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Using fractal dimension or one of other fractal characteristics to detect targets in sea clutter, it is often difficult to distinguish at low signal-to-clutter ratios. To solve this problem, a new method for target detection in sea clutter based on combined fractal characteristics is proposed. The detection process is divided into two stages: coarse detection and fine detection. When the fractal spectrum...
We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real...
Autonomous vehicles operating in dynamic environments rely on precise localization. In this paper we present a novel approach for cooperative localization of vehicular systems and an infrastructure RADAR which is resilient against outliers generated from the RADAR. The problem of cooperative localization is represented as a factor graph, where interrelated topologies (including that of outliers) are...
The conventional multi-target tracking (MTT) algorithms usually suffer from computational intractability problem. The appearance of Iterative Joint Integrated Probabilistic Data Association (iJIPDA) filter solves this problem by providing a tradeoff between the tracking performance and computational cost for computational resource management of sensor systems. However, the iJIPDA filter essentially...
This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework...
In this paper, we propose an integrated system to detect and track a single operator that can switch off and on when it leaves and (re-)enters the scene. Our method is based on a set-valued Bayes-optimal state estimator that integrates RGB-D detections and image-based classification to improve tracking results in severe clutter and under long-term occlusion. The classifier is trained in two stages:...
With the increase of the imaging resolution, the resulting enormous amount of sampling raw data aggravates transmission and storage load for multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the dual-channel SAR images is high, we propose a Bayesian compressive sensing (BCS) based SAR imaging algorithm for ground moving targets indication (GMTI) system,...
For the over-the-horizon radar (OTHR) based target tracking, the reflecting height of the ionosphere, which reflects radar signals, is important. Existing methods assume that this height is exactly known a priori. In practice, however, we can only determine its range, not its specific value. To circumvent this problem, we propose to use a multiple-model approach in which each model corresponds to...
When mounted on a vehicle bumper, ultrasonic transducer signal contains information from valid objects as well as ground reflections. In order to remove ground echoes, the classic approach is to use thresholds to filter reflections of small amplitude. However, valid object reflections can frequently occur beneath the ground thresholds, reducing the detection rate of the sensor. We present an approach...
In a Y-shaped passive linear array sonar (PLAS) system, three sensor legs are configured and report bearings-only measurements, which are complicated due to bearing-ambiguity. As many ghost targets exist, multi-target tracking using a PLAS system is a challenging problem, especially when target miss-detection and clutter are also considered. In this paper, a distributed method is proposed to track...
In order to improve the multi-target tracking performance of Doppler radar in the presence of Doppler blind zone (DBZ), the detection probability model with minimum detectable velocity (MDV) is incorporated into Gaussian mixture cardinality balanced multi-target multi-Bernoulli (GM-CBMeMBer) filter. After integrating the new detection probability model into the multi-target posterior density of the...
The single-object Bayesian filter for an interval, or batch, of data is extended to the multiple object case using the method of analytic combinatorics. The exact expression for the probability generating functional of the Bayes posterior process is derived. It is a nested composition of functions and functionals that is evaluated via a backward recursion. Branching and immigration processes are used...
In this paper, we evaluate the performance of labelled and unlabelled multi-Bernoulli conjugate priors for multi-target filtering. Filters are compared in two different scenarios with performance assessed using the generalised optimal sub-pattern assignment (GOSPA) metric. The first scenario under consideration is tracking of well-spaced targets. The second scenario is more challenging and considers...
The multiple hypothesis tracker (MHT) has historically been considered a gold standard for multi-target tracking. In this paper we show that the key formula for hypothesis probabilities in Reid's MHT can be derived from the modern theory of finite set statistics (FISST) insofar as appropriate assumptions (Poisson models for clutter and undetected targets, no target-death, linear-Gaussian Markov target...
Evaluating the performance of multi-target tracking with respect to tracks rather than unlabeled estimated points is important and challenging. Existing approaches assume exact knowledge of the ground truth. However, this is far from the reality. This paper proposes a method to deal with the case of unknown ground truth by measuring the difference between mock tracks and the assumed targets in the...
This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the...
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