The problem of the limited accuracy of precipitation Doppler spectrum moments estimation measured by fast azimuthally scanning weather radars is addressed. A novel approach for the Doppler moment estimation based on maximum likelihood estimation is proposed. A simplified semianalytical parametric model for the precipitation power spectral density (PSD) as a function of the velocity parameters of the scatterers and the finite radar observation time is derived for typical precipitation-like weather conditions. An inverse problem for estimating the Doppler moments from measurements of the PSD is formulated and solved. It is demonstrated that the variance of the estimation of the Doppler moments approaches the Cramer Rao Lower Bound (CRB) when the observation time approaches infinity. The performance of the proposed approach is compared with some classical techniques and another realization of the maximum likelihood approach based on simulated and experimental data. The results indicate the superiority of the proposed approach, especially for short observation time. Furthermore, a scanning strategy to accurately estimate the Doppler moments based on the true velocity dispersion of the scatterers is provided with the help of the proposed approach.@en

The challenge of avoiding aliasing in the Doppler spectrum for precipitation is addressed. A novel integrative signal processing approach has been proposed to address the research gaps from various disciplines. The proposed approach consists of several steps. First, an aperiodic way of sampling the echoes (aperiodic sampling refers to aperiodic pulse train in the context of radar echoes in slow time) has been proposed by which the maximum unambiguous Doppler frequency (velocity) is enhanced. Second, the Doppler spectrum moment estimation is performed with the help of a parametric form of its covariance. The performance of the moment estimation is assessed by the bias and the variance in the estimated counterparts. The theoretical variance for the parameter estimation is also derived. An aperiodic pulse train design recommendation has been proposed for adequately and unambiguously estimating the Doppler moments for one extended target (like precipitation). Finally, a spectrum reconstruction technique is implemented after the moment estimation on simulated radar echo samples for a realistic precipitation-like event. The comparison with the other approaches proves its superiority for parameter estimation and Doppler spectrum reconstruction.

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The challenge of reconstructing the Doppler spectrum of a precipitation-like event observed by a fast-scanning weather radar is addressed. A novel method is proposed where the echo sequence in time is assumed to be a complex Gaussian process with a known covariance structure. It is a two-step approach where the first step is the estimation of the hyperparameters of the covariance function with a maximum likelihood approach, and the second step is the reconstruction of the spectrum directly in the time or spectral domain. The proposed approach is applied to simulated data for hyper-parameter estimation performance analysis and real radar data for the complete Doppler spectrum reconstruction. @en

The challenge of processing incoherent sets of radar echoes to estimate the Doppler moments and noise standard deviation from precipitation with a fast-scanning radar is addressed. The recently proposed maximum likelihood parametric Doppler spectrum estimator (PSE) is extended to accommodate the noise standard deviation along with the Doppler moments in the parametric model of the Doppler power spectral density (PSD). Its performance in estimating the Doppler moments and the noise standard deviation is compared with another maximum likelihood approach. The proposed approach has a smaller bias in the estimation of the noise standard deviation for a wide range of spectral widths on simulated data. The algorithm is verified on experimental data from a fast-scanning weather radar. @en

The radar resource management problem in a multitarget tracking scenario is considered. The problem is solved using a dynamic budget balancing algorithm. It models the different sensor tasks as partially observable Markov decision processes and solves them by applying a combination of Lagrangian relaxation and policy rollout. The algorithm has a generic architecture and can be applied to different radar or sensor systems and cost functions.This is shown through simulations of two-dimensional tracking scenarios. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment in a nonmyopic fashion. Its performance is compared with different resource allocation techniques and its computational load is investigated with respect to several input parameters.@en

The Multiple Object Tracking problem for a known and constant number of closely-spaced objects in a track-before-detect context is considered. The underlying problem of decomposing a dataassociation- free Bayes posterior density is analyzed. A previously proposed solution for two objects moving in one-dimensional space is generalized for higher dimensional problems where t objects move in a M-dimensional space. The underlying problem is solved with the proposed Cross Modeling Tracker by means of hypothesizing physical crosses between the objects for a general t-MD objects case. In particular, the mathematical definition of cross-between-objects is generalized from a meaningful interpretation of the problem in the low dimensional setting. A method to provide optimal references for evaluation of the Cross Modeling Tracker is also considered. The Cross Modeling Tracker algorithm is validated with the optimal references by simulating t-MD closely-spaced objects scenarios. Wider applicability of the Cross Modeling Tracker with respect to comparable reviewed solutions is demonstrated via simulation experiments. @en

Radar Resource Management in a multi-sensor multi-target scenario is considered. A dynamic resource balancing algorithm is proposed which optimizes target task parameters assuming an underlying partially observable Markov decision process (POMDP). By applying stochastic optimization methods, such as policy rollout, the POMDP is solved non-myopically. The approximately optimal approach is formulated assuming a central processor. Subsequently, a distributed implementation is introduced that converges to the same results as given by the centralized implementation and requires less computational resources. The performance of the proposed approach for both centralized and distributed implementation is demonstrated through dynamic tracking scenarios.@en

The radar resource management problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking task. Model predictive control is applied to solve the POMDPs in a non-myopic way. As a result, the computational complexity compared to stochastic optimization methods such as policy rollout is dramatically reduced while the resource allocation results maintain similar. This is shown through simulations of dynamic multi-target tracking scenarios in which the cost and computational complexity of different approaches are compared.@en

The radar resource management problem in a multitarget tracking scenario for multi-function radar is considered. To solve it, an optimal balancing of the sensor budget by applying Lagrangian relaxation and the subgradient method is proposed. In a time-invariant scenario it is shown that the proposed method will lead to balanced budgets based on track parameters like maneuverability and measurement uncertainty. Moreover, since real world applications quickly lead to time-varying scenarios, it is demonstrated how the approach can be extended to such cases. Furthermore the proposed method is compared with other budget assignment strategies. This paper is the first step into exploring optimal non-myopic solutions using a POMDP framework for surveillance radar applications involving detection, tracking and classification.@en

The sensor resource management problem in a multi-object tracking scenario is considered. In order to solve it, a dynamic budget balancing algorithm is proposed which models the different sensor tasks as partially observable Markov decision processes. Those are being solved by applying a combination of Lagrangian relaxation and policy rollout. The algorithm converges to a solution which is close to the optimal steady-state solution. This is shown through simulations of a two-dimensional tracking scenario. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment and takes predictions of the future situation into account.@en

Wind turbines usually cause significant interference in the conventional radar operations which might degrade the detection capabilities. Wind turbines do not only block the radar beam focusing on a specific target, thus creating shadowing effects, but also impose Doppler spectra contamination due to the continuous blade rotation. Therefore in order to identify, detect and possibly mitigate the presence of Wind Turbine Clutter (WTC), fundamental features of the blades rotation need to be estimated, such as rotation (angular) velocity. This information can be directly extracted by initially evaluating the angular displacement of the blades between successive radar measurements. In this paper, a method to estimate this rotation angle is proposed which is based on both radar polarimetry and estimation theory.

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We consider a target selection problem for multitarget tracking in a multifunction radar network from a gametheoretic perspective. The problem is formulated as a noncooperative game. The radars are considered to be players in this game with utilities modeled using a proper tracking accuracy criterion and their strategies are the observed targets whose number is known. Initially, for the problem of coordination, the Nash equilibria are characterized and, in order to find equilibria points, a distributed algorithm based on the bestresponse dynamics is proposed. Afterwards, the analysis is extended to the case of partial target observability and radar connectivity and heterogeneous interests among radars. The solution concept of correlated equilibria is employed and a distributed algorithm based on the regret-matching is proposed. The proposed algorithms are shown to perform well compared to the centralized approach of significantly higher complexity

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In this paper, waveforms for MIMO phased array radar to enhance cross-range resolution are investigated. The problem of high sidelobes in range created by the use of Hybrid Codes with a single waveform and spatial coding is considered and a method to reduce these sidelobes by the use of Golay sequences as spatial codes is proposed. It is shown that the proposed method achieves the same range performance as a phased array radar with one waveform, despite creating additional sidelobes in Doppler.@en

When the fire brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. Smart buildings should be able to expose this location data to the fire brigade working in a smart city. In this paper the feasibility is researched of using ultrasonic sound sensors for human presence detection in smoke-filled spaces. This type of sensor could assist the fire brigade when evacuating a large building by directing them to the places where their help is most needed. The advantage of ultrasonic sound over other sensors or cameras is that its signal is able to pierce through smoke, does not require badges or other wearable devices and introduces little privacy and security risks. In addition, ultrasonic sensors are very inexpensive making it possible to equip every room of a building with an ultrasonic presence detector. In this research both a preliminary ultrasound measuring device and signal processing algorithm have been designed. Testing results show that the walking movement of a person in an indoor area can be detected with the combination of the sensor and the algorithms. In addition, tests of the signal strength in smoke have shown that ultrasound is capable of “looking through” the smoke. The algorithm based on a particle filter allows for more information to be extracted from the relatively simple sensor signal by detecting human walking movement specifically and opens up the way for an ultrasound based indoor positioning system that can be used in emergency situations.@en

In this paper, a track selection problem for multi-target tracking in a multifunction radar network is studied using the concepts from game theory. The problem is formulated as a non-cooperative game, and specifically as an anti-coordination game, where each player aims to differ from what other players do. The players' utilities are modeled using a proper tracking accuracy criterion and, under different assumptions on the structure of these utilities, the corresponding Nash equilibria are characterized. To find an equilibrium, a distributed algorithm based on the best-response dynamics is proposed. Finally, computer simulations are carried out to verify the effectiveness of the proposed algorithm in a multi-target tracking scenario.@en