the effectiveness and superiority of the proposed algorithm.