Distributed Accumulated State Density (DASD) filtering is an effective strategy for optimal multi-sensor fusion. However, the fusion of Accumulated State Densitys (ASDs) with unequal window lengths, as occurs in typical multi-sensor multiobject applications, has not yet been covered in the literature. This paper aims to fill that gap by exploring various approaches, which range from ASD cutting to more sophisticated ASD adaptation and ASD zero-padding. In consequence, we arrive at the optimal ASD-Information Matrix Fusion or ASD-Tracklet Fusion, respectively. Both provide the optimal and full ASD result with full-rate transmission of standard (single state) densities only, thus reducing the communication load compared to a corresponding transmission of full ASDs. These methods perform particularly well in practical scenarios where the models are not perfectly adapted. All approaches are discussed theoretically and are thoroughly evaluated in simulation.

HENSOLDT has developed a multi-band passive radar system which is using analog and digital audio and/or video broadcasting stations to create an air situation picture in real-time. This paper presents the underlying principles and building blocks of the tracking and data fusion software within the Hensoldt Passive Radar System. Tracking results will demonstrate both overall surveillance performance as well as the capability to track maneuvering targets.

The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made in order to reduce the range bias that shows up in the filter estimates when a Cartesian pseudo-measurement is created from the polar measurements by applying the respective conversion formulae in combination with a corresponding linearized form of the measurement error covariance matrix. It turns out that the actual behavior of these different approaches strongly depends on the specific situation under consideration where observed effects range from a truly (approximate) suppression of conversion bias up to an even increased bias. In this paper, a systematic approach to analyzing the bias effects of measurement conversion in certain typical tracking situations is presented. Starting from this approach, a new measurement conversion technique is proposed that yields consistent unbiased estimates in these cases.

For a reduction of nonlinear distortion produced by a dynamic loudspeaker, there exists a variety of approaches, one of them using the known principle of exact input-output linearization. In combination with a discrete state-space observer, this approach can successfully be applied as long as the amplifier driving the loudspeaker is dc-coupled. This paper discusses how ac amplifiers can be used in this context. It will be shown that an exact linearization is in general not possible with ac amplifiers due to insufficient stability of the feedback system. Two approaches will be presented that abandon the pursuit of an exact linearization in favor of stability and yield satisfactory results when implemented on a floating-point digital signal processor.

In this paper, an electrical circuit is proposed which is suitable for a parametric modeling of electrodynamic transducers, e.g., dynamic loudspeakers. Since the system modeled is known to be passive, the preservation of this property is a key issue of the approach presented here. This is of special importance when nonlinear effects are taken into account, as passivity always ensures different kinds of stability. The only nonlinear elements occurring in the circuit model are ideal transformers, the turns ratios of which are not constant but depend, in particular, on some signal quantity. As a consequence, the circuit can be digitally simulated by applying principles known from the theory of wave digital filters.

Wave digital filter principles are known to be applicable to the numerical solution of many kinds of differential equations. But, when applied to an electrical network containing nonlinear dynamical elements, the resulting algorithm often includes implicit equations. In this paper, a new approach is presented which leaves the underlying wave digital structure unchanged but avoids the task of finding exact solutions to the implicit equations involved.