Mark Rutten

A comparison of detection performance for several Track-Before-Detect algorithms

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point measurements from the observed sensor data. Track before detect (TBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TBD problem. This article compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, dynamic programming, particle filtering methods, and the histogram probabilistic multihypothesis tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.

Detection and Tracking Using Particle-Filter-Based Wireless Sensor Networks

The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramér-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system.

A Comparison of Three Algorithms for Tracking Dim Targets

Tracking of dim, or low signal-to-noise ratio (SNR), targets is commonly achieved using track-before-detect (TBD) techniques. While traditional tracking algorithms operate on detections, typically formed by applying an intensity threshold to the sensor data, TBD algorithms operate directly on un-thresholded sensor data. Increasing the information available to the tracker in this way potentially allows tracking of lower SNR targets compared to trackers using detections. This paper compares three algorithms on simulated data containing a single dim target: a histogram PMHT algorithm and a particle filter, both of which could be classified as track-before-detect algorithms, and a PDA algorithm which operates on detections. The algorithms are compared in terms of detection performance, false track rate and RMS error.

Using Phase to Improve Track-Before-Detect

Track-Before-Detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Existing literature uses only the envelope of complex data; this article presents an approach that also includes the phase information. The inclusion of phase is shown to both improve the discrimination of targets from noise and reduce the computation overhead, with improved performance demonstrated using three representative algorithms.

Detection and tracking using wireless sensor networks

Target detection and tracking is a well-established area of research. However, a majority of proposed solutions in existing literature rely on expensive and specialized sensors, which often have limited coverage. Using low cost sensor nodes is an attractive and complementary approach to scalable target detection and tracking applications. However, tracking with low cost Wireless Sensor Network (WSN), presents its own challenges, namely real time decision making, high frequency sampling, multi-modal sensing, complex signal processing, and data fusion. In this work, we investigate the use of inexpensive off-the-shelf WSN devices for ground surveillance. Our system estimates and tracks a target based on the spatial differences of the target objects signal strength detected by the monitoring sensors at different locations.

Performance evaluation of a wireless sensor network based tracking system

In this paper, we present a comprehensive analysis of the performance of a wireless sensor network based target tracking system using the particle filter. In particular, we evaluate the effect of various network design parameters such as the number of nodes, number of generated particles, and sampling interval on the tracking accuracy and computation time of the tracking system. Based on our analysis, we also present recommendations on suitable values for the relevant network design parameters, which provide a reasonable tradeoff between accuracy and computational expense for this problem. In addition, we also analyse the theoretical Cramer-Rao bound as the benchmark for the best possible tracking performance. We demonstrate that the results from our simulations closely match the theoretical bounds. We also present initial results from experiments comprising of a 25 node wireless sensor network. Initial experimental results are promising and show that the PF based estimation is suitable for detection and tracking using inexpensive wireless sensor network devices.

Over-the-horizon radar multipath track fusion incorporating track history

This paper describes an algorithm for associating and fusing multipath tracks in over-the-horizon radar (OTHR). The algorithm extends earlier work by using a model based approach to incorporate track history in its computation of association probabilities and fused estimate calculations, thus exploiting temporal as well as spatial relationships. The algorithm can be easily extended to achieve asynchronous fusion of non-OTHR tracks (e.g. microwave radar or GPS) with the multipath OTHR tracks.

Fusion of multipath tracks for a network of over-the-horizon radars

/sup O/ver-the-horizon radar (OTHR) provides wide area surveillance coverage beyond the line-of-sight horizon by the propagation of HF signals via the ionosphere. Targets with a radial velocity relative to the radar site are detected by the Doppler component of the received radar signal. A network of OTHRs with overlapping coverage provides for the detection and tracking of targets irrespective of target velocity. When tracks are formed by each radar in the network, multi-radar track fusion is necessary to clarify the surveillance picture. In addition, multipath ionospheric propagation conditions may yield multiple tracks for each target and for each radar. In this paper, a multi-radar multipath track fusion algorithm (MRMPTF) is outlined, extending the single radar multipath track fusion (MPTF) algorithm previously developed. The impact of OTHR track registration errors is discussed, and the equivalence of alternative OTHR track fusion architectures is shown.

Measurement Model, Satellite Communications

The Bayesian filter discussed in Chap. 3 relies on knowledge of three probability density functions: the state prior distribution, the state stochastic model, and the measurement conditional probability density.

Track before Detect for Space Situation Awareness

This article considers the application of multi-target tracking algorithms to Space Situation awareness. The sensor is a telescope fitted with an optical band digital camera. Two different tracking paradigms are demonstrated: the first approach is a detect-then-track method that uses frame-to-frame registration to model the star field and detect a moving satellite, the detections are processed using a point-measurement tracker; the second is a track-before-detect algorithm that uses the telescope images directly as input and jointly tracks the stars and the satellite. The two are compared on experimental imagery collected from a telescope system.