Abstract. Multi-sensor tracking using delayed, out-of-sequence Information (OOSI) is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that information (in the form of raw or processed measurements) is received out-of-time-order at the fusion center. Owing to compatibility with legacy sensors and limited communication bandwidth most practical fusion systems send track information rather than raw measurements to the fusion node. This paper presents a unified Bayesian approach to handling this out-of-sequence information problem and provides implementable sub-optimal algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements/tracks. Such an approach leads to a solution involving the joint probability density of current and past target states. A fixed-lag smoothing framework, developed by John Moore and his students almost 30 years ago, forms the basis of our algorithm. Under linear Gaussian assumptions, the Bayesian solution reduces to an Augmented State Kalman Filter (AS-KF). Computationally efficient versions of the AS-KF are considered in this paper. Simulations are presented to evaluate the performance of these solutions.
Fusing out-of-sequence information is a problem of growing importance due to an increased reliance on networked sensors embedded in complicated network urchirectures. The pmblem of fusing out-ofsequence measurements (OOSM) has received some attention in literature; however. mostpracticalfurion systems, owing to compatibility with legacy sensors and limited communication bandwidth, send track information instead of raw measurements to the fusion node. Delays introduced by the network can result in the reception of out-ofsequence tracks (OOST). This paper considers the pmblem offusing out-ofsequence measurements in general, and proposes an optimal Bayesian solution involving a joint probability density of current and past target stares, referred to as augmented states. By representing tracks using equivalent measurements. the relationship between OOSM and OOST-bused fusion is shown. The special case of Gaussian statistics is also addressed.
Sensor fusion is the notion of combining the data from two or more sensors in order to obtain enhanced performance compared with that of the individual sensors. In addition, Signal Detection Theory can be used to monitor how well a sensor operates. That is, through the number of hits, misses, false alarms and correct rejections a sensor registers, we gain a better understanding as to how reliably it performs. Typically, the performance of a sensor is given in terms of its probability of detection and probability of false alarm, which may not be well characterised. In this paper, we use the Transferable Belief Model to fuse two sensors where there is uncertainty in their performance, so that if two sensors give a report, for example, we can estimate the likelihood of the target being present. We also show that when we have known prior probabilities our result is equivalent to the Bayesian case. A numerical example, as well as entropy measures, are also discussed.
Abstract-Estimating the parameters of a cisoid with an unknown amplitude and polynomial phase using uniformly spaced samples can result in ambiguous estimates due to Nyquist sampling limitations. It has been shown previously that nonuniform sampling has the advantage of unambiguous estimates beyond the Nyquist frequency; however, the effect of sampling on the Cramér-Rao bounds is not well known. This paper first derives the maximum likelihood estimators and Cramér-Rao bounds for the parameters with known, arbitrary sampling times. It then outlines two methods for incorporating random sampling times into the lower variance bounds, describing one in detail.It is then shown that for a signal with additive white, Gaussian noise the bounds for the estimation with nonuniform sampling tend toward those of uniform sampling. Thus, nonuniform sampling overcomes the ambiguity problems of uniform sampling without incurring the penalty of an increased variance in parameter estimation.
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