Passive Radar Tracking Research Articles

Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars.

Approaches to Cartesian Data Association Passive Radar Tracking in a DAB/DVB Network

We present a target tracking system for passive radar suitable for a digital audio/video broadcast (DAB/DVB) network with orthogonal frequency division multiplexing (OFDM) illuminators of opportunity. In this system bistatic range and range-rate are available as a measurement while angular information is assumed unavailable or of very poor quality. There is also a novel association ambiguity between the signals and illuminators, this in addition to the usual one between the signals and targets, and hence an interesting data association problem. Our intention is to provide tracks directly in the geographic space (3-D Cartesian domain). Here, four filters are suggested. The first two are the extended Kalman filter (EKF) and unscented Kalman filter (UKF) based on a joint probabilistic data association (JPDA) modified to incorporate the additional assignment complexity. The other two filters are a bootstrap particle filter (BPF) and an auxiliary particle filter (APF) based on the data association under the probabilistic multi-hypothesis tracker (PMHT) measurement model, which means that each measurement's assignment is independent of all others. Simulation results show that these four filters can work properly, albeit naturally with different performance characteristics and complexity.

Multireceiver passive radar tracking

In this article, we have proved that using several passive radar receivers is of high interest to improve tracking capacities, by increasing the number of bistatic geometries available and diversifying the target aspect angles (multistatism). The experimental results showed that the multireceiver configuration enables us to initialize a track when the number of simultaneous detections is too low to do it in a single-receiver configuration (DVB-T) and/or it improves tracking quality, like track continuity, accuracy, and reduction of the initialization time (FM).

Dynamic Multipath Model of Low Angle Passive Radar Tracking with Experimental Evaluation

The problem of multipath propagation encountered in radar tracking of low elevation targets in the presence of reflections from the sea is addressed. A detailed model of the multipath propagation considering both the specular and the diffuse reflection components in target tracking using passive radars is established. Based on the geometry of the specular and the diffuse reflections, expressions for the reflection coefficient and the scattering field are derived. Experiments in the outfield indicate that the model proposed agrees with the test data well, which can provide an accurate prediction of the angle measurement errors in the presence of multipath effects. Defence Science Journal, 2012, 62(5), pp.331-337 , DOI:http://dx.doi.org/10.14429/dsj.62.1000

Multiple-Target Tracking and Identity Management withApplication to Aircraft Tracking

The problem of tracking and managing the identities of multiple targets is discussed and applied to the passive radar tracking of aircraft. The targets are assumed to be commercial aircraft switching modes during flight, and are thus well modeled by hybrid systems. We propose a computationally efficient algorithm based on joint probabilistic data association for target-measurement correlation. We use the results of this algorithm to simultaneously implement an identity management algorithm based on identity-mass flow, and a multiple-target tracking algorithm based on the residual-mean interacting multiple model algorithm. Together, they constitute the multiple-target tracking and identity management algorithm. The multiple-target tracking and identity management algorithm incorporates suitable local information about target identity, when available, in a manner that decreases the uncertainty in the system as measured by its statistical entropy. For situations in which local information is not explicitly available, a technique based on multiple hypothesis testing is proposed to infer such information. This algorithm allows us to track multiple targets, each capable of multiple modes of operation, in the presence of continuous process noise and of spurious measurements. The multiple-target tracking and identity management algorithm is demonstrated through various scenarios that are motivated by air traffic surveillance applications.