Darryl Morrell

Dynamic Configuration of Time-Varying Waveforms for Agile Sensing and Tracking in Clutter

The advent of waveform-agile sensors has enabled the design of tracking systems where the transmitted waveform is changed on-the-fly in response to the trackers requirements. This approach can provide performance improvements over individual optimization of the sensor waveform or the tracking algorithm. In this paper, we consider joint sensor configuration and tracking for the problem of tracking a single target in the presence of clutter using range and range-rate measurements obtained by waveform-agile, active sensors in a narrowband environment. We propose an algorithm to select and configure linear and nonlinear frequency-modulated waveforms to minimize the predicted mean square error (MSE) in the target state estimate; the MSE is predicted using the Cramer-Rao lower bound on the measurement error in conjunction with the unscented transform. We further extend our algorithm to match wideband environments, and we demonstrate the algorithm performance through a Monte Carlo simulation of a radar tracking example.

Adaptive Waveform Design for Improved Detection of Low-RCS Targets in Heavy Sea Clutter

The dynamic adaptation of waveforms for transmission by active radar has been facilitated by the availability of waveform-agile sensors. In this paper, we propose a method to employ waveform agility to improve the detection of low radar-cross section (RCS) targets on the ocean surface that present low signal-to-clutter ratios due to high sea states and low grazing angles. Employing the expectation-maximization algorithm to estimate the time-varying parameters for compound-Gaussian sea clutter, we develop a generalized likelihood ratio test (GLRT) detector and identify a range bin of interest. The clutter estimates are then used to dynamically design a phase-modulated waveform that minimizes the out-of-bin clutter contributions to this range bin. A simulation based on parameters derived from real sea clutter data demonstrates that our approach provides around 10 dB improvement in detection performance over a nonadaptive system

Convex Bayes decision theory

The basic concepts of Levis epistemic utility theory and credal convexity are presented. Epistemic utility, in addition to penalizing error as is done with traditional Bayesian decision methodology, permits a unit of informational value to be distributed among the hypotheses of a decision problem. Convex Bayes decision theory retains the conditioning structure of probability-based inference, but addresses many of the objections to Bayesian inference through relaxation of the requirement for numerically definite probabilities. The result is a decision methodology that stresses avoiding errors and seeks decisions that are likely to be highly informative as well as true. By relaxing the mandatory requirement for unique decisions and point estimates in all cases, decision and estimation criteria that do not demand more than is possible to obtain from the data and permit a natural man-in-the-loop interface are obtained. Applications are provided to illustrate the theory. >

Waveform-agile sensing for tracking

Waveform-agile sensing is fast becoming an important technique for improving sensor performance in applications such as radar, sonar, biomedicine, and communications. The paper provided an overview of research work on waveform-agile target tracking. From both control theoretic and information theoretic perspectives, waveforms can be selected to optimize a tracking performance criterion such as minimizing the tracking MSE or maximizing target information retrieval. The waveforms can be designed directly based on their estimation resolution properties, selected from a class of waveforms with varying parameter values over a feasible sampling grid in the time-frequency plane, or obtained from different waveform libraries.

Energy efficient target tracking in a sensor network using non-myopic sensor scheduling

We propose to use non-myopic sensor scheduling to minimize the energy usage in a sensor network while maintaining a desired squared-error tracking accuracy of a targets position estimate. The network comprises of Type A sensors that collect measurements, and Type B sensors that collect, process, and schedule measurements. The target is tracked using a particle filter; only Type B sensors hold the target belief and update it with measurements. Network energy consumption is primarily due to sensing and communicating belief and measurements between sensors. To schedule a sequence of M sensing actions, the Type B sensor holding the target belief computes the minimum energy sequence that satisfies the tracking accuracy constraint M steps in the future. Scheduling is implemented efficiently by precomputing an energy tree and using a uniform-cost search. The tracking accuracy for sensor scheduling is approximated by the posterior Cramer-Rao lower bound. Using Monte Carlo simulations, we demonstrate that non-myopic scheduling results in significantly lower energy usage than myopic scheduling while meeting the accuracy constraint.

Set-values filtering and smoothing

A theory of discrete-time optimal filtering and smoothing based on convex sets of probability distributions is presented. Rather than propagating a single conditional distribution as does conventional Bayesian estimation, a convex set of conditional distributions is evolved. For linear Gaussian systems, the convex set can be generated by a set of Gaussian distributions with equal covariance with means in a convex region of state space. The conventional point-valued Kalman filter is generated to a set-valued Kalman filter consisting of equations of evolution of a convex set of conditional means and a conditional covariance. The resulting estimator is an exact solution to the problem of running an infinity of Kalman filters and fixed-interval smoothers, each with different initial conditions. An application is presented to illustrate and interpret the estimator results. >

Multipath exploitationwith adaptivewaveform design for tracking in urban terrain

We integrate multipath exploitation with adaptive waveform design in order to increase the tracking performance of a vehicle moving in urban terrain. Mitigation of both clutter and strong multipath returns can result in increased target detection. However, exploiting multiple bounces from obstacles such as buildings can be shown to increase radar coverage and scene visibility, especially in the absence of direct line-of-sight paths. For this purpose, we formulate the multipath propagation of an arbitrary number of specular bounces in urban terrain for three-dimensional motion. We then further exploit and optimize multipath returns by dynamically selecting the parameters of the transmitted waveform to minimize the predicted mean-squared tracking error. We demonstrate our proposed approach in a realistic urban environment by varying the type of measurement to include regions of obscuration and different number of multipath bounces.

Sequential Monte Carlo Methods for Tracking Multiple Targets With Deterministic and Stochastic Constraints

In multitarget scenarios, kinematic constraints from the interaction of targets with their environment or other targets can restrict target motion. Such motion constraint information could improve tracking performance if effectively used by the tracker. In this paper, we propose three particle filtering methods that incorporate constraint information in their proposal and weighting process; the number of targets is fixed and known in all methods. The reproposed constrained motion proposal (RCOMP) utilizes an accept/reject method to propose particles that meet the constraints. The truncated constraint motion proposal (TCOMP) uses proposal densities truncated to satisfy the constraints. The constraint likelihood independent partitions (CLIP) method simply rejects proposed partitions that do not meet the constraints. We use simulation to evaluate the performance of these three methods for two constrained motion scenarios: a vehicle convoy and soldiers executing a leapfrog motion. Moreover, we demonstrate the utility of constraint information by comparing the proposed algorithms with the independent partition (IP) proposal method that does not use constraint information. The simulation results demonstrate that the root mean square error (RMSE) tracking performance of the RCOMP and the TCOMP methods is much better than the CLIP and IP methods; this is due to their more efficient proposal process.

Scheduling multiple sensors using particle filters in target tracking

A critical component of a multi-sensor system is sensor scheduling to optimize system performance under constraints (e.g. power, bandwidth, and computation). In this paper, we apply particle filter sequential Monte Carlo methods to implement multiple sensor scheduling for target tracking. Under the constraint that only one sensor can be used at each time step, we select a sequence of sensor uses to minimize the predicted mean-square error in the target state estimate; the predicted mean-square error is approximated using the particle filter in conjunction with an extended Kaiman filter approximation. Using Monte Carlo simulations, we demonstrate the improved performance of our scheduling approach over the non-scheduling case.

Nonmyopic sensor scheduling and its efficient implementation for target tracking applications

We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearing-only sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.