An asymptotically efficient ARMA estimator based on sample covariances

An asymptotically efficient autoregressive moving-average (ARMA) spectral estimator is presented, based on the sample covariances of observed time series. The estimate of the autoregressive (AR) part is shown to be identical to the optimal instrumental variable (IV) estimator in [7] although derived here using a different approach. The moving-average (MA) spectral parameter estimate is new.     

MODE-type algorithm for estimating damped, undamped, or explosive modes

We propose a new algorithm for estimating the parameters of damped, undamped, or explosive sinusoidal signals. The algorithm resembles the MODE algorithm, which is commonly used for direction of arrival estimation in the array signal processing field. It is derived as a natural approximation to an asymptotically (high-SNR) optimal parameter estimator and has excellent statistical accuracy. Nevertheless, it is computationally simple and easy to implement. Numerical examples are included to illustrate the performance of the proposed method.This work has been supported by the Swedish Research Council for Engineering Sciences (TFR).     

Hidden-Action in Network Routing

In communication networks, such as the Internet or mobile ad-hoc networks, the actions taken by intermediate nodes or links are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful. Therefore, in the absence of incentives to the contrary, rational (i.e., selfish) intermediaries may choose to forward messages at a low priority or simply not forward messages at all. Using a principal-agent model, we show how the hidden-action problem can be overcome through appropriate design of contracts in both the direct (the endpoints contract with each individual router directly) and the recursive (each router contracts with the next downstream router) cases. We further show that, depending on the network topology, per-hop or per-path monitoring may not necessarily improve the utility of the principal or the social welfare of the system.     

Complex amplitude estimation in the known steering matrix and generalized waveform case

We consider a special growth-curve (SGC) model with a known steering matrix and generalized waveform in the presence of unknown interference and noise. Several estimators of the complex amplitude based on this model are derived, including the methods of approximate maximum likelihood (AML), minimum variance distortionless response (MVDR), and amplitude and phase estimation (APES). We analyze the statistical properties of these estimators and show that in the presence of temporally white but spatially correlated noise and interference, AML is asymptotically statistically efficient for a large snapshot number while MVDR and APES are asymptotically equivalent but not statistically efficient. Via several numerical examples, we also show that when the noise and interference are both spatially and temporally correlated, the APES estimator can achieve better estimation accuracy and exhibit greater robustness than the other methods.     

Orthogonal space-time block codes: maximum likelihood detection for unknown channels and unstructured interferences

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have been attracting increased attention. The so-called orthogonal space-time block codes (OSTBCs) have been of particular interest due to their good performance and low decoding complexity. In this paper, we take a systematic maximum-likelihood (ML) approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions. We derive a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data. Furthermore, we discuss the design of optimal training sequences and optimal information transfer to an outer decoder. Numerical examples demonstrate the performance of our algorithm.     

On LP-MUSIC

Studies the consistency properties of a method recently proposed for temporal or spatial frequency estimation from noisy data. The method in question is a MUSIC technique that makes use of a linear prediction algorithm to determine the signal subspace. It is shown that the signal subspace determined by the subject linear prediction-MUSIC (LP-MUSIC) algorithm can collapse in certain scenarios and. Hence, that the LP-MUSIC frequency estimates are not always consistent. The difficulties LP-MUSIC may encounter in some cases are illustrated by means of numerical examples     

InSAR validation based on GNSS measurements in Bucharest

This research aims to complex validate radar displacement products over Bucharest based on Global Navigation Satellite System or GNSS. Validation was represented by the comparison of the recent TerraSAR-X (TSX) displacement results with field GNSS data. For this purpose, 24 and 27 high-resolution images acquired from the TSX satellite between 2011 and 2014 were processed by Permanent Scatterer Inteferometry (PSI) and Small BASeline Subset (SBAS) – interferometric techniques using synthetic aperture radar (SAR) images. The images were selected from a larger pool of SAR images, after consulting weather databases, which helped eliminate the images acquired under adverse weather conditions. GNSS locations were cross-checked on high-resolution optical images and 21 ground points (including three permanent stations) were selected. All these points were (1) characterized by high coherence on radar images; (2) were on the ground; (3) were situated in large vacant spaces; and (4) far away from radar obstacles such as trees. A major challenge in achieving the research goal was to overcome the lack of permanent stations at the ground level within the area of Bucharest. Instead of permanent stations, our team designed and installed forced centring systems that remained stable over time, eliminating the instalment errors of GNSS receivers. The line-of-site (LOS) components derived from GNSS data have been compared with those of the SAR scatterers closest to the GNSS markers (never coinciding). For each location, the vertical reading was projected in the LOS of the SAR sensor. These readings were compared with the displacement values determined at the locations of permanent scatterers (PSs) within 100 m from the GNSS nodes, by eliminating all scatterers on buildings. The comparison has shown a good agreement.     

On Forward–Backward MODE for Array Signal Processing

We apply the MODE (method of direction estimation) principle to the forward–backward (FB) covariance of the output vector of a sensor array to obtain what we call the FB-MODE procedure. The derivation of FB-MODE is an interesting exercise in matrix analysis, the outcome of which was somewhat unexpected: FB-MODE simply consists of applying the standard MODE approach to the eigenelements of the FB sample covariance matrix. By using an asymptotic expansion technique we also establish the surprising result that FB-MODE is outperformed, from a statistical standpoint, by the standard MODE applied to the forward-only sample covariance (F-MODE). We believe this to be an important result that shows that the FB approach, which proved quite useful for improving the performance of many suboptimal array processing methods, shouldnotbe used with a statistically optimal method such as F-MODE.     

Direction-of-arrival estimation in applications with multipath and few snapshots

This paper analyzes two direction-of-arrival (DOA) estimation algorithms used in the presence of multipath propagation and with very few snapshots. The conditional maximum likelihood (CML) algorithm and the method of direction estimation (MODE) are discussed. The estimates provided by these algorithms are shown to coincide for large number of snapshots or large signal-to-noise ratio. Necessary and sufficient conditions are derived for the algorithms to yield unique estimates. It is shown that their uniqueness conditions coincide with the minimal uniqueness condition on the array, that is independent of the algorithm used (if the array does not satisfy this minimal condition, no DOA estimation method can give unique estimates). Numerical examples are presented to demonstrate the theoretical results.The work of A. Nehorai and D. Starer was supported by the Air Force Office of Scientific Research under Grant No. AFOSR-90-0164 and by the Office of Naval Research under Grant No. N00014-91-J-1298.     

Designing Unimodular Sequence Sets With Good Correlations—Including an Application to MIMO Radar

A multiple-input multiple-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. This increased virtual aperture enables many of the MIMO radar advantages, including enhanced parameter identifiability and improved resolution. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low auto- and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms.