Biruk K. Habtemariam

A Multiple-Detection Joint Probabilistic Data Association Filter

Most conventional target tracking algorithms assume that a target can generate at most one measurement per scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target in a scan can arise due to multipath propagation effects as in the over-the-horizon radar (OTHR). A conventional multitarget tracking algorithm will fail in these scenarios, since it cannot handle multiple target-originated measurements per scan. The Joint Probabilistic Data Association Filter (JPDAF) uses multiple measurements from a single target per scan through a weighted measurement-to-track association. However, its fundamental assumption is still one-to-one. In order to rectify this shortcoming, this paper proposes a new algorithm, called the Multiple-Detection Joint Probabilistic Data Association Filter (MD-JPDAF) for multitarget tracking, which is capable of handling multiple detections from targets per scan in the presence of clutter and missed detection. The multiple-detection pattern, which can account for many-to-one measurement set-to-track association rather than one-to-one measurement-to-track association, is used to generate multiple detection association events. The proposed algorithm exploits all the available information from measurements by combinatorial association of events that are formed to handle the possibility of multiple measurements per scan originating from a target. The MD-JPDAF is applied to a multitarget tracking scenario with an OTHR, where multiple detections occur due to different propagation paths as a result of scattering from different ionospheric layers. Experimental results show that multiple-detection pattern based probabilistic data association improves the state estimation accuracy. Furthermore, the tracking performance of the proposed filter is compared against the Posterior Cramér-Rao Lower Bound (PCRLB), which is explicitly derived for the multiple-detection scenario with a single target.

PHD filter based track-before-detect for MIMO radars

In this paper a Probability Hypothesis Density (PHD) filter based track-before-detect (TBD) algorithm is proposed for Multiple-Input-Multiple-Output (MIMO) radars. The PHD filter, which propagates only the first-order statistical moment of the full target posterior, is a computationally efficient solution to multitarget tracking problems with varying number of targets. The proposed algorithm avoids any assumption on the maximum number of targets as a result of estimating the number of targets together with target states. With widely separated transmitter and receiver pairs, the algorithm utilizes the Radar Cross Section (RCS) diversity as a result of target illumination from ideally uncorrelated aspects. Furthermore, a multiple sensor TBD is proposed in order to process the received signals from different transmitter-receiver pairs in the MIMO radar system. In this model, the target observability to the sensor as a result of target RCS diversity is taken in to consideration in the likelihood calculation. In order to quantify the performance of the proposed algorithm, the Posterior Cramer-Rao Lower Bound (PCRLB) for widely separated MIMO radars is also derived. Simulation results show that the new algorithm meets the PCRLB and provides better results compared with standard Maximum Likelihood (ML) based localizations.

Multitarget track before detect with MIMO radars

Recent advances in Multiple-Input-Multiple-Output (MIMO) radar systems show that they have the potential to improve detection and localization performance of targets over bistatic and multistatic radars. Unlike beam forming, which presumes a high correlation between signals either transmitted or received by an array, the MIMO system exploits the independence between signals at the array elements due to transmit diversity. Previous works focus on waveform design, signal processing and target localization with MIMO radars while no attention has been given to tracking algorithms. In this work, the problem of tracking multiple targets using MIMO radars is considered. The scenario includes multiple targets in a widely-separated MIMO architecture in which Radar-Cross-Section (RCS) diversity can be utilized. Multi target version of Track-Before-Detect (TBD) algorithm is implemented for the collected M × N orthogonal signals at the receiver, where M is the number of transmitters and N is the number of receivers. Besides having the advantage of integrating information over time on unthresholded measurements to yield detection and tracking simultaneously, the TBD technique enables tracking and detecting targets in low Signal-to-Noise-Ratio (SNR) environments. Also, a modified multiple sensor TBD, which weights the target observability to the sensor as a result of target RCS diversity in the likelihood calculation to best fit the centralized MIMO tracking is proposed. Finally, Monte Carlo simulations are performed to evaluate the performance of the proposed tracking algorithm.

Multitarget Tracking with Doppler Ambiguity

A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms.

Measurement level AIS/radar fusion

In maritime surveillance, messages from radar and the Automatic Identification System (AIS) receivers are used for vessel trafficking and monitoring. The common trend is to use radars as the primary source of surveillance and AIS as a secondary source with little interaction between these data sets. The AIS messages provide very accurate position estimates associated with ID and other vessel information. However, AIS messages arrive unpredictably and intermittently depending on the type and behavior of the vessel. In addition, the revisit interval of AIS messages could be very large and it may vary from one vessel to another.In this work, a new measurement-level fusion algorithm to combine radar and AIS messages is proposed using the Joint Probabilistic Data Association (JPDA) framework. The proposed method handles AIS ID swaps between vessels and missing IDs while effectively fusing the radar measurements with AIS messages at measurement level. The uncertainty in the AIS ID-to-track assignment is resolved by assigning multiple AIS IDs to a target and updating the ID probabilities using a Bayesian inference with radar measurements, AIS messages and other targets. The performance of the proposed measurement-level fusion is compared with that of the track-to-track fusion. A modified Posterior Cramer-Rao Lower Bound (PCRLB) is also derived for the variable-rate heterogenous AIS/Radar network. Experimental results based on simulated data demonstrate the performance of the proposed technique. HighlightsProposed new measurement-level fusion algorithm for AIS/radar network.Performance of the proposed algorithm is compared with radar-only and AIS-only track results.Modified Posterior Cramer-Rao Lower Bound (PCRLB) is derived for the variable-rate heterogeneous AIS/Radar network.

Performance comparison of a multiple-detection probabilistic data association filter with PCRLB

Most target tracking algorithms assume that at most one measurement is generated by a target in a scan. However, there are tracking problems where this assumption is not valid. For example, multiple detections from a target can arise due to multipath propagation where different signals scattered from a target arrive at the sensor via different paths. With multiple target-originated measurements, most multitarget trackers will fail or become ineffective due to the violation of the one-to-one assumption. For example, the joint probabilistic data association (JPDA) filter is capable of using multiple measurements for a single target through weighted measurement-to-track association, but its fundamental assumption is still one-to-one. In order to rectify this shortcoming, we developed a new algorithm in our previous work, the multiple-detection probabilistic data association (MD-PDA) filter, which is capable of handling multiple detections from a target in a scan, in the presence of false alarm and probability of detection less than unity. In this paper, the performance of this MD-PDA filter is compared with the posterior Cramér-Rao lower bound (PCRLB), which is explicitly derived for the multiple-detection scenario. Furthermore, experimental results show multiple-detection pattern based probabilistic data association improves the state estimation accuracy and reduces the total number of false tracks.

Continuous 2-D assignment for multitarget tracking with rotating radars

Mechanically steered scanning radars receive measurements continuously while sweeping the surveillance region. However, most target tracking algorithms, like the multiple hypothesis tracker (MHT) and the joint probabilistic data association (JPDA) techniques, wait for the end of a scan in order to process the measurements and to estimate targets states. This is due to the fundamental assumption of one-to-one association between tracks and measurements and the 360° physical limit of a scan. Associating measurements to initialized tracks and filtering at the end of a complete scan may cause significant delays in target state update. In addition, association may become imperfect due to longer intervals between updates. This issue becomes significant when tracking high-speed targets with low scan rate sensors as in the airborne early warning (AEW) system. In this paper, we present a new dynamic sector processing algorithm using two-dimensional (2-D) assignment for scanning radars that report measurements within the duration of a scan. The full scan is dynamically and adaptively divided into sectors, which could be as small as a single detection, depending on the scanning rate, sparsity of targets, and required target state update speed. Measurement-to-track association, filtering, and target state update are done dynamically while sweeping from one region to another, i.e., continuous track update, limited only by the inter-measurement interval, becomes possible. The proposed algorithm offers low latency while maintaining estimation accuracy in track updates as well as efficient utilization of computational resources compared with standard frame-based tracking algorithms. Experimental results based on rotating radars demonstrate the advantages of the proposed technique.

Dynamic sector processing using 2D assignment for rotating radars

Electronically scanned array radars as well as mechanically steered rotating antennas return measurements with different time stamps during the same scan while sweeping form one region to another. Data association algorithms process the measurements at the end of the scan in order to satisfy the common one measurement per track assumption. Data processing at the end of a full scan resulted in delayed target state update. This issue becomes more apparent while tracking fast moving targets with low scan rate sensors. In this paper, we present new dynamic sector processing algorithm using 2D assignment for continuously scanning radars. A complete scan can be divided into sectors, which could be as small as a single detection, depending on the scanning rate and sparsity of targets. Data association followed by filtering and target state update is done dynamically while sweeping from one end to another. Along with the benefit of immediate track updates, continuous tracking results in challenges such as multiple targets spanning multiple sectors and targets crossing consecutive sectors. Also, associations performed in the current sector may require changes in association done in previous sectors. Such difficulties are resolved by the proposed 2D assignment algorithm that implements an incremental Hungarian assignment technique. The algorithm offers flexibility with respect to assignment variables for fusing of measurements received in consecutive sectors. Furthermore the proposed technique can be extended to multiframe assignment for jointly processing data from multiple scanning radars. Experimental results based on rotating radars are presented.

Measurement level AIS/radar fusion for maritime surveillance

Using the Automatic Identification System (AIS) ships identify themselves intermittently by broadcasting their location information. However, traditionally radars are used as the primary source of surveillance and AIS is considered as a supplement with a little interaction between these data sets. The data from AIS is much more accurate than radar data with practically no false alarms. But unlike the radar data, the AIS measurements arrive unpredictably, depending on the type and behavior of a ship. The AIS data includes target IDs that can be associated to initialized tracks. In multitarget maritime surveillance environment, for some targets the revisit interval form the AIS could be very large. In addition, the revisit intervals for various targets can be different. In this paper, we proposed a joint probabilistic data association based tracking algorithm that addresses the aforementioned issues to fuse the radar measurements with AIS data. Multiple AIS IDs are assigned to a track, with probabilities updated by both AIS and radar measurements to resolve the ambiguity in the AIS ID source. Experimental results based on simulated data demonstrate the performance the proposed technique.

Improved multiframe association for tracking maneuvering targets

Data association is the crucial part of any multitarget tracking algorithm in a scenario with multiple closely spaced targets, low probability of detection and high false alarm rate. Multiframe assignment, which solves the data association problem as a constrained optimization, is one of the widely accepted methods to handle the measurement origin uncertainty. If the targets do not maneuver, then multiframe assignment with one or two frames will be enough to find the correct data association. However, more frames must be considered in the data association for maneuvering targets. Also, a target maneuver might be hard to detect when maneuvering index, which is the function of sampling time, is small. In this paper, we propose an improved multiframe data association with better cost calculation using backward multiple model recursion, which increases the maneuvering index. The effectiveness of the proposed algorithm is demonstrated with simulated data.