Model-based trajectory reconstruction with IMM smoothing and segmentation

Abstract

This paper presents a new approach for off-line trajectory reconstruction in air traffic control domain. The proposed algorithm, called model-based reconstruction, performs an accurate IMM smoothing process whose parameters are modified along time according to the flight modes segmented from trajectory measurements. Its competitive performance is demonstrated through comparison with previous reconstruction methods used in ATC and with classical IMM smoothing, using simulated data.

Review and classification of trajectory summarisation algorithms: From compression to segmentation

“…PoI and PoIE, 61 TS, 127,128 2 stage-pls, 126 IMM, [35][36][37] OGPC and OSPC, 47 FFDP and FFUS, 97 S-DMin and SE-DMin, 129 ATS, 117 SMoT, 71 CB-SMoT, 21 Patroumpas, 73,74 STC, 22 RGRASP-SemTS, 24 BTC and HTC, 25 STMaker, 103,104 SELF, 121 SetraStream, 82 HESAVE and SNDSC, 122 SPD 84 then, using these, other algorithm is applied to select the measurements.…”

“…Coresets, 34 AACAT, 30 SimpleTrack, 31 SGTCR-CS 27 Probabilistic IMM, [35][36][37] APSOS, 38 SAS, 32 SAOTS, 33 SGTCR-CS 27 Graph Distance Bellman, 39,40 DOTS, 41 DOTS-CASCADE, 41 Iri-Imai, 42,43 MRPA, 44 Daescu, 45,46 OGPC and OSPC, 47 MMTC-offline, 48 MMTC-online, 48 SPPA, 49 GRTSOpt, 50 Latecki, 51 Trajic, 52 Representativeness, 53 KAA and StreamKAA, 54 OLTS and OPTTS, 55 DOTS*, 56 OSC and OSTC, 28 CLEAN 57 Angle VTracer, 58 DPTS + , 59 Latecki, 51 61 GRPPA, 62 TSHL, 63 AMS, 16 CFF, 64 BOPW and NOPW, 4 OHTA, OnlineOHTA and SATA, 65 CDR, CDRm, GRTSOpt and GRTSSec, 50 TraClus, 66 OPERB and A-OPERB, 67 BQS, 68 ABQS, FBQS and PBQS, 69 LO-OPW-TR, 70 OPW-TR, 3 SMoT, 71 Pan, 72 Patroumpas,…”

Smoothing for continuous dynamical state space models with sampled system coefficients based on sparse kernel learning

“…Out of the three kinds of smoothing, namely the fixedinterval, the fixed-lag and the fixed-point, only the first kind, which may be the most important one in the authors' experience, is studied in this work. Smoothing finds wide applications in mission evaluation [1], system/signal reconstruction [2], final result production [3], etc. As long as the state vector is estimated not necessarily in real time, smoothing should be preferred to filtering, as the accuracy of the former is generally higher than the latter except for the only instant at the end of the whole working time span.…”

“…The above RTS is chosen as a benchmark to check the performance of the proposed method. It is again stressed that the RTS smoother is statistically optimal but only for the discrete model ( 3) and (2). However, this model itself is not rigorous but approximate to the original model ( 1) and (2).…”

“…It is again stressed that the RTS smoother is statistically optimal but only for the discrete model ( 3) and (2). However, this model itself is not rigorous but approximate to the original model ( 1) and (2). Also note that with RTS, estimates of the state vector are only available at sampling instants of the observation y k .…”