Localizing a source from raw array data by reparameterizing the array steering vector by the source position is a fundamental principle of direct position determination (DPD). This study demonstrates passive localization of a source in an urban terrain using unsynchronized homogeneously distributed array sensors. Utilizing the received direct signal alongside its first-order reflections, which are modeled using the image source method (ISM), enables the direct estimation of both the source position and the corresponding image source position. With the knowledge of the known sensor positions and the position estimates, we show the closed form solution to determine the reflection points on the walls, leading to the computation of the corresponding Cramér-Rao Bound (CRB). The determined reflection points form the wall segment, which in turn provides information about the urban environment. Simulation indicates that the estimator approaches the CRB asymptotically. Furthermore, we apply this method to experimental data, demonstrating that it is feasible to extract information about both the source and the surrounding environment without a priori knowledge.

This paper presents the application of a novel target tracking technique employing tensor decompositions for discretizing the target state space. The time evolution of the conditional probability density is realized by a Fokker-Planck equation solver and the measurement update, as usual, by applying Bayes’ rule. The method is applicable to non-Gaussian and non-linear system equations and enables the treatment of complex non-Gaussian target state densities. In addition, the efficient tensor decomposition scheme, in principle, allows for high-dimensional target states. The new tracking filter is applied to the problem of tracking an agile air target using bearing measurements from distributed acoustic and electromagnetic array sensors based on real data. It is shown that the new filter is able to initiate and maintain the target track with localization errors comparable to those of a standard particle filter.

Closing the feedback loop has proven essential for performance improvement and enhancing system reliability in a number of applications. Track evaluation addresses these goals by investigating system influences and analyzing discrepancies between expected and actual operation. Developing a model of a passive radar system which approximates its operational capabilities is therefore not only essential for performance prediction and pre-operational analysis but offers the opportunity for further system improvement. In the following, we assess the fit of a passive radar model to real-world data to investigate if it provides a reasonable representation of the observed results. There, we show that the detection probability model as well as the estimated lower bound on achievable estimation accuracy, where we utilize the Cramér-Rao Lower Bound (CRLB), give a reasonable approximation of real-world results collected by a passive radar system which utilizes Global System for Mobile Communications (GSM) signals.

In extended object tracking, random matrices are commonly used to filter the mean and covariance matrix from measurement data. However, the relation from mean and covariance matrix to the extension parameters can become challenging when a lidar sensor is used. To address this, we propose virtual measurement models to estimate those parameters iteratively by adapting them, until the statistical moments of the measurements they would cause, match the random matrix result. While previous work has focused on 2D shapes, this paper extends the methodology to encompass 3D shapes such as cones, ellipsoids and rectangular cuboids. Additionally, we introduce a classification method based on Chamfer distances for identifying the best-fitting shape when the object’s shape is unknown. Our approach is evaluated through simulation studies and with real lidar data from maritime scenarios. The results indicate that a cone is the best representation for sailing boats, while ellipsoids are optimal for motorboats.

The increasing advances in Unmanned Systems are transforming the type of threats traditional defence systems are designed to tackle. One significant advancement is in artificial intelligence capability which allows a group of agents to perform complex collective behaviors. As consequence, providing defence systems with threat intelligence capability is becoming a necessity, where identifying collective behaviours and coordinated targets can significantly increase the effectiveness of the system. In this work, we propose a probabilistic approach to identify certain types of coordinated targets. A scoring function that utilizes accumulated state densities (ASDs) over a sliding time window is proposed to compute the likelihood of a pair of targets being coordinated. A simulation scenario including different types of coordination is used to test the performance of this proposed method.

Random matrices are used to filter the center of gravity (CoG) and the covariance matrix of measurements. However, these quantities do not always correspond directly to the position and the extent of the object, e.g. when a lidar sensor is used.In this paper, we propose a Gaussian processes regression model (GPRM) to predict the position and extension of the object from the filtered CoG and covariance matrix of the measurements. Training data for the GPRM are generated by a sampling method and a virtual measurement model (VMM). The VMM is a function that generates artificial measurements using ray tracing and allows us to obtain the CoG and covariance matrix that any object would cause. This enables the GPRM to be trained without real data but still be applied to real data due to the precise modeling in the VMM. The results show an accurate extension estimation as long as the reality behaves like the modeling and e.g. lidar measurements only occur on the side facing the sensor.

The localization of sound sources in urban environments is a challenging task and the self-position and self-orientation of the individual sensor nodes can usually not be assumed to be precisely known. Therefore in this paper, a method for simultaneous sensor and source localization under line-of-sight conditions in urban environment is proposed. The method is based on acoustic bearing and relative time difference of arrival measurements as well as self-position and self-orientation measurements of an asynchronous acoustic sensor network. Here, both direct wave and first-order reflections are taken into account. We derive the Cramér-Rao bound for the corresponding localization problem and compare it with results from a Monte Carlo simulation. We consider cases where the sensors provide all measurements as well as cases where parts of the measurements fail, resulting in sub-measurement data sets. Here, a focus is on scenarios with fully unknown sensor states. Depending on the measurement data set under consideration, the initial self-position measurement is improved by taking into account the acoustic measurements, which in turn has a positive impact on the source localization accuracy in many considered cases.

Research on Information Fusion does not consider whether and with what capabilities defense and security systems should be developed. Nor does it have to assess the ethical dimension of their use. Nevertheless, its tasks include systematically investigating technical options and limitations of such systems, researching methods for their use and control, advising users, and finally developing the required technology. At the same time, it must assess development risks associated with this technology at the technical level and estimate future development trends. Fusion research for defense and security therefore provides a factual basis which must be taken into account in any ethical evaluation. In this context, it is also necessary to consider what is substantially new about innovative technology compared to systems already in use. As concrete examples we consider armed drones and hazardous material localization in public spaces.

This paper deals with data fusion of acoustic measurements of multiple sensors for the purpose of shooter localization. Muzzle blast and shock wave are the two impulsive sounds that occur due to supersonic gunfire. Depending on its position, a microphone array can measure a complete measurement data set, composed of two bearing angles and the TDOA between both sound waves, or an incomplete subset. In this paper a framework is created to fuse all measurements, whether complete or incomplete. For this problem, a shooter state estimator is proposed and the associated Cramér-Rao bound is derived. The estimation results are studied in Monte Carlo simulations and evaluated in comparison with the Cramér-Rao bound.

Ahstract- This paper presents a novel discussion on comparison of common extended object tracking methods for estimating target-extension ellipses based on real world road level traffic data and a unique presentation on different target level signatures obtained for various object types such as cars, pedestrians and bicyclist obtained using automotive multi-mode radar network and 4x-Iayer automotive Iidar. The most commonly used extended object tracking methods are briefly introduced. In addition, measurement profiles of road users, car, cyclists, and pedestrians are investigated to compare each model and its assumptions with the real data. The obtained information can be further used to define the appropriate object specific and sensor specific measurement model. The measurement distribution is discussed in detail and compared with the models' assumptions. Extended object tracking is performed on the obtained data and performance analysis is carried out using high resolution ground truth. The evaluation is carried out separately on the radar and lidar data on exemplary traffic scenes. As a final step, a track-to-track intersection fusion approach is evaluated on the same data set to find out the different information gains. The relation between measurements and track behavior as well as other influences are discussed.

The notion of indistinguishable targets is well-established in advanced target tracking. Conceptually, it is rooted in quantum physics where functions of joint particle states are considered that are either symmetric or anti-symmetric under permutation of the particle labels. This symmetry dichotomy explains why two disjunct classes of particles exist in nature: bosons and fermions. Besides symmetry, also anti-symmetry has a place in multiple target tracking as we will show, leading to well-defined probability density functions describing the joint target states. Inbuilt anti-symmetry implies a target tracking version of Pauli'sexclusion principle: Real world targets are “fermions” that cannot exist at the same time at the same place. This is of interest in dense tracking scenarios with resolution conflicts and split-off. We discuss the concept and illustrate its benefits.

With the ubiquity of information distributed in networks, performing recursive Bayesian estimation using distributed calculations is becoming more and more important. There are a wide variety of algorithms catering to different applications and requiring different degrees of knowledge about the other nodes involved. One recently developed algorithm is the distributed Kalman filter (DKF), which assumes that all knowledge about the measurements, except the measurements themselves, are known to all nodes. If this condition is met, the DKF allows deriving the optimal estimate if all information is combined in one node at an arbitrary time step. In this paper, we present an information form of the distributed Kalman filter (IDKF) that allows the use of explicit system inputs at the individual nodes while still yielding the same results as a centralized Kalman filter.

Since navigation systems and tracking devices are becoming ubiquitous in our daily life, the development of efficient methods for processing massive sets of mobile objects are of utmost importance. Although future routes of mobile objects are often known in advance in many applications, this information is not fully utilized in most methods so far. In this paper, we reveal the beneficial effects of exploiting future routes for the early generation of the expected results of spatio-temporal queries. This kind of probable results is important for operative analytics in many applications like smart fleet management or intelligent logistics. For efficiently computing the high number of future trajectory points, a new index structure is presented which allows for a fast maintenance of query results under continuous changes of mobile objects. Our methods make use of specific update patterns, which require substantially less maintenance costs than the most general case of an update. A set of experiments based on a commonly used simulation environment shows the efficiency of our approach.

Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations. Additionally, we provide the possibility to estimate only sub-sets of parameters, and to reliably model resulting added uncertainties by the covariance matrix. The approach will be studied in two practical examples: 3D track initialization using bearings-only measurements and using slant-range and azimuth only. Numerical results will include performance and consistency analysis via Monte-Carlo simulations and comparison to the Cramer-Rao lower bound.

Advances in characterizing the angle measurement covariance for phased array monopulse radar systems that use adaptive beamforming to null out a jammer source allow for the use of improved sensor models in tracking algorithms. Using a detection probability likelihood function consisting of a Gaussian sum that incorporates negative contact measurement information, four tracking systems are compared when used to track a maneuvering target passing into and through standoff jammer interference. Each tracker differs in how closely it replicates sensor performance in terms of accuracy of measurement covariance and the use of negative information. Only the tracker that uses both the negative contact information and corrected angle measurement covariance is able to consistently reacquire the target when it exits the jammer interference

For analyzing dynamic scenarios with many ground moving vehicles, airborne ground moving target indicator (GMTI) radar is well-suited due to its wide-area, all-weather, day/night, and real time capabilities. The generation of GMTI tracks from these data is the backbone for producing a "recognized ground picture" as well as for analyzing traffic flows. In this paper we discuss the benefits of GMTI tracking in view of extracting road map information. The resulting tracking-generated road maps are highly up-to-date and fairly precise. Moreover, their accuracy is quantitatively described. The precision of the extracted road segments can significantly be improved using smoothed or "retrodicted" tracks. In turn, the extracted road information is exploited for the precise tracking of succeeding road targets. The proposed approach is illustrated by a simulated example including Doppler and terrain obscuration, providing hints to the achievable road map accuracies