Feng Lian

Unified cardinalized probability hypothesis density filters for extended targets and unresolved targets

The unified cardinalized probability hypothesis density (CPHD) filters for extended targets and unresolved targets are proposed. The theoretically rigorous measurement-update equations for the proposed filters are derived according to the theory of random finite set (RFS) and finite-set statistics (FISST). By assuming that the predicted distributions of the extended targets and unresolved targets and the distribution of the clutter are Poisson, the exact extended-target and unresolved-target CPHD correctors reduce to the exact extended-target and unresolved-target PHD correctors, respectively. Since the exact CPHD and PHD corrector equations involve with a number of operations that grow exponentially with the number of measurements, the computationally tractable approximations for them are presented, which can be used when the extended targets and the unresolved targets are not too close together and the clutter density is not too large. Monte Carlo simulation results show that the approximate extended-target and unresolved-target CPHD filters, respectively, outperform the approximate extended-target and unresolved-target PHD filters a lot in estimating the target number and states, although the computational requirement of the CPHD filters is more expensive than that of the PHD filters.

Distributed compressed sensing based joint detection and tracking for multistatic radar system

This paper present a novel distributed compressed sensing based joint detection and tracking approach for multi-static radar system, which reduces the computational load largely, in a centralized fusion framework.In this paper, we consider reconstructing the sparse vector representing the target state space directly.A novel DGSSMP algorithm is proposed to reconstruct the sparse grid reflection vector in distributed compressed sensing, under a general condition when each individual sensing matrix is different and with high coherence.The outputs of the DGSSMP algorithm (the states of all potential targets), are directly fed as instantaneous measurements to the TBD tracker, which avoids the use of a nonlinear measurement model in classical TBD algorithm. In this paper, we present a novel distributed compressed sensing based joint detection and tracking algorithm for multistatic radar system, which significantly reduces the computational load in a centralized fusion framework. For different receivers in the multistatic radar system, their corresponding sparse vectors, which are represented in state space, share the same locations of nonzero reflection coefficients. This fits the joint sparsity model 2 (JSM-2) in distributed compressed sensing. In this paper, a novel algorithm, named distributed general similar sensing matrix pursuit (DGSSMP) algorithm, is proposed to tackle the generalized JSM-2 model when each individual sensing matrix is different and with high coherence. In contrast to the classical greedy algorithms dealing with single subspace, the proposed DGSSMP algorithm has to tackle a union of different subspaces, with each subspace corresponding to a different sensing matrix for each individual receiver. The simulation results show that in the proposed distributed compressed sensing based joint detection and tracking framework, the proposed DGSSMP algorithm together with the track before detect (TBD) scheme can effectively distinguish true targets from clutter based on the information from multiple scans.

Multitarget State Extraction for the PHD Filter using MCMC Approach

It is known that multitarget states cannot be directly derived from the particle probability hypothesis density (particle-PHD) filter. Therefore, some cluster algorithms are used to extract the states from the particles. Actually, these algorithms become a crucial step in how to cluster the particles effectively and robustly in the particle-PHD filter. A novel multitarget state extraction algorithm for the particle-PHD filter is proposed. The proposed algorithm is comprised of two steps. First, the target number is calculated via the particle-PHD filter. Second, the distribution of the particles is fitted using finite mixture models (FMMs), whose parameters can be derived using a Markov chain Monte Carlo (MCMC) sampling scheme. Then the states can be extracted according to the fitted mixture distribution. The final simulations show that the proposed algorithm is effective for the extraction of the individual states even when the clutter is dense and the distribution of the particles is relatively complex.

Estimating Unknown Clutter Intensity for PHD Filter

In most of the existing probability hypothesis density (PHD) filters, the clutter is modeled as a Poisson random finite set (RFS) with a known intensity. The clutter intensity is characterized as a product of the average number of clutter (false alarm) points per scan and the probability density of clutter spatial distribution. The PHD filter is generalized to the problem of multi-target tracking (MTT) in clutter with an unknown intensity. In the proposed approach, the unknown clutter intensity is first estimated for the PHD filter. Estimation of the clutter intensity involves the estimation of the average clutter number per scan and the estimation of the clutter density. The clutter density is estimated as finite mixture models (FMM) via either expectation maximum (EM) or Markov chain Monte Carlo (MCMC) algorithm. Then, the estimated intensity is used directly in the PHD filter to perform multi-target detecting and tracking. Monte Carlo (MC) simulation results show that the proposed approach outperforms the naive PHD filter of assuming uniform clutter distribution significantly especially when the nominal clutter model is obviously different from the ground truth.

Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters

The convergence for the sequential Monte Carlo (SMC) implementations of the multitarget multi-Bernoulli (MeMBer) filter and cardinality-balanced MeMBer (CBMeMBer) filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.

Models and Algorithms for Tracking Target with Coordinated Turn Motion

Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

COMPRESSED SENSING BASED TRACK BEFORE DETECT ALGORITHM FOR AIRBORNE RADARS

This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)- Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans. In the proposed CS-TBD algorithm, the improved StOMP algorithm together with the temporal tracking, can efiectively distinguish true targets from false targets and clutter based on information from multiple illuminations.

General similar sensing matrix pursuit

In this paper, a novel algorithm, called the general similar sensing matrix pursuit (GSSMP), is proposed to deal with the deterministic sensing matrix with high coherence. First, the columns of the sensing matrix are divided into a number of similar column groups based on the similarity distance. Each similar column group presents a set of coherent columns or a single incoherent column, which provides a unified frame work to construct the similar sensing matrix. The similar sensing matrix is with low coherence provided that the minimum similar distance between any two condensed columns is large. It is proved that under appropriate conditions the GSSMP algorithm can identify the correct subspace quite well, and reconstruct the original K-sparse signal perfectly. Moreover, we have enhanced the proposed GSSMP algorithm to cope with the unknown sparsity level problem, by testing each individual contributing similar column group one by one to find the true vectors spanning the correct subspace. The simulation results show that the modified GSSMP algorithm with the contributing similar column group test process can estimate the sparse vector representing the radar scene with an unknown number of targets successfully. HighlightsA novel GSSMP algorithm is proposed to tackle the deterministic sensing matrix with high coherence.A rigorous proof of the guaranteed reconstruction performance of the GSSMP algorithm is also provided.The proposed GSSMP algorithm is further enhanced to cope with the unknown sparsity problem.

Multiple-Model Cardinality Balanced Multitarget Multi-Bernoulli Filter for Tracking Maneuvering Targets

By integrating the cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter with the interacting multiple models (IMM) algorithm, an MM-CBMeMBer filter is proposed in this paper for tracking multiple maneuvering targets in clutter. The sequential Monte Carlo (SMC) method is used to implement the filter for generic multi-target models and the Gaussian mixture (GM) method is used to implement the filter for linear-Gaussian multi-target models. Then, the extended Kalman (EK) and unscented Kalman filtering approximations for the GM-MM-CBMeMBer filter to accommodate mildly nonlinear models are described briefly. Simulation results are presented to show the effectiveness of the proposed filter.