Alexander Charlish

Agent based multifunction radar surveillance control

This paper introduces the continuous double auction parameter selection algorithm (CDAPS) which manages multifunction radar resource by utilising an auction mechanism to select parameters for individual radar tasks. The mechanism enables numerous localised agents representing tasks to globally optimise the resource utilisation objective. The algorithm is demonstrated on a long range surveillance control problem and compared to a conventional rule based approach. CDAPS offers a significant improvement by providing an emergent global optimisation of task quality. The continuous nature of the algorithm enables adaptation to a potentially complex and dynamic scenario. Additionally, the algorithm balances the time budget to produce graceful degradation and is implemented in real time.

Phased array radar resource management using continuous double auction

This paper addresses the problem of allocating resource and selecting operational parameters for multiple tasks for an electronically steered phased array radar system. The continuous double auction parameter selection (CDAPS) algorithm is presented, which controls the operational parameters such that a resource constraint is satisfied. The algorithm optimises the complete multiple task parameter selection problem, in contrast to the common approach of optimising the parameter selection for each single task separately. It is shown that CDAPS produces a near-optimal solution for the single resource discrete parameter selection problem. Simulated scenarios verify that this near-optimum solution significantly improves upon conventional rule based methods. The additional desirable characteristics of the CDAPS algorithm: computational efficiency, scalability, and rapid reaction, are also highlighted.

Micro-Doppler based detection and tracking of UAVs with multistatic radar

This paper presents an approach for detection and tracking a micro-UAV using the multistatic radar NetRAD. Experimental trials were performed using NetRAD allowing for analysis of real data to assess the difficulty of detection and tracking of a micro-UAV target. The UAV detection is based on both time domain and micro-Doppler signatures, in order to enhance the discrimination between ground clutter and UAV returns. This micro-Doppler based procedure is shown to improve the clutter/target discrimination, in comparison to a Doppler-shift based procedure. The tracking approach is able to compensate for the limited quality measurement generated by each bistatic pair by fusing the measurements available from multiple bistatic pairs.

An exact solution to track-to-track fusion using accumulated state densities

Originally the Accumulated State Density (ASD) has been proposed to provide an exact solution to the out-of-sequence measurement problem. To this end, the posterior of the joint density of all states accumulated over time was derived for a single sensor scenario. An exact solution for T2TF has been published as the Distributed Kalman Filter (DKF). However, the DKF is exact only if global knowledge in terms of the measurement models for all sensors are available at a local processor. This paper demonstrates that an exact solution for T2TF can also be achieved as a convex combination of local ASDs generated at each node in a distributed sensor system. This method crucially differs from the DKF, in that an exact solution is achieved without each processing platform being required to have knowledge of the global information. Therefore, this theoretical development has significant potential for achieving exact T2TF in practical problems. The resulting algorithm is called the Distributed Accumulated State Density (DASD) filter.

Use of the Kullback-Leibler divergence in estimating clutter distributions

This paper investigates the application of the Kullback-Leibler divergence (KLD) to quantify the loss in detection performance associated with misestimating clutter distributions. The problem of detecting targets in long-tailed clutter is presented. The significance of the KLD as an information theoretic measure to enhance the accuracy of threshold setting is discussed. The behaviour of the KLD is shown by comparing K-distributions with different shape parameters, since this distribution is known to be a good fit to clutter, particularly to sea clutter. The KLD is compared with the difference in Probability of False Alarm, threshold and other ad hoc measures of the accuracy of the fit. The KLD is then used to analyse the quality of the fit to the K-distribution of the statistics in different Doppler bins of a coherent sea clutter spectrum. The paper concludes that the KLD is better than the ad-hoc measures for determining the cost of misestimating the distributions, although some ad-hoc measures can give results that are close to it.

A resource allocation model for the radar search function

A performance model for the radar search function is derived, which can be used for resources management in a multifunction radar that uses an electronically steered array antenna. The proposed resource allocation model incorporates scenario information, such as the platform altitude and velocity, as well as the areas where targets are expected to pop-up. The model is demonstrated by simulation in airborne and ground based radar scenarios, where a significant improvement in performance is shown in comparison to a non-optimised, uniform resource allocation. This improvement equates to an enlargement of the surveillance region.

Towards an online, adaptive algorithm for radar surveillance control

Multifunction radars are highly configurable and possess some form of beam agility, allowing maintenance of a large number of tasks supporting varied functions. However, the surveillance function is commonly executed using a fixed periodic pattern, not utilising the full hardware potential. In this paper, a new method of surveillance control is proposed which utilises a particle filter to estimate a probability density of the undetected target location. Subsequently, the finite resource available for surveillance is allocated between sectors, based on information extracted from this probability density, using the Continuous Double Auction Parameter Selection algorithm. This method is successfully demonstrated through simulation on a surveillance control problem.

Information theoretic measures for MFR tracking control

Multi-function radar resource management addresses how to unlock the potential of electronically steered arrays by allocating and configuring radar resource, for a variety of tasks, in a way which maximises performance. To improve the quality of the allocation novel approaches are called upon, which inevitably require an accurate measure of effectiveness. Information theory shows potential for filling this role by providing a measure independent of task type. As such this paper investigates the use of information theory to control the dedicated track function using a narrow beam. Allocations using the information theoretic measures are compared with a standard approach, in terms of update rate, information rate, root mean squared error and track loss.

On how the distributed Kalman filter is related to the federated Kalman filter

In this paper, a direct connection between the covariance debiasing methodology for the distributed Kalman (DKF) filter in [1] and the federated Kalman filter is shown. In particular, it can be seen that for a unique choice of the information gain hypothesis of the DKF, the covariance debiasing becomes equivalent to the federated Kalman filter. As the complexity of the covariance calculation for the federated Kalman filter is rather low, a hybrid solution is proposed. A numerical evaluation presents two different scenarios where the state estimate of the distributed Kalman filter outperforms the federated Kalman filter in terms of accuracy. The first scenario is using linear Gaussian noise on position measurements whereas in the second scenario a distributed radar application is shown.

Distributed radar tracking using the double debiased distributed Kalman filter

The distributed Kalman filter requires the measurement covariances of remote radar nodes to be known at all radar nodes. This is not possible for a radar network, as the true measurement covariances depend on the radar-target geometry and the fluctuating signal-to-noise ratio. This paper tackles this problem using the double debiased distributed Kalman filter (D3KF) which utilizes a radar model to form a hypothesis on the global covariance. The scheme also transmits debiasing matrices, that account for the mismatch between the assumed and encountered measurement covariance. The scheme is evaluated in a radar network scenario, where it is demonstrated to achieve close to the optimal performance of a centralized Kalman filter (CKF). In contrast to a CKF, the D3KF does not transmit the complete measurement data and is not dependent on the transmission rate of the communication channels to the fusion center.